Corn canopy water content sensing system and method based on unmanned aerial vehicle sensor network

Abstract

This invention relates to the field of data analysis technology, specifically a corn canopy moisture sensing system and method based on a drone sensor network. The method includes: obtaining a specific reflectance image of the corn canopy; calculating an adaptive moisture index and outputting a corn canopy moisture content heatmap; calculating the barrier effect coefficient and corn transpiration source term, marking water vapor channel blockage hotspots on a three-dimensional cornfield structural model, delineating the locations of outer barrier zones and internal retention zones, estimating corn growth potential, and distinguishing between high-potential and risk zones; performing thinning priority scoring, formulating a dynamic thinning strategy based on the thinning priority score, and outputting the thinning coordinates of the cornfield. This invention solves the problem in the prior art where the simplified water vapor flux model ignores the three-dimensional barrier effect, leading to inaccurate analysis of water retention zones.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, specifically a corn canopy moisture sensing system and method based on unmanned aerial vehicle (UAV) sensor networks. Background Technology

[0002] While UAV hyperspectral and LiDAR technologies are gradually being applied in farmland monitoring, the following technical problems exist in the field of maize canopy moisture analysis: existing spectral inversion models are mostly designed based on general vegetation parameters and have not been optimized for the reflectance characteristics of the waxy layer of maize canopy leaves, high leaf area index, and three-dimensional spatial heterogeneity. This results in insufficient accuracy of reflectance correction in water-sensitive bands, especially in densely planted areas where leaf overlap and multiple scattering interference can cause moisture content inversion errors, leading to biased irrigation decisions. In addition, existing water vapor flux models mostly use two-dimensional simplification assumptions and have not quantified the "barrier effect" formed by high-density plants on the periphery of maize fields, resulting in inaccurate prediction of internal water vapor retention hotspots. Furthermore, the lack of a dynamic coupling mechanism between stomatal conductance and transpiration source terms makes it difficult to reflect the true response characteristics of maize growth to water. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to address the problem of inaccurate analysis of water retention zone caused by the simplification of water vapor flux models in the prior art that ignores the three-dimensional barrier effect. The present invention proposes a maize canopy water sensing system and method based on UAV sensor network.

[0004] To achieve the above objectives, the technical solution of the corn canopy moisture sensing method based on UAV sensor networks of the present invention includes the following steps:

[0005] S1: Collect spectral data of maize canopy and leaf tilt angle, process the data using the maize canopy reflectance correction model, and obtain a specific reflectance image of the maize canopy;

[0006] S2: Dynamically adjust the weights of the moisture-sensitive bands, calculate the adaptive moisture index, output a heat map of maize canopy moisture content, and mark the outer high transpiration zone and the inner retention zone.

[0007] S3: Construct a three-dimensional structural model of the cornfield, calculate the barrier effect coefficient and the corn transpiration source term, mark the water vapor channel blocking hotspots on the three-dimensional structural model of the cornfield, and delineate the location of the outer barrier zone and the location of the inner retention zone.

[0008] S4: Establish a coupled model of light interception and water use efficiency, calculate the light-water coupled growth index, predict maize growth potential, and distinguish between high-potential areas and risk areas;

[0009] S5: Based on S3-S4, score the priority of thinning, formulate a dynamic thinning strategy based on the score, and output the coordinates of the thinning points in the cornfield.

[0010] Specifically, S1 includes:

[0011] S11: Using a clustering algorithm, the boundaries are automatically divided based on the height and density gradient of the maize plants to generate a vector partitioning layer. The appropriate partitioning map includes: an outer barrier area and an inner core area.

[0012] It should be noted that the key parameters of the clustering algorithm include: a neighbor range of 1.5 meters and a number of corn plants greater than or equal to 5 in the effective cluster;

[0013] S12: Dynamically adjusts the flight parameters of the UAV in the outer barrier zone and the inner core zone, and outputs the UAV's regional flight path planning map;

[0014] S13: Preset the strong reflection band of corn wax layer and the moisture-sensitive band, and dynamically adjust the sensor integration time according to the real-time light intensity;

[0015] S14: Use a corn leaf biomimetic plate to correct the collected spectral data;

[0016] S15: Simultaneously acquire LiDAR point cloud data, calculate the single-leaf tilt angle through normal vector estimation, and statistically analyze the average tilt angle θ of maize leaves within the region. avg By introducing the wax layer reflection enhancement coefficient α of maize leaves to correct the reflectivity of the target canopy, a specific reflectivity image of the maize canopy is obtained.

[0017] Preferably, the correction of the target canopy reflectivity specifically involves:

[0018] R corn (λ)=R cal (λ)×[1+α×cos(θ avg )];

[0019] Among them, R corn (λ) represents the target canopy reflectivity after correction by the wax layer reflectance enhancement coefficient; R cal (λ) represents the target canopy reflectivity after correction using a corn leaf biomimetic plate;

[0020] Specifically, S2 includes the following steps:

[0021] S21: Extract meteorological parameters from the UAV mini weather station and preprocess the meteorological parameters, wherein the meteorological parameters include: temperature data, humidity data and wind speed data; the preprocessing of the meteorological parameters includes: removing meteorological data when the pitch angle of the UAV flight attitude is greater than ten degrees;

[0022] S22: Using the spatiotemporal kriging algorithm, with fixed time intervals and spatial grids, dynamic data from UAVs and data from fixed weather stations along the cornfield ridges are fused to construct an hourly temperature field for the cornfield, calculate the effective accumulated temperature at each location in the cornfield, and output an effective accumulated temperature heat map.

[0023] Preferably, the calculation strategy for the effective accumulated temperature is as follows:

[0024]

[0025] Where GDD(x,y) is the effective accumulated temperature at the coordinate (x,y) position in the cornfield;

[0026] T day (x,y),T night (x, y) represent the temperature data at noon and midnight at the location (x, y) in the cornfield, respectively; T base This is the baseline growth temperature for the maize canopy;

[0027] S23: Dynamically adjust the weight of moisture-sensitive bands based on the GDD progress coefficient;

[0028] It should be noted that the GDD progress coefficient represents the proportion of current GDD to the target reproductive period GDD;

[0029] Preferably, the dynamic adjustment of the weights of the moisture-sensitive bands includes:

[0030]

[0031] Among them, w i (GDD) represents the adjusted weights for the moisture-sensitive bands.

[0032] These are the weight values ​​for the stage following the target reproductive period and the weight values ​​for the stage preceding the target reproductive period, respectively.

[0033] GDD is the cumulative GDD value at the current calculation time;

[0034] GDD next GDD prev The critical value for GDD at the beginning of the next reproductive period and the critical value for GDD at the end of the previous reproductive period;

[0035] S24: Based on S23, calculate the adaptive moisture index, compare the adaptive moisture index at each location in the cornfield with the set moisture threshold, mark the area below 80% of the moisture threshold as the internal retention zone, and output the corn canopy moisture content heat map for each growth stage.

[0036] Preferably, the adaptive moisture index calculation strategy is as follows:

[0037]

[0038] Where i is the spectral band index, and n represents the total number of bands involved in the calculation;

[0039] R corn (λ i (x,y) represents the reflectance in the specific reflectance image of the maize canopy obtained by S1;

[0040] w i (GDD(x,y)) represents the weight of the water-sensitive band at coordinate (x,y) in the cornfield;

[0041] Specifically, S3 includes the following steps:

[0042] S31: Adjust the scanning mode of the UAV to collaboratively acquire multimodal LiDAR and turbulence data, and optimize the LiDAR point cloud density according to the scanning tilt angle and flight altitude.

[0043] S32: Extract the highest point of each maize plant from the LiDAR point cloud and calculate the average height of the outer barrier area and the inner core area;

[0044] The number of corn plants per unit area is calculated based on LiDAR point cloud data, thereby obtaining the plant density in the outer barrier area and the inner core area.

[0045] S33: Based on S32, calculate and obtain the wind permeability coefficient and barrier coefficient of the corn canopy;

[0046] The specific strategy for calculating the wind permeability coefficient TF of the maize canopy is as follows:

[0047]

[0048] Where M is the total number of leaves in the corn plant, θ m Let be the tilt angle of the m-th blade;

[0049] The specific strategy for calculating the barrier coefficient PZ of the maize canopy is as follows:

[0050]

[0051] Among them, H out ,ρ out ,TF out These represent the average height, average density, and air permeability coefficient of maize plants in the outer barrier area, respectively.

[0052] H in ,ρ in ,TF inThese represent the average height, average density, and ventilation coefficient of the corn plants in the core area.

[0053] Specifically, S3 also includes:

[0054] S34: Extract the adaptive moisture index and canopy temperature calculated in S2, construct a dynamic inversion model of maize canopy stomatal conductance, and output the maize canopy stomatal conductance.

[0055]

[0056] Where k1 and k2 are the half-saturation constants and temperature response coefficients, respectively; g s,max This represents the maximum conductivity of the pores.

[0057] S35: Obtain the transpiration source term S of maize based on the stomatal conductance of the maize canopy. corn ;

[0058] Preferably, the corn transpiration source item S corn The calculation strategy is as follows:

[0059] S corn =g s ×VPD×LAI;

[0060] Where VPD stands for vapor pressure deficit; LAI stands for leaf area index.

[0061] S36: Construct a three-dimensional water vapor diffusion equation for cornfields, and solve the difference equation or finite element equation using the iterative method according to the set boundary conditions to obtain the water vapor concentration distribution at each time step;

[0062] The three-dimensional water vapor diffusion equation for the cornfield is as follows:

[0063] Where C is the water vapor concentration, D is the diffusion coefficient, and ν is the wind speed vector;

[0064] S37: Calculate the water vapor channel blockage index BI. The water vapor channel blockage index (BI) is compared with a preset blockage threshold. When the water vapor channel blockage index BI is greater than the blockage threshold, the current location is marked as a blockage hotspot. Where C... act C exp These represent the calculated water vapor concentration and the expected water vapor concentration, respectively.

[0065] Specifically, S4 includes:

[0066] S41: Extracting the vertical distribution of maize leaf area based on LiDAR point clouds and simulating the shading path using a ray tracing algorithm, including: calculating the maize canopy shading rate, wherein the calculation strategy for the maize canopy shading rate is as follows:

[0067]

[0068] Where Sha(x,y,t) is the shading rate of the maize canopy;

[0069] Q ray This represents the total number of rays used in the ray tracing simulation.

[0070] g is the extinction coefficient of the maize canopy;

[0071] LAI(x,y,z) is the leaf area index at position (x,y) and height z in the three-dimensional space of a cornfield; Δz is the height interval of the vertical stratification.

[0072] S42: Based on S41, calculate and obtain the actual photosynthetic radiation above the corn canopy. The calculation strategy for the actual photosynthetic radiation is as follows:

[0073] PAR act (x,y,t)=PAR above (t)×[1-Sha(x,y,t)];

[0074] Among them, PAR act (x,y,t) represents the actual photosynthetic radiation above the maize canopy; PAR above (t) represents the photosynthetically active radiation above the maize canopy;

[0075] S43: Based on the adaptive moisture index obtained in S2, calculate the water use efficiency (WUE) of the maize canopy.

[0076] Preferably, the calculation strategy for the water use efficiency of the maize canopy is as follows:

[0077]

[0078] Among them, A net (x,y,t) Net photosynthetic rate.

[0079] Specifically, S4 also includes:

[0080] S44: Based on S41-S43, calculate and obtain the maize growth potential index (GPI). The specific calculation strategy for the maize growth potential index is as follows:

[0081]

[0082] Among them, PAR max W0 and W0 represent the maximum photosynthetic radiation and the nominal water use efficiency, respectively.

[0083] S45: Based on the maize growth potential index, the growth potential of maize plants in the maize field is classified, including:

[0084] When GPI ≥ 0.8, the current region is judged to be a high-potential region;

[0085] When GPI < 0.5, the current area is judged as a risk area;

[0086] When 0.5 ≤ GPI < 0.8, the current region is determined to be a transition region.

[0087] Specifically, S5 includes the following steps:

[0088] S51: Based on the outer barrier area, the inner core area, the maize growth potential index, and the barrier coefficient, a thinning priority score is calculated. The thinning priority score is specifically as follows:

[0089]

[0090] Among them, P remove Score the priority of thinning;

[0091] S52: Based on the priority score for thinning, formulate a dynamic thinning strategy. The dynamic thinning strategy includes: using the DBSCAN clustering algorithm to identify priority thinning areas, statistically generating thinning coordinate points for each cluster center, and simultaneously extracting the turbulence intensity in S3, and adjusting the spacing between thinning points according to the turbulence intensity.

[0092] Preferably, the spacing d between the thinning points is... spa The calculation strategy is as follows:

[0093] d spa =d base ×(1+0.5×TI);

[0094] Where, d base TI represents the baseline spacing between corn plants in a cornfield; TI represents the turbulence intensity.

[0095] In addition, the corn canopy moisture sensing system based on UAV sensor networks of the present invention includes the following modules:

[0096] The module includes a reflectance calculation module, a moisture heatmap generation module, a water vapor retention assessment module, a growth potential prediction module, and a thinning strategy output module.

[0097] The reflectance calculation module is used to collect spectral data of the corn canopy and leaf tilt angle, and to process the data using the corn canopy reflectance correction model to obtain a specific reflectance image of the corn canopy.

[0098] The moisture heat map generation module is used to dynamically adjust the weight of moisture-sensitive bands, calculate the adaptive moisture index, output a heat map of maize canopy moisture content, and mark the outer high transpiration zone and the inner retention zone.

[0099] The water vapor retention assessment module is used to construct a three-dimensional structural model of the cornfield, calculate the barrier effect coefficient and corn transpiration source term, mark the water vapor channel blocking hotspots on the three-dimensional structural model of the cornfield, and delineate the location of the outer barrier zone and the location of the inner retention zone.

[0100] The growth potential prediction module is used to establish a coupled model of light interception and water use efficiency, calculate the light-water coupled growth index, predict the growth potential of maize, and distinguish between high-potential areas and risk areas.

[0101] The thinning strategy output module is used to score the thinning priority, formulate a dynamic thinning strategy based on the thinning priority score, and output the coordinate points of the thinning in the cornfield.

[0102] Compared with the prior art, the technical effects of the present invention are as follows:

[0103] 1. This invention addresses the reflectivity characteristics of the waxy layer of the maize canopy and high-density planting structure. By optimizing dynamic bands and using an adaptive moisture index based on phenological stages, it reduces the error in the reflectivity correction of moisture-sensitive bands. Furthermore, by dynamically adjusting model parameters based on the effective accumulated temperature of maize, it accurately captures the spatial heterogeneity between the outer high transpiration zone and the inner stagnant zone, effectively solving the "one-size-fits-all" error problem caused by traditional spectral models that ignore leaf tilt angle and differences in growth stages.

[0104] 2. This invention utilizes LiDAR tilt scanning and turbulence data to construct a three-dimensional barrier coefficient of "height-density-ventilation", quantifying the blocking effect of the outer corn canopy on airflow and moisture diffusion. Combined with a stomatal conductance dynamic inversion model, it realizes the physical driving simulation of the three-dimensional distribution of water vapor flux, improving the prediction accuracy of internal stagnant hotspots and providing a scientific basis for accurately identifying high-risk areas.

[0105] 3. Based on the spatial clustering of barrier coefficient and growth potential, this invention sets up a "wind-resistant thinning" algorithm based on turbulence and a thinning-irrigation linkage mechanism. Through priority scoring, the thinning density and irrigation amount are dynamically adjusted to ensure water vapor flux after the outer barrier is regulated, while reducing lodging. Attached Figure Description

[0106] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0107] in:

[0108] Figure 1This is a schematic flowchart of the corn canopy moisture sensing method based on UAV sensor network of the present invention;

[0109] Figure 2 This is a schematic diagram of the structure of the corn canopy moisture sensing system based on a drone sensor network according to the present invention;

[0110] Figure 3 This is an example diagram illustrating a scenario where a cornfield generates a barrier effect according to the present invention. Detailed Implementation

[0111] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0112] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0113] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0114] Example 1:

[0115] like Figure 1 As shown in the figure, the corn canopy moisture sensing method based on UAV sensor network of this invention is as follows: Figure 1 As shown, the specific steps include the following:

[0116] S1: Collect spectral data of maize canopy and leaf tilt angle, process the data using the maize canopy reflectance correction model, and obtain a specific reflectance image of the maize canopy;

[0117] S1 includes:

[0118] S11: The DBSCAN clustering algorithm is used to automatically divide the boundaries based on the height and density gradient of maize plants and generate a vector partitioning layer. The appropriate partitioning map includes: an outer barrier area and an inner core area.

[0119] It should be noted that the key parameters of the clustering algorithm include: a neighbor range of 1.5 meters and a number of corn plants greater than or equal to 5 in the effective cluster;

[0120] For example, in this embodiment, a method for generating a vector partition layer is provided, including: preprocessing LiDAR point cloud data (point density ≥ 100 points / m²). 2 Extract the three-dimensional coordinates of plant height, density, and maize canopy; based on historical maize planting data (sowing date, variety distribution), mark the outer perimeter with a density greater than or equal to 8 plants / m². 2 Plants with a height variation coefficient greater than or equal to 15% are designated as the outer barrier zone, and the marked density is less than or equal to 6 plants / m². 2 Furthermore, plants with a height variation coefficient of less than or equal to 10% are considered to be in the inner core area;

[0121] S12: Dynamically adjusts the flight parameters of the UAV in the outer barrier zone and the inner core zone, and outputs the UAV's regional flight path planning map;

[0122] For example, in this embodiment, the flight parameters of the outer barrier zone include: a flight altitude of 2-3 meters and a spiral progressive flight strategy;

[0123] The spiral radius of the spiral progressive route is 5 meters and the layer spacing is 12 meters.

[0124] It should be noted that after the flight altitude is reduced in the outer barrier zone, the spatial resolution of the spectral sensor is improved, which can capture subtle differences in the reflection of the waxy layer of individual leaves.

[0125] For example, in this embodiment, the flight parameters of the internal core area include: a flight altitude of 5-6 meters and a grid-density flight path;

[0126] The grid-encrypted flight path uses a 40m×40m grid with a flight path spacing of 6m, and a 1-second hovering is set at the grid intersection.

[0127] It should be noted that increasing the flight altitude can expand the coverage area and reduce spectral mixing errors caused by blade overlap;

[0128] S13: Preset the strong reflection bands (1450nm, 1650nm) and moisture-sensitive bands (1940nm, 2200nm) of the corn wax layer, and dynamically adjust the sensor integration time according to the real-time light intensity (feedback from the light intensity sensor) (such as shortening the integration time under strong light to avoid overexposure).

[0129] For example, in this embodiment, the dynamic adjustment of the sensor integration time includes:

[0130] Real-time light intensity data is converted into irradiance; when the irradiance is greater than 800 W / m² 2 At nm, the integration time decreased from 50 ms to 20 ms; when the irradiance is less than 200 W / m 2At nm, the integration time is extended to 100ms to avoid A / D converter saturation and overexposure;

[0131] S14: Use a corn leaf biomimetic plate to correct the collected spectral data;

[0132] For example, in this embodiment, a specific strategy for correcting collected spectral data using a corn leaf biomimetic plate is provided, including:

[0133]

[0134] Among them, R cal (λ) represents the target canopy reflectivity after correction using a corn leaf biomimetic plate;

[0135] L sen The sensor measures the original radiance value of the corn canopy.

[0136] L dark This is the sensor's output signal under completely dark conditions;

[0137] L fsb (λ) represents the radiance value of the biomimetic corn leaf plate measured by the sensor;

[0138] For example, in this embodiment, it should be noted that the corn leaf biomimetic board should be placed horizontally to avoid the sun's incident angle from being greater than 60°, and the drone should measure the white board's reflectance value in the field before each flight.

[0139] S15: Simultaneously acquire LiDAR point cloud data, calculate the single-leaf tilt angle through normal vector estimation, and statistically analyze the average tilt angle θ of maize leaves within the region. avg By introducing the wax layer reflection enhancement coefficient α of maize leaves to correct the reflectivity of the target canopy, a specific reflectivity image of the maize canopy is obtained.

[0140] In one specific implementation, the correction of the target canopy reflectivity is as follows:

[0141] R corn (λ)=R cal (λ)×[1+α×cos(θ avg )];

[0142] Among them, R corn (λ) represents the target canopy reflectivity after correction by the wax layer reflectance enhancement coefficient; R cal (λ) represents the target canopy reflectivity after correction using a corn leaf biomimetic plate;

[0143] For example, it should be noted that the wax layer reflection enhancement coefficient of the corn leaves decreases with increasing leaf age, which was obtained by multiple sets of experimental measurements. In this embodiment, the wax layer reflection enhancement coefficient of the corn leaves is 0.18.

[0144] S2: Dynamically adjust the weights of the moisture-sensitive bands, calculate the adaptive moisture index, output a heat map of maize canopy moisture content, and mark the outer high transpiration zone and the inner retention zone.

[0145] S2 includes the following specific steps:

[0146] S21: Extract meteorological parameters from the UAV mini weather station and preprocess the meteorological parameters, wherein the meteorological parameters include: temperature data, humidity data and wind speed data; the preprocessing of the meteorological parameters includes: removing meteorological data when the pitch angle of the UAV flight attitude is greater than ten degrees;

[0147] S22: Using the spatiotemporal kriging algorithm, with fixed time intervals and spatial grids, dynamic data from UAVs and data from fixed weather stations along the cornfield ridges are fused to construct an hourly temperature field for the cornfield, calculate the effective accumulated temperature at each location in the cornfield, and output an effective accumulated temperature heat map.

[0148] In one specific implementation, the calculation strategy for the effective accumulated temperature is as follows:

[0149]

[0150] Where GDD(x,y) is the effective accumulated temperature at the coordinate (x,y) position in the cornfield;

[0151] T day (x,y),T night (x, y) represent the temperature data at noon and midnight at the location (x, y) in the cornfield, respectively; T base This is the baseline growth temperature for the maize canopy;

[0152] For example, it should be noted that the basic growth temperature of the corn canopy is determined by the corn variety. In this embodiment, the basic growth temperature of the corn canopy is 10°C.

[0153] For example, in this embodiment, the time interval is 10 minutes and the spatial grid is 5m;

[0154] It should be noted that the cumulative heat required for corn growth is calculated by combining data from drones and fixed weather stations along the cornfield ridges. This is to calculate the effective accumulated temperature at each location in the cornfield, taking into account diurnal temperature and canopy cooling effects, which can solve the problem of low accuracy in the original analysis data of the corn canopy.

[0155] S23: Dynamically adjust the weight of moisture-sensitive bands based on the GDD progress coefficient;

[0156] It should be noted that the GDD progress coefficient represents the proportion of current GDD to the target reproductive period GDD;

[0157] Preferably, the dynamic adjustment of the weights of the moisture-sensitive bands includes:

[0158]

[0159] Among them, w i (GDD) represents the adjusted weights for the moisture-sensitive bands.

[0160] These are the weight values ​​for the stage following the target reproductive period and the weight values ​​for the stage preceding the target reproductive period, respectively.

[0161] GDD is the cumulative GDD value at the current calculation time;

[0162] GDD next GDD prev The critical value for GDD at the beginning of the next reproductive period and the critical value for GDD at the end of the previous reproductive period;

[0163] S24: Based on S23, calculate the adaptive moisture index, compare the adaptive moisture index at each location in the cornfield with the set moisture threshold, mark the area below 80% of the moisture threshold as the internal retention zone, and output the corn canopy moisture content heat map for each growth stage.

[0164] Preferably, the adaptive moisture index calculation strategy is as follows:

[0165]

[0166] Where i is the spectral band index, and n represents the total number of bands involved in the calculation;

[0167] R corn (λ i (x,y) represents the reflectance in the specific reflectance image of the maize canopy obtained by S1;

[0168] w i (GDD(x,y)) represents the weight of the water-sensitive band at coordinate (x,y) in the cornfield;

[0169] S3: Construct a three-dimensional structural model of the cornfield, calculate the barrier effect coefficient and the corn transpiration source term, mark the water vapor channel blocking hotspots on the three-dimensional structural model of the cornfield, and delineate the location of the outer barrier zone and the location of the inner retention zone.

[0170] S3 includes the following specific steps:

[0171] S31: Adjust the scanning mode of the UAV to collaboratively acquire multimodal LiDAR and turbulence data, and optimize the LiDAR point cloud density according to the scanning tilt angle and flight altitude.

[0172] For example, in this embodiment, adjusting the drone scanning mode includes:

[0173] In the outer barrier area, the UAV is adjusted to fly at a 30° tilt angle along the outer barrier area, and the LiDAR emission angle is dynamically adjusted to penetrate the high-density corn canopy and capture the three-dimensional arrangement characteristics of the corn plants.

[0174] In the inner core region, the canopy vertical structure parameters, i.e., the vertical distribution of leaf area, were obtained by stratifying the canopy at flight heights of 2m, 4m, and 6m.

[0175] For example, in this embodiment, the acquisition of turbulence data includes: using an ultrasonic anemometer mounted on a drone to measure the wind speed and turbulence intensity above the canopy in real time;

[0176] For example, in this embodiment, the optimization of the LiDAR point cloud density includes:

[0177]

[0178] Where, ρ point The optimized LiDAR point cloud density;

[0179] N las f is the number of lasers; pul H represents the pulse frequency; H represents the UAV's flight altitude.

[0180] φ is the scanning tilt angle; Δt is the scanning time;

[0181] It should be noted that the number of laser pulses determines the number of lasers emitted by LiDAR; the more lasers emitted, the more point cloud data can be acquired.

[0182] It should be noted that the scanning tilt angle affects the angle between the laser and the target object. When φ is small, the laser illuminates the target object perpendicularly, resulting in more effective point cloud data. When φ is large, the laser illuminates the target object at an angle, and some laser light may be blocked, leading to a reduction in the number of point clouds. Therefore, cosφ is used to correct the effect of the scanning tilt angle on the point cloud density.

[0183] It should be noted that the flight altitude is inversely proportional to the point cloud density. The higher the flight altitude, the larger the spot of the laser beam on the ground and the lower the point cloud density; the lower the flight altitude, the smaller the spot of the laser beam on the ground and the higher the point cloud density.

[0184] It should be noted that optimizing the density of LiDAR point clouds can adapt to the actual conditions of different cornfields, thereby improving the density and accuracy of the point clouds.

[0185] S32: Extract the highest point of each maize plant from the LiDAR point cloud and calculate the average height of the outer barrier area and the inner core area;

[0186] The number of corn plants per unit area is calculated based on LiDAR point cloud data, thereby obtaining the plant density in the outer barrier area and the inner core area.

[0187] S33: As Figure 3 As shown, according to S32, the wind permeability coefficient and barrier coefficient of the maize canopy are calculated and obtained;

[0188] The specific strategy for calculating the wind permeability coefficient TF of the maize canopy is as follows:

[0189]

[0190] Where M is the total number of leaves in the corn plant, θ m Let be the tilt angle of the m-th blade;

[0191] For example, in this embodiment, it should be noted that the canopy permeability coefficient reflects the canopy's ability to allow airflow and is closely related to the blade tilt angle. When the blade tilt angle is large, the airflow can penetrate the canopy more easily, and the canopy permeability coefficient is large; when the blade tilt angle is small, the airflow is more obstructed, and the canopy permeability coefficient is small.

[0192] The specific strategy for calculating the barrier coefficient PZ of the maize canopy is as follows:

[0193]

[0194] Among them, H out ,ρ out ,TF out These represent the average height, average density, and air permeability coefficient of maize plants in the outer barrier area, respectively.

[0195] H in ,ρ in ,TF in These represent the average height, average density, and ventilation coefficient of the corn plants in the core area.

[0196] For example, in this embodiment, it should be noted that the barrier coefficient of the maize canopy reflects the resistance ratio of the maize canopy in the outer barrier region and the inner core region;

[0197] S34: Extract the adaptive moisture index and canopy temperature calculated in S2, construct a dynamic inversion model of maize canopy stomatal conductance, and output the maize canopy stomatal conductance.

[0198]

[0199] Where k1 and k2 are the half-saturation constants and temperature response coefficients, respectively; g s,max This represents the maximum conductivity of the pores.

[0200] S35: Obtain the transpiration source term S of maize based on the stomatal conductance of the maize canopy. corn ;

[0201] Preferably, the corn transpiration source item S corn The calculation strategy is as follows:

[0202] S corn =g s ×VPD×LAI;

[0203] Where VPD stands for vapor pressure deficit; LAI stands for leaf area index.

[0204] S36: Construct a three-dimensional water vapor diffusion equation for cornfields, and solve the difference equation or finite element equation using the iterative method according to the set boundary conditions to obtain the water vapor concentration distribution at each time step;

[0205] The three-dimensional water vapor diffusion equation for the cornfield is as follows:

[0206] Where C is the water vapor concentration, D is the diffusion coefficient, and v is the wind speed vector;

[0207] For example, in this embodiment, the boundary conditions include: water vapor flux in the outer barrier region and maize transpiration source term;

[0208] For example, in this embodiment, the calculation strategy for the water vapor flux of the outer barrier region is as follows:

[0209] S37: Calculate the water vapor channel blockage index BI. The water vapor channel blockage index (BI) is compared with a preset blockage threshold. When the water vapor channel blockage index BI is greater than the blockage threshold, the current location is marked as a blockage hotspot. Where C... act C exp These represent the calculated water vapor concentration and the expected water vapor concentration, respectively.

[0210] S4: Establish a coupled model of light interception and water use efficiency, calculate the light-water coupled growth index, predict maize growth potential, and distinguish between high-potential areas and risk areas;

[0211] S4 includes:

[0212] S41: Extract the vertical distribution of maize leaf area based on LiDAR point cloud, and simulate the shading path using a ray tracing algorithm, including: calculating the maize canopy shading rate, wherein the calculation strategy for the maize canopy shading rate is as follows:

[0213]

[0214] Where Sha(x,y,t) is the shading rate of the maize canopy;

[0215] Q ray This represents the total number of rays used in the ray tracing simulation.

[0216] g is the extinction coefficient of the corn canopy, which describes the attenuation ability of light as it passes through the corn.

[0217] LAI(x,y,z) is the leaf area index at position (x,y) and height z in the three-dimensional space of a cornfield; Δz is the height interval of the vertical stratification.

[0218] For example, in this embodiment, it should be noted that the method of obtaining LAI(x,y,z) includes: using the vertical layered scanning data of LiDAR, combining the point cloud density to invert the LAI of each layer, and constructing the vertical distribution curve of LAI(z).

[0219] S42: Based on S41, calculate and obtain the actual photosynthetic radiation above the corn canopy. The calculation strategy for the actual photosynthetic radiation is as follows:

[0220] PAR act (x,y,t)=PAR above (t)×[1-Sha(x,y,t)];

[0221] Among them, PAR act (x,y,t) represents the actual photosynthetic radiation above the maize canopy; PAR above (t) represents the photosynthetically active radiation above the maize canopy;

[0222] S43: Based on the adaptive moisture index obtained in S2, calculate the water use efficiency (WUE) of the maize canopy.

[0223] Preferably, the calculation strategy for the water use efficiency of the maize canopy is as follows:

[0224]

[0225] Among them, A net (x,y,t) Net photosynthetic rate.

[0226] S44: Based on S41-S43, calculate and obtain the maize growth potential index (GPI). The specific calculation strategy for the maize growth potential index is as follows:

[0227]

[0228] Among them, PAR max W0 and W0 represent the maximum photosynthetic radiation and the nominal water use efficiency, respectively.

[0229] S45: Based on the maize growth potential index, the growth potential of maize plants in the maize field is classified, including:

[0230] When GPI ≥ 0.8, the current region is judged to be a high-potential region;

[0231] When GPI < 0.5, the current area is judged as a risk area;

[0232] When 0.5 ≤ GPI < 0.8, the current region is determined to be a transition region.

[0233] S5: Based on S3-S4, score the priority of thinning, formulate a dynamic thinning strategy based on the score, and output the coordinates of the thinning points in the cornfield.

[0234] S5 includes the following specific steps:

[0235] S51: Based on the outer barrier area, the inner core area, the maize growth potential index, and the barrier coefficient, a thinning priority score is calculated. The thinning priority score is specifically as follows:

[0236]

[0237] Among them, P remove Score the priority of thinning;

[0238] S52: Based on the priority score for thinning, formulate a dynamic thinning strategy. The dynamic thinning strategy includes: using the DBSCAN clustering algorithm to identify priority thinning areas, statistically generating thinning coordinate points for each cluster center, and simultaneously extracting the turbulence intensity in S3, and adjusting the spacing between thinning points according to the turbulence intensity.

[0239] Preferably, the spacing d between the thinning points spa The calculation strategy is as follows:

[0240] d spa =d base ×(1+0.5×TI);

[0241] Where, d base TI represents the baseline spacing between corn plants in a cornfield; TI represents the turbulence intensity.

[0242] Example 2:

[0243] like Figure 2 As shown in the figure, the corn canopy moisture sensing system based on a drone sensor network according to an embodiment of the present invention is as follows: Figure 2 As shown, it includes the following modules:

[0244] The module includes a reflectance calculation module, a moisture heatmap generation module, a water vapor retention assessment module, a growth potential prediction module, and a thinning strategy output module.

[0245] The reflectance calculation module is used to collect spectral data of the corn canopy and leaf tilt angle, and to process the data using the corn canopy reflectance correction model to obtain a specific reflectance image of the corn canopy.

[0246] The moisture heat map generation module is used to dynamically adjust the weight of the moisture-sensitive band, calculate the adaptive moisture index, output a heat map of the moisture content of the maize canopy, and mark the outer high transpiration zone and the inner retention zone.

[0247] The water vapor retention assessment module is used to construct a three-dimensional structural model of the cornfield, calculate the barrier effect coefficient and corn transpiration source term, mark the water vapor channel blocking hotspots on the three-dimensional structural model of the cornfield, and delineate the location of the outer barrier zone and the location of the inner retention zone.

[0248] The growth potential prediction module is used to establish a coupled model of light interception and water use efficiency, calculate the light-water coupled growth index, predict the growth potential of maize, and distinguish between high-potential areas and risk areas.

[0249] The thinning strategy output module is used to score the thinning priority, formulate a dynamic thinning strategy based on the thinning priority score, and output the coordinate points of the thinning in the cornfield.

[0250] Example 3:

[0251] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0252] The processor executes the aforementioned corn canopy moisture sensing method based on a drone sensor network by calling a computer program stored in memory.

[0253] The electronic device can vary considerably depending on its configuration and performance. It may include one or more Central Processing Units (CPUs) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the corn canopy moisture sensing method based on a UAV sensor network provided in the above-described embodiment. The electronic device may also include other components for implementing its functions; for example, it may have wired or wireless network interfaces and input / output interfaces for data input and output. Details will not be elaborated upon in this embodiment.

[0254] Example 4:

[0255] This embodiment proposes a computer-readable storage medium on which an erasable and rewritable computer program is stored.

[0256] When the computer program runs on the computer device, it causes the computer device to execute the above-mentioned corn canopy moisture sensing method based on UAV sensor network.

[0257] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage devices.

[0258] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0259] It should be understood that determining B based on A does not mean determining B solely based on A; it also means determining B based on A and / or other information.

[0260] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network and / or wireless network. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0261] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0262] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0263] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only one method, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0264] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0265] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0266] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0267] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for sensing moisture in the maize canopy based on a drone sensor network, characterized in that, The method includes: S1: Collect spectral data of maize canopy and leaf tilt angle, process the data using the maize canopy reflectance correction model, and obtain a specific reflectance image of the maize canopy; S2: Dynamically adjust the weights of the moisture-sensitive bands, calculate the adaptive moisture index, output a heat map of maize canopy moisture content, and mark the outer high transpiration zone and the inner retention zone. S3: Construct a three-dimensional structural model of the cornfield, calculate the barrier effect coefficient and the corn transpiration source term, mark the water vapor channel blocking hotspots on the three-dimensional structural model of the cornfield, and delineate the location of the outer barrier zone and the location of the inner retention zone. S4: Establish a coupled model of light interception and water use efficiency, calculate the light-water coupled growth index, predict maize growth potential, and distinguish between high-potential areas and risk areas; S5: Based on S3-S4, score the priority of thinning, formulate a dynamic thinning strategy based on the score, and output the coordinates of the thinning points in the cornfield. S3 includes the following specific steps: S31: Adjust the scanning mode of the UAV to collaboratively acquire multimodal LiDAR and turbulence data, and optimize the LiDAR point cloud density according to the scanning tilt angle and flight altitude. S32: Extract the highest point of each maize plant from the LiDAR point cloud and calculate the average height of the outer barrier area and the inner core area; The number of corn plants per unit area is calculated based on LiDAR point cloud data, thereby obtaining the plant density in the outer barrier area and the inner core area. S33: Based on S32, calculate and obtain the wind permeability coefficient and barrier coefficient of the corn canopy; The specific strategy for calculating the wind permeability coefficient TF of the maize canopy is as follows: ； Where M is the total number of leaves on the corn plant. Let be the tilt angle of the m-th blade; The specific strategy for calculating the barrier coefficient PZ of the maize canopy is as follows: ； in, These represent the average height, average density, and air permeability coefficient of maize plants in the outer barrier area, respectively. These represent the average height, average density, and ventilation coefficient of the corn plants in the internal core area, respectively. S34: Extract the adaptive moisture index and canopy temperature calculated in S2, construct a dynamic inversion model of maize canopy stomatal conductance, and output the maize canopy stomatal conductance. ； in, These are the half-saturation constant and the temperature response coefficient; This represents the maximum conductivity of the pores. S35: Calculate the transpiration source term of maize based on the stomatal conductance of the maize canopy. ; S36: Construct a three-dimensional water vapor diffusion equation for cornfields, and solve the difference equation or finite element equation using the iterative method according to the set boundary conditions to obtain the water vapor concentration distribution at each time step; The three-dimensional water vapor diffusion equation for the cornfield is as follows: ; Where C is the water vapor concentration and D is the diffusion coefficient. This is the wind speed vector; S37: Calculate the water vapor channel blockage index BI. The water vapor channel blockage index (BI) is compared with a preset blockage threshold. When the water vapor channel blockage index BI is greater than the blockage threshold, the current location is marked as a blockage hotspot. These represent the calculated water vapor concentration and the expected water vapor concentration, respectively.

2. The method for sensing moisture in maize canopy based on a UAV sensor network according to claim 1, characterized in that, S4 includes: S41: Extracting the vertical distribution of maize leaf area based on LiDAR point clouds and simulating the shading path using a ray tracing algorithm, including: calculating the maize canopy shading rate, wherein the calculation strategy for the maize canopy shading rate is as follows: ； in, The shading rate of the corn canopy; This represents the total number of rays used in the ray tracing simulation. g is the extinction coefficient of the maize canopy; Let be the leaf area index at position (x, y) and height z in the three-dimensional space of a cornfield; The height interval for vertical layering; S42: Based on S41, calculate and obtain the actual photosynthetic radiation above the corn canopy. The calculation strategy for the actual photosynthetic radiation is as follows: ； in, This refers to the actual photosynthetic radiation above the corn canopy. Photosynthetically active radiation above the corn canopy; S43: Based on the adaptive moisture index obtained in S2, calculate the water use efficiency of the maize canopy. .

3. The method for sensing moisture in maize canopy based on a UAV sensor network according to claim 2, characterized in that, S4 also includes: S44: Based on S41-S43, calculate and obtain the maize growth potential index (GPI). The specific calculation strategy for the maize growth potential index is as follows: ； in, These are maximum photosynthetic radiation and nominal water use efficiency, respectively. S45: Based on the maize growth potential index, the growth potential of maize plants in the maize field is classified, including: when When the current area is determined to be a high-potential area; when When this happens, the current area is determined to be a risk area; when When the current region is determined to be a transition region, it is then determined that the current region is a transition region.

4. The method for sensing moisture in maize canopy based on a UAV sensor network according to claim 3, characterized in that, S5 includes the following specific steps: S51: Based on the outer barrier area, the inner core area, the maize growth potential index, and the barrier coefficient, a thinning priority score is calculated. The thinning priority score is specifically as follows: ； in, Score the priority of thinning; S52: Based on the priority score for thinning, formulate a dynamic thinning strategy. The dynamic thinning strategy includes: using the DBSCAN clustering algorithm to identify priority thinning areas, statistically generating thinning coordinate points for each cluster center, and simultaneously extracting the turbulence intensity in S3, and adjusting the spacing between thinning points according to the turbulence intensity.

5. The method for sensing moisture in maize canopy based on a UAV sensor network according to claim 1, characterized in that, S2 includes the following specific steps: S21: Extract meteorological parameters from the UAV mini weather station and preprocess the meteorological parameters, wherein the meteorological parameters include: temperature data, humidity data and wind speed data; the preprocessing of the meteorological parameters includes: removing meteorological data when the pitch angle of the UAV flight attitude is greater than ten degrees; S22: Using the spatiotemporal kriging algorithm, with fixed time intervals and spatial grids, dynamic data from UAVs and data from fixed weather stations along the cornfield ridges are fused to construct an hourly temperature field for the cornfield, calculate the effective accumulated temperature at each location in the cornfield, and output an effective accumulated temperature heat map. S23: Dynamically adjust the weight of moisture-sensitive bands based on the GDD progress coefficient; S24: Based on S23, calculate the adaptive moisture index, compare the adaptive moisture index of each location in the cornfield with the set moisture threshold, mark the area below 80% of the moisture threshold as the internal retention zone, and output the corn canopy moisture content heat map for each growth stage.

6. The method for sensing moisture in maize canopy based on a UAV sensor network according to claim 1, characterized in that, S1 includes: S11: Using a clustering algorithm, the boundaries are automatically divided based on the height and density gradient of the maize plants to generate a vector partitioning layer. The appropriate partitioning map includes: an outer barrier area and an inner core area. S12: Dynamically adjusts the flight parameters of the UAV in the outer barrier zone and the inner core zone, and outputs the UAV's regional flight path planning map; S13: Preset the strong reflection band of corn wax layer and the moisture-sensitive band, and dynamically adjust the sensor integration time according to the real-time light intensity; S14: Use a corn leaf biomimetic plate to correct the collected spectral data; S15: Synchronously acquire LiDAR point cloud data, calculate the single-leaf tilt angle through normal vector estimation, and statistically analyze the average tilt angle of maize leaves within the region. Introducing the reflectance enhancement coefficient of the waxy layer of maize leaves The reflectance of the target canopy is corrected to obtain a specific reflectance image of the maize canopy.

7. A corn canopy moisture sensing system based on a drone sensor network, used to implement the corn canopy moisture sensing method based on a drone sensor network as described in any one of claims 1-6, characterized in that, The system includes: The module includes a reflectance calculation module, a moisture heatmap generation module, a water vapor retention assessment module, a growth potential prediction module, and a thinning strategy output module. The reflectance calculation module is used to collect spectral data of the corn canopy and leaf tilt angle, and to process the data using the corn canopy reflectance correction model to obtain a specific reflectance image of the corn canopy. The moisture heat map generation module is used to dynamically adjust the weight of moisture-sensitive bands, calculate the adaptive moisture index, output a heat map of maize canopy moisture content, and mark the outer high transpiration zone and the inner retention zone. The water vapor retention assessment module is used to construct a three-dimensional structural model of the cornfield, calculate the barrier effect coefficient and corn transpiration source term, mark the water vapor channel blocking hotspots on the three-dimensional structural model of the cornfield, and delineate the location of the outer barrier zone and the location of the inner retention zone. The growth potential prediction module is used to establish a coupled model of light interception and water use efficiency, calculate the light-water coupled growth index, predict the growth potential of maize, and distinguish between high-potential areas and risk areas. The thinning strategy output module is used to score the thinning priority, formulate a dynamic thinning strategy based on the thinning priority score, and output the coordinate points of the thinning in the cornfield.

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