CN114863369B - Method, device, equipment and medium for monitoring corn lodging by laser radar - Google Patents
Method, device, equipment and medium for monitoring corn lodging by laser radar Download PDFInfo
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Abstract
The invention provides a method, a device, equipment and a medium for monitoring corn lodging by using a laser radar, which relate to the field of monitoring of corn lodging severity grade and comprise the following steps: constructing a canopy height model in a total monitoring area based on laser radar data; screening out the plant heights of all the un-lodging corns and all the lodging corns in the target monitoring area based on the canopy height model in the total monitoring area; and determining the corn lodging severity grade corresponding to the target monitoring area based on the plant heights of all the un-lodging corns and the plant heights of all the lodging corns in the target monitoring area. The invention takes the plant height of the unbowed corn in different target monitoring areas as a measurement standard, has accurate monitoring and strong objectivity, reflects the most obvious change of the corn after lodging on the plant height and the lodging angle, intuitively and quantitatively describes the severity of the corn under lodging stress, improves the accuracy of disaster diagnosis, has high response speed, is not limited by illumination conditions, and is suitable for the changeable meteorological environment of disaster areas.
Description
Technical Field
The invention relates to the field of corn lodging severity monitoring, in particular to a method, a device, equipment and a medium for monitoring corn lodging by using a laser radar.
Background
The existing monitoring method for the corn lodging disasters mainly comprises a manual investigation method, an unmanned aerial vehicle low-altitude remote sensing technology and a satellite remote sensing technology, but all have respective defects:
the manual survey method is simple and visual to operate, but the precision of the method depends on the working experience of surveyors, the standard is not uniform, the subjectivity is strong, the survey results of different people on the same plot are very different, and the error and the randomness are large. The investigation efficiency is low, the influence of weather conditions is serious, only a small area can be investigated in unit time, the representativeness of the investigation area cannot be guaranteed, and the corn plants can be damaged by sampling and other operations during investigation, so that the method is an inefficient and low-objectivity investigation mode;
the unmanned aerial vehicle low-altitude remote sensing technology is a corn lodging monitoring technology which is widely used at present, is low in price, flexible to use, simple to operate, and free of interference to normal growth of crops, can be used for carrying out multi-scale real-time short-distance ground monitoring on a disaster area, and acquiring digital images and multispectral images of the disaster area. However, the point cloud data acquired by digital aerial triangulation has relatively low precision, different lodging grades of corns cannot be distinguished, and the data acquisition is influenced by illumination conditions and cannot acquire lodging conditions of a disaster area at the first time after a disaster;
the satellite remote sensing technology is also a common corn lodging disaster monitoring method at present, has the advantages of large-area synchronous observation, rapidness and objectivity, but is limited by a revisit period of a satellite platform, and can not reach the upper part of a disaster area at the first time, and an optical satellite is obviously restricted by weather conditions, so that the disaster area is often subjected to cloud and rain, and effective information can not be obtained; the radar satellite is not limited by weather conditions, but has lower resolution, and cannot accurately classify the lodging severity grade of the corn, so that the satellite remote sensing technology is only suitable for the disaster area statistical work with lower requirement on timeliness, and cannot provide guidance for the accurate determination work of the disaster degree.
In conclusion, how to eliminate the manual investigation method, the unmanned aerial vehicle low-altitude remote sensing technology and the satellite remote sensing technology in the traditional sense and realize accurate calculation of plant heights of corns in different states through the laser radar technology becomes a technical problem to be solved urgently at present.
At present, when calculating crop lodging, especially when aiming at a crop such as corn, the situation of large-area lodging often occurs, however, due to the difference of growth cycle and growth stage, when the lodging degree is predicted, the unbowed corn in a single region is often adopted for large-scale estimation, and then the prediction result is not accurate enough, how to determine different unbowed corns in different regions as calculation parameters according to the difference of corn lodging area, the difference of corn growth stage and growth cycle, so that the calculation result is more in line with expectation, and the technical problem to be solved urgently at present is also formed.
At present, a technical scheme for efficiently and accurately monitoring the lodging severity level of the corn aiming at the corresponding areas of the corn in different growth stages does not exist.
Disclosure of Invention
The invention provides a method, a device, equipment and a medium for monitoring corn lodging by using a laser radar, which are used for solving the defect that the corn lodging cannot be accurately monitored in the prior art.
In a first aspect, the invention provides a method for monitoring corn lodging by using a laser radar, which comprises the following steps:
constructing a canopy height model in the total monitoring area based on the laser radar data;
screening out the plant heights of all the unbowed corns and the plant heights of all the lodging corns in the target monitoring area based on the canopy height model in the total monitoring area;
determining a corn lodging severity grade corresponding to the target monitoring area based on the plant heights of all un-lodging corns and the plant heights of all lodging corns in the target monitoring area;
the total monitoring area is composed of a plurality of target monitoring areas and a plurality of non-target monitoring areas;
the target monitoring area includes at least lodging corn.
According to the method for monitoring corn lodging by using the laser radar, provided by the invention, the canopy height model is constructed based on the laser radar data, and the method comprises the following steps:
processing the lidar data to construct a digital surface model and a digital elevation model;
determining the canopy height model based on the digital surface model and the digital elevation model.
According to the method for monitoring corn lodging by using the laser radar, provided by the invention, the plant heights of all un-lodging corns and all lodging corns in the target monitoring area are screened out based on the canopy height model in the total monitoring area, and the method comprises the following steps:
marking lodging corns in the canopy height model corresponding to the total monitoring area, and determining a target monitoring area where the lodging corns are located;
and extracting the plant height of each un-lodging corn and the plant height of each lodging corn from the canopy height model corresponding to the target monitoring area.
According to the method for monitoring corn lodging by using the laser radar, provided by the invention, the corn lodging severity grade corresponding to the target monitoring area is determined based on the plant heights of all un-lodging corns and all lodging corns in the target monitoring area, and the method comprises the following steps:
equalizing the plant heights of all the unhusked corns in the target monitoring areas to determine the average plant height of all the unhusked corns in each target monitoring area;
and traversing all the target monitoring areas, and determining the lodging severity grade of each lodging corn in each target monitoring area based on the plant height of each lodging corn and the average plant height.
According to the method for monitoring corn lodging by using the laser radar, provided by the invention, all target monitoring areas are traversed, and the lodging severity grade of each lodging corn in each target monitoring area is determined based on the plant height of each lodging corn and the average plant height, and the method comprises the following steps:
determining the lodging angle of each lodging corn in any target monitoring area based on the plant height of each lodging corn in any target monitoring area and the average plant height;
determining a lodging angle of each lodging corn and a corresponding interval of the plant height of the corn in a preset lodging level strategy;
determining a lodging level for each lodging corn based on the interval;
and traversing all the target monitoring areas until determining the lodging level of each lodging corn in all the target monitoring areas.
According to the method for monitoring corn lodging by using the laser radar, provided by the invention, the preset lodging level strategy is as follows:
determining the lodging grade of the lodging corn as root lodging under the condition that the lodging angle is at a first preset threshold value;
determining the lodging grade of the lodging corn as one grade of stalk fold under the condition that the lodging angle is in a second preset threshold value;
determining the lodging grade of the lodging corn as one stalk falling grade under the condition that the lodging angle is in a third preset threshold value;
determining the lodging grade of the lodging corn as a second stalk-fold grade under the condition that the lodging angle is at a fourth preset threshold value and the plant height of the corn is at a fifth preset threshold value;
determining the lodging grade of the lodging corn as a stem lodging grade under the conditions that the lodging angle is at a fourth preset threshold value and the plant height of the corn is at a sixth preset threshold value;
determining the lodging grade of the lodging corn as three stem-lodging grades under the condition that the lodging angle is at a seventh preset threshold value;
determining the lodging grade of the lodging corn as non-lodging under the condition that the lodging angle is in an eighth preset threshold value;
the first preset threshold is smaller than a second preset threshold, the second preset threshold is smaller than a third preset threshold, the third preset threshold is smaller than a fourth preset threshold, the fourth preset threshold is smaller than a seventh preset threshold, the seventh preset threshold is smaller than an eighth preset threshold, and the fifth preset threshold is smaller than a sixth preset threshold.
According to the method for monitoring corn lodging by using the laser radar, after the corn lodging severity level corresponding to the target monitoring area is determined, the method further comprises the following steps:
and traversing all the target monitoring areas to determine the corn lodging severity grade corresponding to each target monitoring area.
In a second aspect, the present invention also provides a device for monitoring corn lodging by using a laser radar, comprising:
constructing a device: the method comprises the steps of constructing a canopy height model in a total monitoring area based on laser radar data;
the screening device comprises: screening out the plant heights of all the unbowed corns and the plant heights of all the lodging corns in the target monitoring area based on the canopy height model in the total monitoring area;
the determination means: the method is used for determining the corn lodging severity grade corresponding to the target monitoring area based on the plant heights of all the un-lodging corns and the plant heights of all the lodging corns in the target monitoring area.
In a third aspect, the present invention provides an electronic device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for monitoring corn lodging by laser radar when executing the program.
In a fourth aspect, the present invention provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method for lidar to monitor corn lodging.
The method is characterized by constructing a canopy height model based on laser radar data, determining the plant heights of all the unhurried corns and the plant heights of all the lodging corns in a monitored area, and determining the lodging grades of all the lodging corns based on the plant heights of all the unhurried corns and the plant heights of all the lodging corns. Compared with the lodging monitoring means commonly used at present, the method for monitoring the lodging level of the corn by adopting the laser radar has the following advantages:
the method can accurately acquire the three-dimensional canopy structure information of the corn, acquire a large amount of point cloud data of the target in a short time, and have the advantage of high precision; the objectivity is strong, the most obvious change after the corn is lodging is reflected on the plant height and the lodging angle, the severity of the lodging stress of the corn is described more visually and quantitatively, and the accuracy of disaster diagnosis is improved; the application scene is extensive, and convenient integration is at different remote sensing platforms, works as and carries on the monitoring of lodging that can acquire the regional in disaster area of large tracts of land fast at the unmanned aerial vehicle platform, and response speed is fast to not being restricted by the illumination condition, being applicable to the changeable meteorological environment in disaster area.
Compared with the currently common lodging monitoring means, the method for independently predicting lodging of each target area according to different lodging corn areas can determine the plant heights of unhooked corns and lodging corns in different target areas by combining different growth stages, different growth environments, different irrigation modes, different disaster action areas and the like of corns in each target area, and further obtain the monitoring result of each target area in a targeted manner, so that the monitoring result is more accurate.
Drawings
In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for monitoring corn lodging by a laser radar provided by the invention;
FIG. 2 is a schematic flow chart of the present invention for constructing a canopy height model based on lidar data;
FIG. 3 is a schematic flow chart for screening out the plant heights of all the unbowed corns and the plant heights of all the lodging corns in a target monitoring area, provided by the invention;
FIG. 4 is a schematic flow chart of the present invention for determining a corn lodging severity level corresponding to a target monitoring area;
FIG. 5 is a schematic flow chart for determining a lodging severity level for each lodging corn provided by the present invention;
FIG. 6 is a second schematic flow chart of a method for monitoring corn lodging by laser radar according to the present invention;
FIG. 7 is a schematic diagram of the calculation method provided by the present invention for determining the lodging angle of each lodging corn;
FIG. 8 is a schematic structural diagram of a device for monitoring corn lodging by using a laser radar provided by the invention;
fig. 9 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Under the influence of global warming, extreme weather frequently occurs in the world in recent years, which causes serious influence on agricultural production, and the weather disasters frequently occur in seasonal wind regions with the highest change rate of the climate conditions. The data show that from 1951 to 2019, the extremely strong precipitation events tend to increase, the precipitation is mainly concentrated in summer, months 7-9 are the growth period of the corn, and extreme weather such as heavy rain and strong wind are the main reasons for lodging of the corn. The lodging can cause the inclination and even bending of the roots and stems of the corn, and the transportation of nutrients is seriously influenced; and lodging can cause the stacking of corn leaves, so that the illumination area of the leaves is reduced, the photosynthesis efficiency is reduced, and finally, the yield and the quality of the corn are reduced. Meanwhile, lodging can also seriously affect the health degree of the corn, so that the disease and insect resistance of the corn is reduced, and the lodging can also cause adverse effects on mechanized harvesting. Lodging is one of the main natural disasters affecting the production of grain crops.
The following methods are commonly used for monitoring corn lodging at present: firstly, manual investigation is carried out, scientific research personnel or agricultural insurance damage assessment personnel go to a disaster-affected area, the lodging level of the corn is evaluated by using methods such as manual measurement, field sampling and the like, and then the lodging disaster-affected condition of the whole planting area is presumed; secondly, acquiring a digital image and a multispectral image of a disaster area by using an unmanned aerial vehicle low-altitude remote sensing technology, acquiring canopy structure parameters and reflectivity data of lodging corns, and further evaluating the lodging situation of the corns; thirdly, the lodging and disaster situations of the corn are monitored by calculating various vegetation indexes and analyzing the spectral characteristic difference of the normal crops and the disaster crops by utilizing a satellite remote sensing technology. The method provides a certain support for evaluating the severity of the corn lodging disasters, but the problems of being not objective, accurate and rapid exist, and the improvement and even the solution of the problems can provide a powerful support for disaster rescue, agricultural insurance damage settlement and claim settlement and the cultivation of corn varieties.
In order to solve the technical problems, the invention provides a method, a device, equipment and a storage medium for monitoring corn lodging severity by using a laser radar, and specifically comprises the following steps:
fig. 1 is a schematic flow diagram of a method for monitoring corn lodging by using a laser radar, according to the present invention, corn is selected as a crop object targeted by the technical scheme of the present invention, specifically, after a corn plant is lodging, a morphological structure of the corn plant is changed greatly compared with a normal plant, which is also a basis for monitoring corn lodging by using a laser radar.
The laser radar is a novel active remote sensing technology, and can acquire parameters such as three-dimensional terrain, vegetation structure parameters, leaf area indexes and the like with high resolution of a forest ecosystem on a multi-space-time scale. Generally, the method is divided into satellite-borne, airborne and ground laser radars according to different bearing platforms.
When large-area corn disaster investigation is monitored, an airborne laser radar mode is generally adopted for data acquisition, an airborne laser radar system takes a man-machine or unmanned aerial vehicle as a carrying platform, the carried main load comprises a laser sensor, a global navigation satellite system receiver, an inertial navigation system and the like, before data acquisition, the flight range of an airborne laser radar aerial camera needs to be predetermined, and parameters such as the flight direction, the flight altitude, the flight speed, the point cloud density and the like are determined.
The invention constructs a Canopy Height Model (CHM) of lodging corn based on point cloud data of a monitoring area obtained by a multi-source laser radar, wherein the Canopy Height Model is a surface Model for expressing the Height of a Canopy from the ground, and simultaneously constructs an inversion Model of lodging angles, calculates the Canopy Height and the lodging angle of the lodging corn, divides the lodging corn into different lodging severity grades according to different lodging bending positions and lodging angles, and realizes high-precision lodging severity monitoring and drawing of the corn in the afflicted area, and specifically comprises the following steps:
constructing a canopy height model in the total monitoring area based on the laser radar data;
screening out the plant heights of all the unbowed corns and the plant heights of all the lodging corns in the target monitoring area based on the canopy height model in the total monitoring area;
determining a corn lodging severity grade corresponding to the target monitoring area based on the plant heights of all un-lodging corns and the plant heights of all lodging corns in the target monitoring area;
the total monitoring area is composed of a plurality of target monitoring areas and a plurality of non-target monitoring areas;
the target monitoring area includes at least lodging corn.
In step 101, firstly, a disaster area to be monitored is surveyed in real time, preferably, point cloud data of an unmanned airborne laser radar and a backpack radar in a lodging area of corn disaster is obtained, the point cloud data is the laser radar data, and the laser radar data obtaining time is preferably within one week of lodging. And further preprocessing the laser radar data to obtain a digital surface model and a digital elevation model so as to determine a canopy height model, and extracting the plant heights of all the unbowed corns and the plant heights of all the lodging corns after determining the canopy height model.
In step 102, the target monitoring area at least comprises lodging corns, and also comprises non-lodging corns, and the non-target monitoring area only comprises the non-lodging corns and does not comprise the lodging corns.
In step S103, the present invention compares and calculates the average plant height of the unbowed corn corresponding to the average plant height of the unbowed corn in each target monitoring area as a reference standard, and further determines the lodging angle of the plant height of each lodging corn relative to the average plant height of the unbowed corn in the target monitoring area, and further determines the lodging grades of all lodging corn based on the division standards of different lodging angles and lodging grades under different conditions divided in advance, and after determining the corn lodging severity grade corresponding to the target monitoring area, the present invention further includes: and traversing all target monitoring areas to determine the corn lodging severity level corresponding to each target monitoring area so as to determine the corn lodging severity levels corresponding to all target monitoring areas.
According to the method, a single comparison calculation principle is adopted, the specific corn lodging severity level graph of each target monitoring area is obtained, the high-precision lodging severity level of the corn in the disaster area is determined based on the lodging level graphs of all lodging corn in all target monitoring areas, the disaster area is further evaluated objectively more accurately, conveniently and efficiently, and powerful technical support is provided for damage settlement and claim settlement quickly and accurately.
The lidar is an active remote sensing system, and is a technology for calculating information such as the distance and angle from a sensor to a target by processing the time and the reflection intensity of each transmitted pulse which is reflected by the target and then returns to the sensor. The device can obtain the spatial three-dimensional information of crops, the resolution can reach centimeter level, the plant structure information of the crops can be rapidly obtained, the device is not affected by sunshine conditions, the device is suitable for the changeable weather of disaster areas, and the information such as canopy height and lodging angle of lodging corns can be accurately extracted. And laser radar can be carried by multiple platforms such as unmanned aerial vehicle, ground vehicle and staff, and the flexibility is high, has combined the advantage of multiple monitoring methods, has very strong adaptability, and a small amount of personnel can monitor the regional of a large scale disaster, and efficient, the precision is high, the objectivity is strong, not influenced by the illumination condition, can provide powerful technical support to work such as lodging severity of maize, agricultural insurance damage settlement claim.
Along with the development of the technology and the reduction of the price of the sensor, the laser radar technology is widely applied to the agricultural field, the laser radar can obtain the space three-dimensional information of crops, the resolution ratio is high, the plant structure information of the crops can be obtained, the influence of sunshine conditions is avoided, the plant height information can be accurately extracted, the laser radar technology is already applied to the extraction of the canopy height of the crops, and the most intuitive change of the corn after the influence of lodging disasters is morphological structure information such as the canopy height and the like. The method has the advantages that the corn lodging disaster is monitored by the aid of the laser radar, high-precision point cloud data of a disaster area are obtained, the disaster area and lodging types of the corn are calculated, and powerful technical support can be provided for rapid, accurate and objective evaluation and damage settlement of the disaster area.
Firstly, the laser radar can accurately acquire three-dimensional space structure information of lodging corn, compared with a common unmanned aerial vehicle which constructs point cloud data through digital aerial triangulation, the laser radar can acquire a large amount of point cloud data of a target in a short time, and can penetrate through a blade gap to acquire lower-layer blade and ground point cloud, and the advantage of millimeter-level resolution, so that complicated morphological structures such as tassels, blade ends and overlapped blades of the corn can be accurately mapped, and the precision advantage is high;
secondly, the objectivity of monitoring the lodging level of the corn by adopting the laser radar is strong, the most obvious change of the corn after lodging is reflected on the plant height and the lodging angle, the laser radar is the only means which can accurately estimate the two indexes at the same time at present, the severity of the corn under lodging stress can be quantitatively described, a profile map of a lodging area can be drawn, the method is more intuitive, and the accuracy of disaster diagnosis is improved;
finally, the application scene that adopts laser radar monitoring maize lodging level is extensive, and laser radar can be convenient integrated in different remote sensing platforms, the advantage of playing different platforms, can obtain the monitoring of lodging in the regional disaster area of large tracts of land fast when carrying on at the unmanned aerial vehicle platform, response speed is fast to not being restricted by the illumination condition, being applicable to the changeable meteorological environment in disaster area. The method is convenient and simple, has strong functions, and can efficiently and accurately monitor the lodging severity level of the corn.
Optionally, after constructing the canopy height model based on the lidar data, performing precision verification on the canopy height model based on a ratio of regression sum of squares and total sum of deviation sum of squares, specifically, referring to the following formula:
in the formula (1), the reaction mixture is,
for goodness of fit, the ratio of the regression sum of squares to the total sum of squared deviations, i.e., the agreement of the estimated values with the measured values,
closer to 1 indicates higher estimation accuracy.
As another method for verifying the accuracy, the method of the present invention may also perform accuracy verification on the canopy height model based on a root-mean-square of a sum of squares of differences between the sample canopy height data and the canopy height model data, and specifically, may refer to the following formula:
in the formula (2), RMSE is a root mean square error, which is a root mean square of a sum of squares of differences between a measured canopy height value and a laser radar estimated value, and reflects a degree of deviation of the estimated value and a true value to a certain extent.
As another method for verifying precision, the invention may also verify precision of the canopy height model based on normalized root mean square error, specifically, the following formula may be referred to:
in the formula (3), nRMSE is a normalized root mean square error, and RMSE is normalized to have a value between (0,1) to indicate the difference between the estimated value and the measured value, and the smaller the value, the smaller the difference, and the higher the estimation accuracy.
Optionally, after determining the lodging levels of all the lodging crops, performing correlation verification on the lodging levels of all the lodging corns, wherein the correlation verification is realized based on monitoring the correlation between the lodging angles and the sample lodging angles, and specifically, the following formula can be referred to:
in the formula (4), the plant height and the lodging angle of the lodging corn can be accurately estimated by the laser radar, and classified drawing is carried out on the research area according to lodging types by combining the advantages of the laser radar in the two aspects.
Compared with the currently common lodging monitoring means, the method for independently predicting lodging of each target area according to different lodging corn areas can determine the plant heights of unhooked corns and lodging corns in different target areas by combining different growth stages, different growth environments, different irrigation modes, different disaster action areas and the like of corns in each target area, and further obtain the monitoring result of each target area in a targeted manner, so that the monitoring result is more accurate.
Fig. 2 is a schematic flow chart of constructing a canopy height model based on lidar data according to the present invention, and specifically, the constructing the canopy height model based on lidar data includes:
processing the lidar data to construct a digital surface model and a digital elevation model;
determining the canopy height model based on the digital surface model and the digital elevation model.
In step S1011, the processing of the laser radar data includes decompressing, differentiating, calculating POS data, performing laser calibration, generating point cloud data, performing coordinate transformation on the point cloud data, performing attitude correction on the point cloud data, removing noise points from the point cloud data, and performing indirect edge processing on the ground base station to obtain point cloud data that can satisfy the post-processing.
Firstly, track calculation is carried out on laser radar data, a route/path is cut off according to time, track calculation is carried out, software is used for calculating route data, base station data is added to calibrate the route, an accurate track is obtained, and the track and the route are cut off according to time.
Then, matching the route/path with the point cloud, converting the point cloud data format into a standard format, matching the point cloud data with the processed track, inputting various parameters to calculate the point cloud data, splicing and visualizing the point cloud data, and converting the point cloud data into an industrial standard format of laser radar data after the processing is finished;
and finally, analyzing the point cloud data, including merging, denoising, filtering, cutting and other processing of the point cloud data, and extracting operations of constructing a digital canopy model, height of a canopy, inclination angle of a plant and the like.
A Digital Surface Model (DSM) is a ground elevation Model including the heights of Surface buildings, trees, crops, etc., and a Digital Surface Model is constructed and output by using all Surface points in a classified point cloud and points on the Surface morphology of natural and artificial ground objects such as crops, buildings, etc. on the Surface as feature points and using a planar range line with elevation information as a feature line.
A Digital Elevation Model (DEM) realizes Digital simulation of ground topography, namely Digital expression of topography surface morphology through limited topography Elevation data, and is an entity ground Model for expressing the ground Elevation in a group of ordered numerical array forms.
In the embodiment shown in the invention, the digital surface model is constructed by using the processed point cloud data, each point in the point cloud data has a corresponding three-dimensional coordinate (x, y, z), and all the points with the same or similar x and y values (the same horizontal coordinate) and the maximum z value in the data are extracted, namely the surface points of the monitoring area. Interpolating the extracted points to obtain a digital surface model in the region, namely a corn top canopy; the digital elevation model is ground elevation, bare ground points in a digital surface model product are screened out, the elevation value of the bare ground points is extracted, a planar digital elevation model is obtained through spatial interpolation, common kriging interpolation is generally adopted, and a spherical model is adopted as a semi-variation function.
In step S1012, a Canopy Height Model (CHM) is a surface Model for expressing the Height of the corn Canopy from the ground, and is usually determined based on a digital surface Model and a digital elevation Model, and the construction of the digital Canopy Model is a key step of lodging monitoring, and is obtained by subtracting the digital surface Model and the digital elevation Model, and the formula is as follows:
the CHM is a canopy height model, the DSM is a digital surface model, and the DEM is a digital elevation model.
Fig. 3 is a schematic flow chart of screening out plant heights of all un-lodging corns and plant heights of all lodging corns in a target monitoring area, which is provided by the invention, and the screening out plant heights of all un-lodging corns and plant heights of all lodging corns in the target monitoring area comprises:
marking lodging corns in the canopy height model corresponding to the total monitoring area, and determining a target monitoring area where the lodging corns are located;
and extracting the plant height of each un-lodging corn and the plant height of each lodging corn from the canopy height model corresponding to the target monitoring area.
In step 1021, before the area division is not performed according to whether the corn is lodging or not, the total monitoring area is a large-area corn planting field, according to fig. 2, a canopy height model corresponding to the total monitoring area is determined, and then the lodging corn in the canopy height model corresponding to the total monitoring area can be determined in the form of image data processing or manual marking, the total monitoring area can be equally divided according to the area, a plurality of sub-areas are determined, and any sub-area corresponding to the lodging corn is the target monitoring area.
In step 1022, the plant heights of the unbowed corns corresponding to each target monitoring area are different, and at this time, the mean value of the plant heights of the unbowed corns corresponding to the current target monitoring area may be obtained, and the plant height of each lodging corn is determined by taking the mean value as a reference.
Fig. 4 is a schematic flow chart of determining a corn lodging severity level corresponding to a target monitoring area, where the determining of the corn lodging severity level corresponding to the target monitoring area based on plant heights of all un-lodged corns and plant heights of all lodging corns in the target monitoring area includes:
equalizing the plant heights of all the unbolted corns in the target monitoring areas to determine the average plant height of all the unbolted corns in each target monitoring area;
and traversing all target monitoring areas, and determining the lodging severity grade of each lodging corn in each target monitoring area based on the plant height of each lodging corn and the average plant height.
In step S1031, the average plant height of all the un-lodging corns is determined mainly for one of all the target monitoring areas, further, the plant heights of all the un-lodging corns are averaged, and the average value of the plant heights of all the un-lodging corns is used as the average plant height of all the un-lodging corns. In actual lodging, lodging and non-lodging are often in staggered mixed distribution, and only the non-lodging corn region needs to be marked through digital images or manual work to serve as a contrast group, namely, one lodging region only needs to obtain a group of high-precision canopy height models, and the plant height of all non-lodging corns can be obtained. Further, the average plant height of all the unhusked corns determined in the invention is specific to the target monitoring area, not the total monitoring area, and the average plant height of all the unhusked corns determined by the invention is different for different target monitoring areas.
In step S1032, all the target monitoring areas are traversed, the plant height of each lodging corn corresponding to each target monitoring area and the average plant height are obtained, and the lodging severity level of each lodging corn in each target monitoring area is determined according to the plant height of each lodging corn corresponding to each target monitoring area and the average plant height.
As an embodiment of the invention, the invention extracts the height values of the unbowed corns based on a digital elevation model, takes the mean value of the height values as the canopy height of the normally growing corns, compares and calculates the plant height of each lodging crop with the average plant height, and determines the lodging severity grade of each lodging crop.
In other embodiments, the height values of unbowed corn are extracted based on a digital elevation model, the mean value of the height values is taken as the canopy height of the normally growing corn, the monitoring area is divided into monitoring sub-areas of different lodging types, further, fig. 7 is a schematic diagram of the calculation method for determining the lodging angle of each lodging corn provided by the invention, the height values are extracted from the monitoring sub-areas of different lodging types respectively, the mean value of the height values is taken as the canopy height of the different monitoring sub-areas, and finally, the lodging angle in the monitoring sub-areas is calculated based on a formula:
in the formula (6), the reaction mixture is,
to monitor the angle of lodging within a sub-area,
for the canopy heights of the different monitoring sub-regions,
the average plant height of the crops without lodging.
Optionally, the plant heights of all the lodging corns are traversed to determine the lodging levels of each lodging crop, and those skilled in the art understand that this step can accurately determine the lodging levels of each lodging crop, and thus determine the disaster situations of the lodging corns more accurately, and in the actual process, the above steps may be implemented but are too complex, and as a variation of the present invention, in combination with the embodiment in step S1032, the plant heights of the lodging corns in all the monitoring sub-regions are correspondingly traversed to determine the lodging levels of the lodging corns in each monitoring sub-region.
After the canopy height model is constructed, the plant height of the lodging corn can be directly calculated, but the canopy height of the corn can change along with different growth periods; compared with the prior art, the lodging angle is the most intuitive biological index for evaluating the influence degree of the lodging disasters on the corns, and the lodging severity can be evaluated more objectively. The lodging angle measurement in the past can only rely on manual measurement, and inefficiency just is difficult for drawing the lodging angle of a large number of lodging district maize. The emergence of laser radar technology and the application of the laser radar technology to lodging monitoring provide an effective technical means for the extraction of lodging angles.
Fig. 5 is a schematic flow chart of determining a lodging severity grade of each lodging corn, which is based on the plant height of each lodging corn and the average plant height, according to the present invention, and includes:
determining the lodging angle of each lodging corn in any target monitoring area based on the plant height of each lodging corn in any target monitoring area and the average plant height;
determining a lodging angle of each lodging corn and a corresponding interval of the plant height of the corn in a preset lodging level strategy;
determining a lodging level for each lodging corn based on the interval;
and traversing all the target monitoring areas until determining the lodging level of each lodging corn in all the target monitoring areas.
In step S10321, in an embodiment of the present invention, the lodging angle of each lodging corn is determined according to the plant height of each lodging corn in any one of the target monitoring areas and the average plant height, and the lodging angle of each lodging corn is determined with reference to formula (6), while in other embodiments, if the monitoring sub-areas are taken as the calculation objects, the lodging angle of each lodging corn in each monitoring sub-area is determined based on the average plant height of the lodging corn in each monitoring sub-area and the average plant height of the non-lodging crop.
In step S10322, the preset lodging level policy is:
determining the lodging grade of the lodging corn as root lodging under the condition that the lodging angle is at a first preset threshold value;
determining the lodging grade of the lodging corn as one grade of stalk fold under the condition that the lodging angle is in a second preset threshold value;
under the condition that the lodging angle is at a third preset threshold value, determining the lodging grade of the lodging corn as one stalk lodging grade;
determining the lodging grade of the lodging corn as a second stalk-fold grade under the condition that the lodging angle is at a fourth preset threshold value and the plant height of the corn is at a fifth preset threshold value;
determining the lodging grade of the lodging corn as a stem lodging grade under the condition that the lodging angle is at a fourth preset threshold value and the plant height of the corn is at a sixth preset threshold value;
determining the lodging grade of the lodging corn as three stem-lodging grades under the condition that the lodging angle is at a seventh preset threshold value;
determining the lodging grade of the lodging corn as non-lodging under the condition that the lodging angle is in an eighth preset threshold value;
the first preset threshold is smaller than a second preset threshold, the second preset threshold is smaller than a third preset threshold, the third preset threshold is smaller than a fourth preset threshold, the fourth preset threshold is smaller than a seventh preset threshold, the seventh preset threshold is smaller than an eighth preset threshold, and the fifth preset threshold is smaller than a sixth preset threshold.
In step S10323, the lodging level of each lodging corn is determined based on each interval, and those skilled in the art understand that the past corn lodging monitoring is limited by the accuracy of the data source, and the corn lodging types can only be classified into more abstract concepts of lodging/non-lodging or lodging and light/heavy lodging, and the lodging types cannot be described quantitatively.
In step S10324, all target monitoring areas are traversed until the lodging level of each lodging corn in all target monitoring areas is determined, according to steps 10321 to S10323, the lodging level of each lodging corn in any target monitoring area can be determined, and according to step S10324, all target monitoring areas are traversed, and then the lodging level of each lodging corn in all target monitoring areas is determined.
By means of the advantage that the laser radar can acquire the high-precision three-dimensional information of the corn plants, quantitative evaluation can be given to lodging positions and lodging severity of lodging corns. In order to standardize the evaluation of the severity of corn lodging, it is preferable to perform manual experiments to simulate various common corn lodging states, and in the embodiment, the lodging severity grades of corn are divided into 6 types according to the corn bending/breaking position and the lodging angle, and the types are from strong to weak: lodging in a ground laying mode when a root inverted (RL) -main root is broken and half fibrous roots are not broken; stem fold (SF 1) -20 cm above ground is broken; stem fold (SF 2) -ear fold without breaking; the stem falls (ST) -the plant stem inclines, set up 3 stem fall types, the contained angle of plant and ground is 30 °, 45 °, 60 ° (ST 1, ST2, ST 3) respectively, as shown in fig. 7, the classification system includes soil, the root falls (RL), the stem is rolled over (SF 1, SF 2), 30 °, 45 °, 60 ° (ST 1, ST2, ST 3) and not lodging (CK), give different shades of gray color to the 8 kinds of categorised back and show, simultaneously, utilize laser radar can obtain the advantage of the three-dimensional structure parameter of target, can draw the section view of lodging maize, and then can more audio-visually judge the lodging severity of maize.
More specifically, the lodging type is judged according to two indexes of plant height and lodging angle. Firstly, carrying out preliminary classification according to a lodging angle: when the corn lodging angle is about 10 degrees, the corn is root lodging; when the lodging angle of the corn is about 20 degrees, the corn is folded into a first stage; when the lodging angle of the corn is about 45 degrees, the corn is in a second-stage stem folding or second-stage stem inverting state; when the lodging angle of the corn is about 30 degrees, the corn is inverted for one grade; when the lodging angle of the corn is about 60 degrees, the corn is inverted three-stage stem. Secondly, distinguishing a stem bending secondary stage from a stem pouring secondary stage: when the plant height of the corn is about 1.4m, the plant is the second grade of stalk break, and when the plant height of the corn is about 1.8m, the plant is the second grade of stalk inversion. In conclusion, the lodging corns can be accurately classified by combining two indexes of plant height and angle.
Fig. 6 is a second schematic flow chart of the method for monitoring corn lodging by using laser radar, according to the second schematic flow chart, firstly, under the support of field lodging field investigation information and agronomy expert knowledge, dividing corn lodging types into 6 types according to the severity, and sequentially according to the severity from strong to weak: root fall (RL), stem fold heel (SF 1), stem fold waist (SF 2), stem fall 30 degrees (ST 1), 45 degrees (ST 2), 60 degrees (ST 3), then obtain the laser radar point cloud data of monitoring area, extract canopy height and lodging angle of lodging maize, monitor maize lodging severity grade and drawing at last.
Specifically, determining original point cloud data, trajectory data and base station data based on the original data, performing trajectory correction based on the trajectory data and the base station data, determining point cloud and trajectory matching based on the original point cloud data and the trajectory correction, further performing point cloud data preprocessing, determining a canopy height Model based on a Digital Surface Model (DSM) and a Digital Elevation Model (DEM), further extracting the plant height of the corn, performing inversion of lodging angles, then verifying the extraction accuracy based on sample data, and drawing disaster area division so as to determine an lodging severity classification diagram and a lodging section diagram of the corn.
Fig. 8 is a schematic structural diagram of a lodging level monitoring device provided by the present invention, which includes a construction device 1: for constructing the model of the height of the canopy in the total monitored area based on the lidar data, the operation principle of the constructing apparatus 1 may refer to the foregoing step 101, which is not described herein again.
The lodging level monitoring device also comprises a screening device 2: the method is used for screening out plant heights of all unbowed corns and plant heights of all lodging corns in the target monitoring area based on the canopy height model in the total monitoring area, and the working principle of the screening device 2 can refer to the step 102, which is not described herein again.
The lodging level monitoring device also comprises a determining device 3: the method is used for determining the corn lodging severity grade corresponding to the target monitoring area based on the plant heights of all the un-lodging corns and the plant heights of all the lodging corns in the target monitoring area, and the working principle of the determining device 3 can refer to the step 103, which is not described herein again.
The method can quickly acquire the plant structure information of the corn, is not influenced by sunshine conditions, is suitable for the variable weather in disaster areas, and can accurately extract the information of canopy height, lodging angle and the like of the lodging corn. And laser radar can be carried by multiple platforms such as unmanned aerial vehicle, ground vehicle and staff, and the flexibility is high, has combined the advantage of multiple monitoring methods, has very strong adaptability, and a small amount of personnel can monitor the regional of a large scale disaster, and efficient, the precision is high, the objectivity is strong, not influenced by the illumination condition, can provide powerful technical support to the lodging area and the severity of maize, work such as agricultural insurance settlement damage claim.
The laser radar can obtain the spatial three-dimensional information of crops, has high resolution, can obtain the plant structure information of the crops, is not influenced by sunshine conditions, can accurately extract the plant height information, is applied to the extraction of the canopy height of the crops, and the most intuitive change of the corn after being influenced by lodging disasters is morphological structure information such as the canopy height and the like. The method has the advantages that the corn lodging disaster is monitored by the aid of the laser radar, high-precision point cloud data of a disaster area are obtained, the disaster area and lodging types of the corn are calculated, and powerful technical support can be provided for quick, accurate and objective evaluation and damage settlement of the disaster area.
The laser radar can accurately acquire the three-dimensional space structure information of the lodging corn, compared with a common unmanned aerial vehicle which constructs point cloud data through digital aerial triangulation, the laser radar can acquire a large amount of point cloud data of a target in a short time, can penetrate through a blade gap to acquire lower-layer blade and ground point clouds and has the advantage of millimeter-level resolution, can accurately map complex morphological structures such as tassels, blade ends and overlapped blades of the corn, and has the advantage of high precision;
the objectivity of monitoring the lodging level of the corn by adopting the laser radar is strong, the most obvious change of the corn after lodging is reflected on the plant height and the lodging angle, the laser radar is the only means which can simultaneously and accurately estimate the two indexes, and simultaneously can quantitatively describe the severity of the corn under lodging stress, and can draw a profile map of the lodging area, so that the method is more intuitive, and the accuracy of disaster diagnosis is improved;
the application scene of monitoring the corn lodging level by adopting the laser radar is wide, the laser radar can be conveniently integrated on different remote sensing platforms, the advantages of different platforms are exerted, when the laser radar is carried on an unmanned aerial vehicle platform, lodging monitoring of a large-area disaster area can be quickly obtained, the response speed is high, the laser radar is not limited by illumination conditions, and the laser radar is suitable for meteorological environments with variable disaster areas. The corn lodging level monitoring system is convenient and simple, has strong functions, and can efficiently and accurately realize corn lodging level monitoring.
Compared with the conventional lodging monitoring means, the method for individually predicting lodging in each target area according to different lodging corn areas can determine the plant heights of unbolted corn and lodging corn in different target areas by combining different growth stages, different growth environments, different irrigation modes, different disaster action areas and the like of corn in each target area, and further pertinently obtain the monitoring result of each target area, so that the monitoring result is more accurate.
Fig. 9 is a schematic structural diagram of an electronic device provided in the present invention, and as shown in fig. 9, the electronic device may include: a processor (processor) 210, a communication Interface (Communications Interface) 220, a memory (memory) 230 and a communication bus 240, wherein the processor 210, the communication Interface 220 and the memory 230 communicate with each other via the communication bus 240. Processor 210 may call logic instructions in memory 230 to perform a lodging level monitoring method comprising: constructing a canopy height model in a total monitoring area based on laser radar data; screening out the plant heights of all the unbowed corns and the plant heights of all the lodging corns in the target monitoring area based on the canopy height model in the total monitoring area; determining a corn lodging severity grade corresponding to the target monitoring area based on the plant heights of all un-lodging corns and the plant heights of all lodging corns in the target monitoring area; the total monitoring area is composed of a plurality of target monitoring areas and a plurality of non-target monitoring areas; the target monitoring area includes at least lodging corn.
In addition, the logic instructions in the memory 230 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, the computer program product comprising a computer program, the computer program being storable on a non-transitory computer readable storage medium, wherein when the computer program is executed by a processor, the computer is capable of executing a method for performing lodging level monitoring provided by the methods, the method comprising: constructing a canopy height model in the total monitoring area based on the laser radar data; screening out the plant heights of all the unbowed corns and the plant heights of all the lodging corns in the target monitoring area based on the canopy height model in the total monitoring area; determining a corn lodging severity grade corresponding to the target monitoring area based on the plant heights of all un-lodging corns and the plant heights of all lodging corns in the target monitoring area; the total monitoring area is composed of a plurality of target monitoring areas and a plurality of non-target monitoring areas; the target monitoring area includes at least lodging corn.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a lodging level monitoring method provided by the above methods, the method comprising: constructing a canopy height model in the total monitoring area based on the laser radar data; screening out the plant heights of all the unbowed corns and the plant heights of all the lodging corns in the target monitoring area based on the canopy height model in the total monitoring area; determining a corn lodging severity grade corresponding to the target monitoring area based on the plant heights of all un-lodging corns and the plant heights of all lodging corns in the target monitoring area; the total monitoring area is composed of a plurality of target monitoring areas and a plurality of non-target monitoring areas; the target monitoring area includes at least lodging corn.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. A method for monitoring corn lodging by a laser radar is characterized by comprising the following steps:
constructing a canopy height model in the total monitoring area based on the laser radar data;
screening out the plant heights of all the un-lodging corns and all the lodging corns in the target monitoring area based on the canopy height model in the total monitoring area;
determining a corn lodging severity grade corresponding to the target monitoring area based on the plant heights of all un-lodging corns and the plant heights of all lodging corns in the target monitoring area;
the total monitoring area is composed of a plurality of target monitoring areas and a plurality of non-target monitoring areas;
the target monitoring area at least comprises lodging corn; non-target monitoring areas include only non-lodging corn; the total monitoring area is divided equally according to the area, a plurality of sub-areas are determined, and any sub-area corresponding to the lodging corn is the target monitoring area;
screening out the plant heights of all the un-lodging corns and the plant heights of all the lodging corns in the target monitoring area based on the canopy height model in the total monitoring area, and comprising the following steps:
marking lodging corns in the canopy height model corresponding to the total monitoring area, and determining a target monitoring area where the lodging corns are located;
extracting the plant height of each un-lodging corn and the plant height of each lodging corn from the canopy height model corresponding to the target monitoring area;
the determining of the corn lodging severity grade corresponding to the target monitoring area based on the plant heights of all the un-lodging corns and the plant heights of all the lodging corns in the target monitoring area comprises the following steps:
equalizing the plant heights of all the unhusked corns in the target monitoring areas to determine the average plant height of all the unhusked corns in each target monitoring area;
and traversing all the target monitoring areas, and determining the lodging severity grade of each lodging corn in each target monitoring area based on the plant height of each lodging corn and the average plant height.
2. The lidar method for monitoring corn lodging according to claim 1, wherein the constructing of the canopy height model based on lidar data comprises:
processing the lidar data to construct a digital surface model and a digital elevation model;
determining the canopy height model based on the digital surface model and the digital elevation model.
3. The method for lidar to monitor corn lodging according to claim 1, wherein traversing all target monitoring areas, determining the lodging severity level of each lodging corn in each target monitoring area based on the plant height of each lodging corn and the average plant height comprises:
determining the lodging angle of each lodging corn in any target monitoring area based on the plant height of each lodging corn in any target monitoring area and the average plant height;
determining a lodging angle of each lodging corn and a corresponding interval of the plant height of the corn in a preset lodging level strategy;
determining a lodging level for each lodging corn based on the interval;
and traversing all the target monitoring areas until determining the lodging level of each lodging corn in all the target monitoring areas.
4. The lidar method of claim 3, wherein the preset lodging level policy is:
determining the lodging grade of the lodging corn as root lodging under the condition that the lodging angle is at a first preset threshold value;
under the condition that the lodging angle is at a second preset threshold value, determining the lodging grade of the lodging corn as one grade of stalk breakage;
under the condition that the lodging angle is at a third preset threshold value, determining the lodging grade of the lodging corn as one stalk lodging grade;
determining the lodging grade of the lodging corn as a second stalk-fold grade under the condition that the lodging angle is at a fourth preset threshold value and the plant height of the corn is at a fifth preset threshold value;
determining the lodging grade of the lodging corn as a stem lodging grade under the condition that the lodging angle is at a fourth preset threshold value and the plant height of the corn is at a sixth preset threshold value;
determining the lodging grade of the lodging corn as three-stage stem lodging under the condition that the lodging angle is at a seventh preset threshold value;
under the condition that the lodging angle is at an eighth preset threshold value, determining the lodging grade of the lodging corn as no lodging;
the first preset threshold is smaller than a second preset threshold, the second preset threshold is smaller than a third preset threshold, the third preset threshold is smaller than a fourth preset threshold, the fourth preset threshold is smaller than a seventh preset threshold, the seventh preset threshold is smaller than an eighth preset threshold, and the fifth preset threshold is smaller than a sixth preset threshold.
5. The lidar method for monitoring corn lodging according to claim 1, wherein after determining the corn lodging severity level corresponding to the target monitoring area, further comprising:
and traversing all target monitoring areas to determine the corn lodging severity level corresponding to each target monitoring area.
6. The utility model provides a device that laser radar monitored maize lodging which characterized in that includes:
constructing a device: the method comprises the steps of constructing a canopy height model in a total monitoring area based on laser radar data;
the screening device comprises: screening out the plant heights of all the un-lodging corns and the plant heights of all the lodging corns in the target monitoring area based on the canopy height model in the total monitoring area;
the determination means: the system is used for determining the corn lodging severity grade corresponding to the target monitoring area based on the plant heights of all the un-lodging corns and the plant heights of all the lodging corns in the target monitoring area;
the total monitoring area is composed of a plurality of target monitoring areas and a plurality of non-target monitoring areas;
the target monitoring area at least comprises lodging corn; non-target monitoring areas include only un-lodging corn; the total monitoring area is divided equally according to the area, a plurality of sub-areas are determined, and any sub-area corresponding to the lodging corn is the target monitoring area;
screening out the plant heights of all the un-lodging corns and the plant heights of all the lodging corns in the target monitoring area based on the canopy height model in the total monitoring area, and comprising the following steps:
marking lodging corns in the canopy height model corresponding to the total monitoring area, and determining a target monitoring area where the lodging corns are located;
extracting the plant height of each un-lodging corn and the plant height of each lodging corn from the canopy height model corresponding to the target monitoring area;
determining a corn lodging severity grade corresponding to the target monitoring area based on plant heights of all un-lodging corns and plant heights of all lodging corns in the target monitoring area, wherein the determining comprises the following steps:
equalizing the plant heights of all the unhusked corns in the target monitoring areas to determine the average plant height of all the unhusked corns in each target monitoring area;
and traversing all the target monitoring areas, and determining the lodging severity grade of each lodging corn in each target monitoring area based on the plant height of each lodging corn and the average plant height.
7. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the method for lidar to monitor corn lodging according to any of claims 1-5.
8. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the method for lidar to monitor corn lodging according to any of claims 1-5.
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