CN114460099A - Unmanned aerial vehicle-based water hyacinth monitoring method and device, unmanned aerial vehicle and medium - Google Patents
Unmanned aerial vehicle-based water hyacinth monitoring method and device, unmanned aerial vehicle and medium Download PDFInfo
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Abstract
The invention discloses a water hyacinth monitoring method and device based on an unmanned aerial vehicle, the unmanned aerial vehicle and a medium. The method comprises the following steps: acquiring multispectral data of a water area to be monitored by a multispectral camera arranged on the unmanned aerial vehicle; determining the area of the water hyacinth in the water area to be monitored based on the multispectral data and the set range; and monitoring the water hyacinth by analyzing the multispectral data in the area of the water hyacinth. By utilizing the method, the water hyacinth and other organisms can be better distinguished by analyzing the multispectral data in the area where the water hyacinth is located, so that the effective monitoring on the water hyacinth can be realized.
Description
Technical Field
The embodiment of the invention relates to the technical field of ecological environment monitoring, in particular to a water hyacinth monitoring method and device based on an unmanned aerial vehicle, the unmanned aerial vehicle and a medium.
Background
In recent years, the water hyacinth grows and spreads in the middle and lower reaches of Yangtze river and bead triangle areas, so that river channels, reservoirs, irrigation and drainage stations and the like are blocked, drinking water sources are polluted, and the normal agricultural production and water conservancy irrigation and drainage are seriously affected, so that the effective monitoring of the water hyacinth is very necessary.
The existing water hyacinth monitoring scheme generally adopts a mode of erecting cameras, the scheme is limited in efficiency, and the purpose of comprehensively and effectively monitoring can be achieved only by erecting a certain number of cameras.
Disclosure of Invention
The embodiment of the invention provides a water hyacinth monitoring method and device based on an unmanned aerial vehicle, the unmanned aerial vehicle and a medium, so as to realize comprehensive and effective monitoring of a water hyacinth.
In a first aspect, an embodiment of the present invention provides a water hyacinth monitoring method based on an unmanned aerial vehicle, including:
acquiring multispectral data of a water area to be monitored by a multispectral camera arranged on the unmanned aerial vehicle;
determining the area of the water hyacinth in the water area to be monitored based on the multispectral data and the set range;
and monitoring the water hyacinth by analyzing the multispectral data in the area of the water hyacinth.
In a second aspect, an embodiment of the present invention further provides an unmanned aerial vehicle-based water hyacinth monitoring device, including:
the acquisition module is used for acquiring multispectral data of a water area to be monitored through a multispectral camera arranged on the unmanned aerial vehicle;
the determining module is used for determining the area of the water hyacinth in the water area to be monitored based on the multispectral data;
and the analysis module is used for analyzing the multispectral data in the area where the water hyacinth is located to monitor the water hyacinth.
In a third aspect, an embodiment of the present invention further provides an unmanned aerial vehicle, including: a multispectral camera to collect multispectral data;
one or more processors;
storage means for storing one or more programs;
the processor is respectively connected with the multispectral camera and the storage device;
the one or more programs are executed by the one or more processors, so that the one or more processors implement the unmanned aerial vehicle-based water hyacinth monitoring method provided by the embodiment of the invention.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the method for monitoring a water hyacinth based on an unmanned aerial vehicle provided in an embodiment of the present invention is implemented.
The embodiment of the invention provides a water hyacinth monitoring method and device based on an unmanned aerial vehicle, the unmanned aerial vehicle and a medium. The method comprises the following steps: acquiring multispectral data of a water area to be monitored by a multispectral camera arranged on the unmanned aerial vehicle; determining the area of the water hyacinth in the water area to be monitored based on the multispectral data and the set range; and monitoring the water hyacinth by analyzing the multispectral data in the area of the water hyacinth. By utilizing the technical scheme, the water hyacinth and other organisms can be better distinguished by analyzing the multispectral data in the area where the water hyacinth is located, so that the water hyacinth can be effectively monitored.
Drawings
Fig. 1 is a schematic flow chart of a water hyacinth monitoring method based on an unmanned aerial vehicle according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a water hyacinth monitoring method based on an unmanned aerial vehicle according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a water hyacinth monitoring device based on an unmanned aerial vehicle according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an unmanned aerial vehicle according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the operations (or steps) as a sequential process, many of the operations can be performed in parallel, concurrently or simultaneously. In addition, the order of the operations may be re-arranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like. In addition, the embodiments and features of the embodiments in the present invention may be combined with each other without conflict.
The term "include" and variations thereof as used herein are intended to be open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment".
It should be noted that the concepts of "first", "second", etc. mentioned in the present invention are only used for distinguishing corresponding contents, and are not used for limiting the order or interdependence relationship.
It is noted that references to "a", "an", and "the" modifications in the present invention are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that reference to "one or more" unless the context clearly dictates otherwise.
Example one
Fig. 1 is a schematic flow chart of a water hyacinth monitoring method based on an unmanned aerial vehicle according to an embodiment of the present invention, where the method is applicable to a case where a water hyacinth is monitored, and the method can be executed by a water hyacinth monitoring apparatus based on an unmanned aerial vehicle, where the apparatus can be implemented by software and/or hardware and is generally integrated on an unmanned aerial vehicle.
It can be understood that in recent years, water hyacinth grows and spreads in middle and lower reaches of Yangtze river and bead triangle areas, and once the water hyacinth coverage rate exceeds three, a plurality of defects can be caused, such as blocking a channel, influencing shipping and excretion, and becoming the first enemy of agriculture, water conservancy and environmental protection; the flow of the water body is limited, the water body is not irradiated by sunlight and becomes smelly, so that the dissolved oxygen in the water body is reduced, the growth of plankton is inhibited, and the river ecological environment is damaged; providing a breeding ground for the germs such as schistosome, encephalitis and influenza, breeding mosquitoes and flies, and providing breathing and breeding opportunities for the larvae of mosquitoes; destroying drinking water resources. Because of blockage, the water body can not flow freely, various pollution sources such as heavy metal elements and other trace elements harmful to the human body can not be effectively removed, and people can directly or indirectly absorb the polluted water source; the influence on the quality of fresh water at a water source water collection point is that a large amount of water lettuce covers the water surface to reduce the pH value of the water; increased CO2 concentration; the chroma of water is increased; the acidity in water is also increased, so that the use value of water resources is greatly reduced until the water can not be drunk. Therefore, the growth condition of the water hyacinth needs to be monitored in a large area and high efficiency.
With the development of unmanned aerial vehicle technology in recent years, unmanned aerial vehicle low-altitude remote sensing can make up the deficiencies of satellite remote sensing and artificial law enforcement environment monitoring means in timeliness and accuracy; meanwhile, with the continuous expansion of big data related services, higher requirements are put forward on the accuracy and the real-time performance of data, so that the data acquisition is integrated by using the low-altitude remote sensing of the unmanned aerial vehicle for the inevitable trend of the development of the water body protection industry. The unmanned aerial vehicle can realize high-space and large-area monitoring through different heights, can realize accurate monitoring in a small range of a low space, and can monitor tens of thousands of square measuring areas simultaneously for many times in the monitoring process.
On the other hand, the monitoring data of a large-area water area is obtained through multispectral analysis, the surface information can be combined with the traditional point information, the basis is provided for the evaluation of the macroscopic environment of the whole measuring area through displaying the water area condition and the breeding degree and range of the water hyacinths, and the image collected by the common camera is a visible light wave band RGB image, so that the water area to be monitored is difficult to distinguish.
The water hyacinth unmanned aerial vehicle monitoring system can be regarded as a networking monitoring system integrating hardware, software and a network, can realize multistage networking and cross-region monitoring, and can realize centralized monitoring and unified management of a front-end system at a monitoring position.
As shown in fig. 1, a water hyacinth monitoring method based on an unmanned aerial vehicle according to an embodiment of the present invention includes the following steps:
and S110, acquiring multispectral data of the water area to be monitored through a multispectral camera arranged on the unmanned aerial vehicle.
In this embodiment, the multispectral camera may be a camera capable of expanding in two directions of infrared light and ultraviolet light on the basis of visible light, and the multispectral camera may receive information radiated or reflected by the same target in different narrow spectral bands through a combination of an optical filter or a beam splitter and a photosensitive film, so as to form images of a plurality of different spectral bands, that is, multispectral data. The number of the multispectral cameras is not limited, and may be one or more. The water area to be monitored can be regarded as the water area needing to be monitored, and the area of the water area to be monitored is not limited.
Specifically, a multispectral camera can be mounted on the unmanned aerial vehicle to collect multispectral data of the water area to be monitored so as to analyze the multispectral data of the water area to be monitored.
And S120, determining the area of the water hyacinth in the water area to be monitored based on the multispectral data and the set range.
The setting range may be a range formed by a threshold value for distinguishing an area where the water hyacinth is located, and the means for determining the setting range is not limited in this embodiment, for example, the setting range may be preset by a system or related personnel, or may be determined based on a range detection model.
Specifically, based on the collected multispectral data and the set range, the area of the water hyacinth in the water area to be monitored can be determined so as to analyze the area of the water hyacinth. The embodiment does not limit the specific steps, for example, the collected multispectral data may be used to generate an NGVI value image; the NGVI value image and the set range can be used for distinguishing and extracting the water area part, the water hyacinth, the algae and the like, so that the area where the water hyacinth is located in the water area to be monitored can be determined. Wherein the set range may be formed by the NGVI threshold.
In one embodiment, the set range is determined based on a range detection model obtained by training a predetermined neural network based on a data set, wherein the data set comprises multispectral data of different water areas, and water hyacinth is included in the different water areas.
In this embodiment, the range detection model may be a model for determining a set range based on input multispectral data, and the range detection model may be obtained by training a preset neural network based on a data set, where the data set may include multispectral data of a plurality of different water areas, and the water hyacinth is included in the different water areas, and the size of the data set is not limited as long as the accuracy of the range detection model is ensured. The data set may further include a plurality of setting ranges corresponding to the multispectral data, respectively, so as to implement training of the range detection model. In the application stage of the range detection model, the acquired multispectral data can be input into the range detection model to determine the corresponding set range, so that the area where the water hyacinth is located is determined based on the comparison between the multispectral data and the set range.
Specifically, the preset neural network may be trained based on a data set to obtain a range detection model, and the specific steps for obtaining the range detection model are not limited in this embodiment, for example, multispectral data of a plurality of different water areas in the data set may be grouped to obtain a training set and a test set, and a model obtained through training in the training set may be tested in the test set and may be used as the range detection model after a certain condition is met. The conditions are not limited to these.
S130, monitoring the water hyacinth by analyzing the multispectral data in the area where the water hyacinth is located.
The multispectral data in the area where the water hyacinth is located is obtained from an unlimited source, for example, the multispectral data may be extracted from the water area to be monitored, or the multispectral data may be acquired from the area where the water hyacinth is located by using a multispectral camera, which is not limited in this embodiment.
The area of the water hyacinth in the water area to be monitored can be obtained through the steps, and the water hyacinth can be monitored by analyzing the multispectral data in the area of the water hyacinth. In this embodiment, the step of monitoring the water hyacinth is not limited, for example, the density of the water hyacinth may be determined based on multispectral data in the area where the water hyacinth is located, and then the density of the water hyacinth is compared with a density threshold to monitor the water hyacinth, or the multispectral data in the area where the water hyacinth is located may be obtained by a multispectral camera; then determining the breeding range of the water hyacinth based on the multispectral data of the area where the water hyacinth is located; and finally, analyzing the breeding range to realize the monitoring of the water hyacinth, wherein the breeding range can be understood as the current breeding range of the water hyacinth and can be determined through multispectral data in the area where the water hyacinth is located.
The embodiment of the invention provides a water hyacinth monitoring method based on an unmanned aerial vehicle, which comprises the following steps: acquiring multispectral data of a water area to be monitored by a multispectral camera arranged on the unmanned aerial vehicle; determining the area of the water hyacinth in the water area to be monitored based on the multispectral data and the set range; and monitoring the water hyacinth by analyzing the multispectral data in the area of the water hyacinth. By utilizing the method, the water hyacinth and other organisms can be better distinguished by analyzing the multispectral data in the area where the water hyacinth is located, so that the effective monitoring on the water hyacinth can be realized.
On the basis of the above-described embodiment, a modified embodiment of the above-described embodiment is proposed, and it is to be noted herein that, in order to make the description brief, only the differences from the above-described embodiment are described in the modified embodiment.
In one embodiment, before acquiring multispectral data of a water area to be monitored by a multispectral camera arranged on an unmanned aerial vehicle, the method further comprises the following steps:
collecting RGB images of a water area to be monitored through an RGB camera on the unmanned aerial vehicle;
and adjusting the monitoring range of the water area to be monitored based on the RGB image.
The RGB camera may be a camera that collects RGB images in a visible light band, and the RGB images may be images that are superimposed in different proportions by three primary colors of red, green, and blue. In this embodiment, the unmanned aerial vehicle may further carry one or more RGB cameras to collect RGB images of the waters to be monitored.
Specifically, the RGB images of the water area to be monitored can be collected through an RGB camera on the unmanned aerial vehicle; and then adjusting the monitoring range of the water area to be monitored based on the RGB image. For example, the range of the area where the water hyacinth is located in the water area to be monitored can be roughly determined based on the RGB image, and then the range of the water area to be monitored based on the multispectral camera is adjusted based on the rough range of the area where the water hyacinth is located.
In one embodiment, adjusting the monitoring range of the water area to be monitored based on the RGB image includes:
and if the ratio of the area where the water hyacinth is located in the RGB image is smaller than a set threshold, reducing the monitoring range of the water area to be monitored.
The set threshold may be a critical value of the ratio of the area where the water hyacinth is located in the RGB image, and the step does not limit the set threshold, and may be preset by a system or related personnel, for example.
Specifically, the occupation ratio of the area where the water hyacinth is located in the RGB image can be determined based on the RGB image, and if the occupation ratio is smaller than a set threshold, it indicates that the area where the water hyacinth is located does not exceed the critical value of the area where the water hyacinth is located, and at this time, the monitoring range of the water area to be monitored can be narrowed, so that the water area to be monitored can be accurately monitored based on the multispectral camera.
In one embodiment, adjusting the monitoring range of the water area to be monitored based on the RGB image includes:
and if the ratio of the area of the water hyacinth in the RGB image is larger than or equal to a set threshold, expanding the monitoring range of the water area to be detected.
In this embodiment, if the proportion of the area where the water hyacinth is located in the RGB image is greater than or equal to the set threshold, it indicates that the area where the water hyacinth is located currently exceeds the critical value of the area where the water hyacinth is located, and at this time, the monitoring range of the water area to be monitored needs to be expanded, so as to monitor the water area to be monitored based on the adjusted monitoring range.
As can be seen from the above description, the embodiment of the present invention can find the water hyacinth region: the method comprises the steps that the maneuverability of the environment monitoring unmanned aerial vehicle and the high-altitude observation capability of a pan-tilt camera are utilized, and breeding heads are found in time; simultaneously, can acquire the image data in monitoring waters: the collected RGB image and the multispectral data are verified mutually, and the reliability of the monitoring data is guaranteed.
Example two
Fig. 2 is a schematic flow chart of a water hyacinth monitoring method based on an unmanned aerial vehicle according to a second embodiment of the present invention, and the second embodiment is optimized based on the above embodiments. In this embodiment, the monitoring of the water hyacinth is realized by analyzing the multispectral data in the area where the water hyacinth is located, and the monitoring is further embodied as follows: acquiring multispectral data in the area where the water hyacinth is located through the multispectral camera; determining a breeding range of the water hyacinth based on the multispectral data in the area where the water hyacinth is located; and analyzing the breeding range to realize the monitoring of the water hyacinth.
Please refer to the first embodiment for a detailed description of the present embodiment.
As shown in fig. 2, a water hyacinth monitoring method based on an unmanned aerial vehicle provided by the second embodiment of the present invention includes the following steps:
s210, acquiring multispectral data of the water area to be monitored through a multispectral camera arranged on the unmanned aerial vehicle.
S220, determining the area of the water hyacinth in the water area to be monitored based on the multispectral data and the set range.
And S230, acquiring multispectral data in the area where the water hyacinth is located through the multispectral camera.
S240, determining a breeding range of the water hyacinth based on the multispectral data in the area where the water hyacinth is located.
In this embodiment, after the area where the water hyacinth is located in the water area to be monitored is determined, the multispectral data in the area where the water hyacinth is located may be further obtained by the multispectral camera, and meanwhile, the breeding range of the water hyacinth may be determined based on the multispectral data in the area where the water hyacinth is located.
And S250, analyzing the breeding range to realize the monitoring of the water hyacinth.
After the breeding range of the water hyacinth is obtained, the breeding range needs to be analyzed to monitor the water hyacinth, the embodiment does not limit the specific steps of the analysis, for example, the breeding range can be analyzed to obtain the growth situation, the density and the like of the water hyacinth, if the density of the water hyacinth is high and the growth situation is good, the water hyacinth can be monitored and simultaneously treated by taking corresponding measures; if the density of the water hyacinth is low, the water hyacinth needs to be monitored continuously.
The embodiment of the invention provides a water hyacinth monitoring method based on an unmanned aerial vehicle, which comprises the following steps: acquiring multispectral data of a water area to be monitored by a multispectral camera arranged on the unmanned aerial vehicle; determining the area of the water hyacinth in the water area to be monitored based on the multispectral data and the set range; acquiring multispectral data in the area where the water hyacinth is located through the multispectral camera; determining a breeding range of the water hyacinth based on the multispectral data in the area where the water hyacinth is located; and analyzing the breeding range to realize the monitoring of the water hyacinth. By the aid of the method, the breeding range of the water hyacinth can be determined based on the multispectral data in the area where the water hyacinth is located, and meanwhile, the breeding range is analyzed, so that monitoring accuracy is improved, and the water hyacinth is monitored.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a water hyacinth monitoring device based on an unmanned aerial vehicle according to a third embodiment of the present invention, where the device is applicable to a case of monitoring a water hyacinth, and the device may be implemented by software and/or hardware and is generally integrated on an unmanned aerial vehicle.
As shown in fig. 3, the apparatus includes:
the acquisition module 310 is used for acquiring multispectral data of a water area to be monitored through a multispectral camera arranged on the unmanned aerial vehicle;
the determining module 320 is configured to determine, based on the multispectral data, an area where a water hyacinth is located in the water area to be monitored;
the analysis module 330 is configured to analyze the multispectral data in the area where the water hyacinth is located, so as to monitor the water hyacinth.
In the water hyacinth monitoring device based on the unmanned aerial vehicle provided by the third embodiment of the invention, the multispectral camera arranged on the unmanned aerial vehicle through the acquisition module 310 acquires multispectral data of a water area to be monitored; determining the area of the water hyacinth in the water area to be monitored by the determining module 320 based on the multispectral data; the multispectral data in the area where the water hyacinth is located is analyzed through the analysis module 330, so that the water hyacinth is monitored. The device can better distinguish the water hyacinth from other organisms by analyzing the multispectral data in the area where the water hyacinth is located, so that the water hyacinth can be effectively monitored.
Further, the analysis module 330 includes:
acquiring multispectral data in the region where the water hyacinth is located through the multispectral camera;
determining a breeding range of the water hyacinth based on the multispectral data of the area where the water hyacinth is located;
and analyzing the breeding range to realize the monitoring of the water hyacinth.
Further, the set range is determined based on a range detection model, the range detection model is obtained by training a preset neural network based on a data set, the data set comprises multispectral data of different water areas, and water hyacinth is included in the different water areas.
Further, before the acquiring module 310, the method further includes:
the acquisition unit is used for acquiring RGB images of a water area to be monitored through an RGB camera on the unmanned aerial vehicle;
and the adjusting unit is used for adjusting the monitoring range of the water area to be monitored based on the RGB image.
Further, the adjusting unit is specifically configured to:
and if the ratio of the area where the water hyacinth is located in the RGB image is smaller than a set threshold, reducing the monitoring range of the water area to be monitored.
Further, the adjusting unit is specifically configured to:
and if the ratio of the area of the water hyacinth in the RGB image is larger than or equal to a set threshold, expanding the monitoring range of the water area to be detected.
The water hyacinth monitoring device based on the unmanned aerial vehicle can execute the water hyacinth monitoring method based on the unmanned aerial vehicle provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example four
Fig. 4 is a schematic structural diagram of an unmanned aerial vehicle according to a fourth embodiment of the present invention. As shown in fig. 4, an unmanned aerial vehicle provided by the fourth embodiment of the present invention includes: a multispectral camera 40, the multispectral camera 40 being configured to collect multispectral data; one or more processors 41 and storage 42; the processor 41 is connected with the multispectral camera 40 and the storage device 42 respectively;
the number of the processors 41 in the drone may be one or more, and one processor 41 is taken as an example in fig. 4; storage 42 is used to store one or more programs; the one or more programs are executed by the one or more processors 41, so that the one or more processors 41 implement the drone-based water hyacinth monitoring method according to any one of the embodiments of the present invention.
The drone may further include: an input device 43 and an output device 44.
The processor 41, the storage device 42, the input device 43 and the output device 44 in the drone may be connected by a bus or other means, as exemplified by the bus connection in fig. 4.
The storage device 42 in the drone serves as a computer-readable storage medium, and may be configured to store one or more programs, which may be software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the drone-based water hyacinth monitoring method according to one or two embodiments of the present invention (for example, the modules in the drone-based water hyacinth monitoring device shown in fig. 3 include the acquisition module 310, the determination module 320, and the analysis module 330). The processor 41 executes various functional applications and data processing of the unmanned aerial vehicle by running software programs, instructions and modules stored in the storage device 42, that is, the unmanned aerial vehicle-based water hyacinth monitoring method in the above method embodiment is implemented.
The storage device 42 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the drone, and the like. Further, the storage 42 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, storage 42 may further include memory located remotely from processor 41, which may be connected to the device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 43 may be used to receive input numeric or character information and generate key signal inputs relating to user settings and function control of the drone. The output device 44 may include a display device such as a display screen.
And, when the one or more programs included in the above-mentioned drone are executed by the one or more processors 41, the programs perform the following operations:
acquiring multispectral data of a water area to be monitored by a multispectral camera arranged on the unmanned aerial vehicle;
determining the area of the water hyacinth in the water area to be monitored based on the multispectral data and the set range;
and monitoring the water hyacinth by analyzing the multispectral data in the area of the water hyacinth.
In one embodiment, the drone further comprises:
and the RGB camera is connected with the processor and is used for acquiring RGB images.
EXAMPLE five
An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the program is used, when executed by a processor, to execute a water hyacinth monitoring method based on an unmanned aerial vehicle, where the method includes:
acquiring multispectral data of a water area to be monitored by a multispectral camera arranged on the unmanned aerial vehicle;
determining the area of the water hyacinth in the water area to be monitored based on the multispectral data and the set range;
and monitoring the water hyacinth by analyzing the multispectral data in the area of the water hyacinth.
Optionally, the program, when executed by the processor, may be further configured to execute the method for monitoring a water hyacinth based on an unmanned aerial vehicle according to any embodiment of the present invention.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read Only Memory (ROM), an Erasable Programmable Read Only Memory (EPROM), a flash Memory, an optical fiber, a portable CD-ROM, an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. A computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take a variety of forms, including, but not limited to: an electromagnetic signal, an optical signal, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, Radio Frequency (RF), etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A water hyacinth monitoring method based on an unmanned aerial vehicle is characterized by comprising the following steps:
acquiring multispectral data of a water area to be monitored by a multispectral camera arranged on the unmanned aerial vehicle;
determining the area of the water hyacinth in the water area to be monitored based on the multispectral data and the set range;
and monitoring the water hyacinth by analyzing the multispectral data in the area of the water hyacinth.
2. The method of claim 1, wherein the monitoring of the water hyacinth is performed by analyzing multispectral data of the area in which the water hyacinth is located, and comprises:
acquiring multispectral data in the area where the water hyacinth is located through the multispectral camera;
determining a breeding range of the water hyacinth based on the multispectral data in the area where the water hyacinth is located;
and analyzing the breeding range to realize the monitoring of the water hyacinth.
3. The method of claim 1, wherein the set range is determined based on a range detection model, the range detection model is obtained by training a neural network based on a data set, the data set comprises multispectral data of different water areas, and the water hyacinth is included in the different water areas.
4. The method of claim 1, wherein before collecting the multispectral data of the water area to be monitored by a multispectral camera disposed on the drone, further comprising:
collecting RGB images of a water area to be monitored through an RGB camera on the unmanned aerial vehicle;
and adjusting the monitoring range of the water area to be monitored based on the RGB image.
5. The method of claim 4, wherein adjusting the monitoring range of the water area to be monitored based on the RGB image comprises:
and if the ratio of the area where the water hyacinth is located in the RGB image is smaller than a set threshold, reducing the monitoring range of the water area to be monitored.
6. The method of claim 4, wherein adjusting the monitoring range of the water area to be monitored based on the RGB image comprises:
and if the ratio of the area of the water hyacinth in the RGB image is larger than or equal to a set threshold, expanding the monitoring range of the water area to be detected.
7. The utility model provides a water hyacinth monitoring devices based on unmanned aerial vehicle which characterized in that includes:
the acquisition module is used for acquiring multispectral data of a water area to be monitored through a multispectral camera arranged on the unmanned aerial vehicle;
the determining module is used for determining the area of the water hyacinth in the water area to be monitored based on the multispectral data;
and the analysis module is used for analyzing the multispectral data in the area where the water hyacinth is located to monitor the water hyacinth.
8. An unmanned aerial vehicle, comprising: a multispectral camera to collect multispectral data;
one or more processors;
storage means for storing one or more programs;
the processor is respectively connected with the multispectral camera and the storage device;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-6.
9. The drone of claim 8, further comprising:
and the RGB camera is connected with the processor and is used for acquiring RGB images.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-6.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106197380A (en) * | 2016-09-04 | 2016-12-07 | 南京理工大学 | Aquatic vegetation monitoring method based on unmanned plane and system |
| CN106875636A (en) * | 2017-04-05 | 2017-06-20 | 南京理工大学 | Blue algae monitoring method for early warning and system based on unmanned plane |
| CN109459392A (en) * | 2018-11-06 | 2019-03-12 | 南京农业大学 | A kind of rice the upperground part biomass estimating and measuring method based on unmanned plane multispectral image |
| CN109507119A (en) * | 2018-09-28 | 2019-03-22 | 浙江农林大学 | A kind of water quality element concentration detection method based on unmanned plane spectrum |
| CN110334683A (en) * | 2019-07-12 | 2019-10-15 | 四川长虹电器股份有限公司 | Method based on experiment of UAV remote sensing system intelligent recognition plant |
| CN110683653A (en) * | 2019-11-21 | 2020-01-14 | 广州国苑规划设计有限公司 | Method for treating aquatic plant sewage |
| CN110954484A (en) * | 2019-12-09 | 2020-04-03 | 南京搜天数据科技有限公司 | Method for evaluating urban river water quality by using multispectral data of unmanned aerial vehicle |
| CN111047566A (en) * | 2019-12-04 | 2020-04-21 | 昆明市滇池高原湖泊研究院 | Method for carrying out aquatic vegetation annual change statistics by unmanned aerial vehicle and multispectral satellite image |
| CN113029971A (en) * | 2021-02-10 | 2021-06-25 | 北京农业信息技术研究中心 | Crop canopy nitrogen monitoring method and system |
| CN213620278U (en) * | 2020-10-22 | 2021-07-06 | 枣庄学院 | Multisource remote sensing monitoring device of unmanned aerial vehicle |
-
2022
- 2022-02-11 CN CN202210127765.6A patent/CN114460099A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106197380A (en) * | 2016-09-04 | 2016-12-07 | 南京理工大学 | Aquatic vegetation monitoring method based on unmanned plane and system |
| CN106875636A (en) * | 2017-04-05 | 2017-06-20 | 南京理工大学 | Blue algae monitoring method for early warning and system based on unmanned plane |
| CN109507119A (en) * | 2018-09-28 | 2019-03-22 | 浙江农林大学 | A kind of water quality element concentration detection method based on unmanned plane spectrum |
| CN109459392A (en) * | 2018-11-06 | 2019-03-12 | 南京农业大学 | A kind of rice the upperground part biomass estimating and measuring method based on unmanned plane multispectral image |
| CN110334683A (en) * | 2019-07-12 | 2019-10-15 | 四川长虹电器股份有限公司 | Method based on experiment of UAV remote sensing system intelligent recognition plant |
| CN110683653A (en) * | 2019-11-21 | 2020-01-14 | 广州国苑规划设计有限公司 | Method for treating aquatic plant sewage |
| CN111047566A (en) * | 2019-12-04 | 2020-04-21 | 昆明市滇池高原湖泊研究院 | Method for carrying out aquatic vegetation annual change statistics by unmanned aerial vehicle and multispectral satellite image |
| CN110954484A (en) * | 2019-12-09 | 2020-04-03 | 南京搜天数据科技有限公司 | Method for evaluating urban river water quality by using multispectral data of unmanned aerial vehicle |
| CN213620278U (en) * | 2020-10-22 | 2021-07-06 | 枣庄学院 | Multisource remote sensing monitoring device of unmanned aerial vehicle |
| CN113029971A (en) * | 2021-02-10 | 2021-06-25 | 北京农业信息技术研究中心 | Crop canopy nitrogen monitoring method and system |
Non-Patent Citations (2)
| Title |
|---|
| 李江波等: "《图谱分析技术在农产品质量和安全评估中的应用》", vol. 1, 中国地质大学出版社, pages: 207 - 87 * |
| 闻亮;李胜;陈清;姚文才;: "基于无人机遥感的水质监测信息集成与应用", 江苏水利, no. 10, pages 35 - 40 * |
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