CN117115687A - Unmanned aerial vehicle accurate fertilization method and system based on artificial intelligence technology - Google Patents
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Abstract
The application is applicable to the technical field of unmanned aerial vehicle fertilization, and provides an unmanned aerial vehicle precise fertilization method and system based on an artificial intelligence technology, wherein the method comprises the following steps: receiving spraying delay and unmanned aerial vehicle flight height uploaded by a user, determining a shooting angle, and performing angle adjustment on a camera according to the shooting angle so that fertilizer is sprayed in a correct area; acquiring crop nodding images through a camera, and performing shape analysis on the crop nodding images to determine crop types; performing color analysis on the crop nodding image to determine the maturity of the crop; determining spraying fertilizer information and spraying quantity information according to the crop types and the crop maturity; therefore, the opening and closing conditions of various fertilizer valves can be determined according to the fertilizer spraying information, and then the flow of the opened fertilizer valves is controlled by combining the spraying amount information, so that the unmanned aerial vehicle can carry out accurate fertilization, and the fertilization effect is better.
Description
Technical Field
The application relates to the technical field of unmanned aerial vehicle fertilization, in particular to an unmanned aerial vehicle precise fertilization method and system based on an artificial intelligence technology.
Background
Compared with ground fertilization machinery, unmanned aerial vehicle fertilization has the advantages that trafficability is good, and stability, usability, cruising ability and effective load of the existing unmanned aerial vehicle are continuously improved, and application in agricultural production is wider and wider. Because the types or growth vigor of crops are different, the types and the fertilization amount of the fertilizer required by each area are different, and the fertilizer types and the fertilization amount of the fertilizer cannot be well controlled by the current unmanned aerial vehicle. Therefore, an unmanned aerial vehicle accurate fertilization method and system based on an artificial intelligence technology are needed to solve the problems.
Disclosure of Invention
Aiming at the defects existing in the prior art, the application aims to provide an unmanned aerial vehicle accurate fertilization method and system based on an artificial intelligence technology, so as to solve the problems existing in the background technology.
The application discloses an unmanned aerial vehicle precise fertilization method based on an artificial intelligence technology, which comprises the following steps of:
receiving spraying delay and unmanned aerial vehicle flight height uploaded by a user, determining a shooting angle, and performing angle adjustment on a camera according to the shooting angle;
acquiring crop nodding images through a camera, and performing shape analysis on the crop nodding images to determine crop types;
performing color analysis on the crop nodding image to determine the maturity of the crop;
determining spraying fertilizer information and spraying quantity information according to the crop types and the crop maturity;
the opening and closing and the flow of various fertilizer valves are controlled according to the fertilizer spraying information and the spraying amount information, so that the unmanned aerial vehicle sprays corresponding fertilizer to fertilize.
As a further scheme of the application: the step of determining the shooting angle and carrying out angle adjustment on the camera according to the shooting angle specifically comprises the following steps:
the flying speed of the unmanned aerial vehicle is called;
calculating a shooting angle, tan shooting angle = unmanned aerial vehicle flying height +.2% (spraying delay x flying speed);
and sending the shooting angle to a camera angle controller so that the camera can conduct angle adjustment.
As a further scheme of the application: the step of performing shape analysis on the crop nodding image to determine the crop type specifically comprises the following steps:
amplifying and cutting the crop nodding image to obtain a local crop image;
carrying out edge sharpening treatment on the local crop image, and enhancing shape characteristics;
and identifying the processed local crop image based on the convolutional neural network to obtain the crop type.
As a further scheme of the application: the step of performing color analysis on the crop nodding image to determine the maturity of the crop specifically comprises the following steps:
according to the determined crop types, invoking color information of each maturity, wherein the color information of each maturity comprises a plurality of maturity, and each maturity corresponds to a color feature;
determining the pixel point number of each color feature in the crop nodding image;
and determining the maturity corresponding to the color characteristic with the maximum pixel number as the crop maturity.
As a further scheme of the application: the step of determining the spraying fertilizer information and the spraying quantity information according to the crop types and the crop maturity specifically comprises the following steps:
inputting crop types and crop maturity into a crop fertilizer warehouse, wherein the crop fertilizer warehouse comprises all crop types, each crop type corresponds to spraying fertilizer information, each crop type corresponds to a plurality of maturity, and each maturity corresponds to spraying amount information;
and outputting corresponding spraying fertilizer information and corresponding spraying quantity information.
Another object of the present application is to provide an unmanned aerial vehicle precise fertilization system based on artificial intelligence technology, the system comprising:
the camera angle adjusting module is used for receiving the spraying delay and the unmanned aerial vehicle flight height uploaded by the user, determining a shooting angle and adjusting the angle of the camera according to the shooting angle;
the crop type determining module is used for acquiring crop nodding images through the camera and carrying out shape analysis on the crop nodding images to determine crop types;
the crop maturity determining module is used for performing color analysis on the crop nodding image to determine crop maturity;
the fertilizer spraying information module is used for determining fertilizer spraying information and spraying amount information according to the crop types and the crop maturity;
and the fertilizer valve control module is used for controlling the opening and closing and flow of various fertilizer valves according to the spraying fertilizer information and the spraying quantity information, so that the unmanned aerial vehicle sprays corresponding fertilizer to fertilize.
As a further scheme of the application: the camera angle adjustment module comprises:
the flying speed adjusting unit is used for adjusting the flying speed of the unmanned aerial vehicle;
a shooting angle calculation unit for calculating a shooting angle, tan shooting angle=unmanned aerial vehicle flight height ≡ (spraying delay×flight speed);
and the angle adjustment control unit is used for sending the shooting angle to the camera angle controller so that the camera can perform angle adjustment.
As a further scheme of the application: the crop species determination module includes:
the image amplifying and cutting unit is used for amplifying and cutting the crop nodding image to obtain a local crop image;
the edge sharpening processing unit is used for carrying out edge sharpening processing on the local crop image and enhancing the shape characteristics;
and the crop type determining unit is used for identifying the processed local crop image based on the convolutional neural network to obtain the crop type.
As a further scheme of the application: the crop maturity determination module includes:
the color information calling unit is used for calling color information of each maturity according to the determined crop variety, the color information of each maturity comprises a plurality of maturity, and each maturity corresponds to a color feature;
the pixel point number identification unit is used for determining the pixel point number of each color feature in the crop nodding image;
the maturity determining unit is used for determining that the maturity corresponding to the color feature with the largest pixel number is crop maturity.
As a further scheme of the application: the fertilizer spraying information module comprises:
the crop information input unit is used for inputting crop types and crop maturity into a crop fertilizer warehouse, the crop fertilizer warehouse comprises all crop types, each crop type corresponds to spraying fertilizer information, each crop type corresponds to a plurality of maturity, and each maturity corresponds to spraying amount information;
and the fertilizer information determining unit is used for outputting corresponding spraying fertilizer information and corresponding spraying quantity information.
Compared with the prior art, the application has the beneficial effects that:
according to the application, the angle of the camera can be adjusted according to the shooting angle, so that fertilizer is sprayed in a correct area, and dislocation is avoided; then acquiring a crop nodding image through a camera, performing shape analysis on the crop nodding image to determine crop types, and performing color analysis on the crop nodding image to determine crop maturity; determining spraying fertilizer information and spraying quantity information according to the crop types and the crop maturity; therefore, the opening and closing conditions of various fertilizer valves can be determined according to the fertilizer spraying information, and then the flow of the opened fertilizer valves is controlled by combining the spraying amount information, so that the unmanned aerial vehicle can carry out accurate fertilization, and the fertilization effect is better.
Drawings
Fig. 1 is a flow chart of an unmanned aerial vehicle precise fertilization method based on an artificial intelligence technology.
Fig. 2 is a flowchart for determining a shooting angle in an unmanned aerial vehicle precise fertilization method based on an artificial intelligence technology.
Fig. 3 is a flow chart of determining crop types in an unmanned aerial vehicle precise fertilization method based on artificial intelligence technology.
Fig. 4 is a flow chart of determining crop maturity in an unmanned aerial vehicle precise fertilization method based on artificial intelligence technology.
Fig. 5 is a flowchart of determining spraying fertilizer information and spraying amount information in an unmanned aerial vehicle precise fertilization method based on artificial intelligence technology.
Fig. 6 is a schematic structural diagram of an unmanned aerial vehicle precise fertilization system based on artificial intelligence technology.
Fig. 7 is a schematic structural diagram of a camera angle adjustment module in an unmanned aerial vehicle precise fertilization system based on artificial intelligence technology.
Fig. 8 is a schematic structural diagram of a crop type determining module in an unmanned aerial vehicle precise fertilization system based on artificial intelligence technology.
Fig. 9 is a schematic structural diagram of a crop maturity determining module in an unmanned aerial vehicle precise fertilization system based on artificial intelligence technology.
Fig. 10 is a schematic structural diagram of a module for spraying fertilizer information in an unmanned aerial vehicle precise fertilization system based on artificial intelligence technology.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clear, the present application will be described in further detail with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
Specific implementations of the application are described in detail below in connection with specific embodiments.
As shown in fig. 1, the embodiment of the application provides an unmanned aerial vehicle accurate fertilization method based on an artificial intelligence technology, which comprises the following steps:
s100, receiving spraying delay and unmanned aerial vehicle flight height uploaded by a user, determining a shooting angle, and performing angle adjustment on a camera according to the shooting angle;
s200, acquiring a crop nodding image through a camera, and performing shape analysis on the crop nodding image to determine the crop type;
s300, performing color analysis on the crop nodding image to determine the maturity of the crop;
s400, determining spraying fertilizer information and spraying quantity information according to the crop types and the crop maturity;
s500, controlling the opening and closing and flow of various fertilizer valves according to the fertilizer spraying information and the spraying amount information, so that the unmanned aerial vehicle sprays corresponding fertilizer to perform fertilizer application.
It should be noted that, compared with ground fertilization machinery, unmanned aerial vehicle fertilization has the advantage of good trafficability, and stability, usability, duration and payload of the existing unmanned aerial vehicle are continuously improved, and application in agricultural production is wider and wider. Because the types or growth vigor of crops are different, the types and the fertilization amount of the fertilizer required by each area are different, the fertilizer types and the fertilization amount of the fertilizer cannot be well controlled by the current unmanned aerial vehicle.
In the embodiment of the application, a user needs to input a spraying delay time and the flight height of the unmanned aerial vehicle in advance, wherein the spraying delay time is the time difference between the opening of a fertilizer valve and the spraying of fertilizer; it is easy to understand that when a camera on the unmanned aerial vehicle shoots aiming at the point A right below, a fertilizer valve is opened after the type and the fertilizer application amount of the fertilizer are determined, and when the fertilizer is sprayed out, the fertilizer tends to fall in front of the point A, so that the angle of the camera is inclined downwards. After the angle of the camera is adjusted, acquiring a crop nodding image through the camera, performing shape analysis on the crop nodding image to determine the crop type, and performing color analysis on the crop nodding image to determine the crop maturity; then can confirm to spray fertilizer information and spray quantity information according to crops kind and crops maturity, just can confirm the switching condition of all kinds of fertilizer valves according to spraying fertilizer information, then combine the flow of the fertilizer valve that spray quantity information to open to control, so, can make unmanned aerial vehicle carry out accurate fertilization, the effect is better.
As shown in fig. 2, as a preferred embodiment of the present application, the step of determining the shooting angle and performing an angle adjustment on the camera according to the shooting angle specifically includes:
s101, calling the flying speed of the unmanned aerial vehicle;
s102, calculating a shooting angle, wherein tan shooting angle = unmanned aerial vehicle flight height/unmanned aerial vehicle flight speed (spraying delay x flight speed);
and S103, sending the shooting angle to a camera angle controller so that the camera can conduct angle adjustment.
In the embodiment of the application, in order to determine a more accurate shooting angle, the flight speed of the unmanned aerial vehicle at the moment needs to be adjusted, then the shooting angle can be calculated, tan shooting angle = unmanned aerial vehicle flight height +.times. (spraying delay × flight speed), the shooting angle is the angle of downward inclination of the camera, after the shooting angle is calculated, the shooting angle is sent to the camera angle controller, and the angle of the camera can be adjusted by itself.
As shown in fig. 3, as a preferred embodiment of the present application, the step of performing shape analysis on the crop nodding image to determine the crop species specifically includes:
s201, amplifying and cutting a crop nodding image to obtain a local crop image;
s202, carrying out edge sharpening treatment on the local crop image, and enhancing shape characteristics;
and S203, identifying the processed local crop image based on the convolutional neural network to obtain the crop type.
In the embodiment of the application, the convolutional neural network is constructed by performing image feature learning on all crops in the farm in advance, and the classification and identification of the crop images by using the deep convolutional neural network are widely applied and are not described herein. In order to enable the convolutional neural network to be more accurate and rapid in identification, the embodiment of the application can amplify and cut the crop nodding image to obtain a local crop image, and preferably, the local crop image is a partial image of the middle area of the crop nodding image, so that the content in the local crop image is less, and the identification is more convenient; and then carrying out edge sharpening treatment on the local crop image, and enhancing the shape characteristics so that the recognition result is more accurate.
As shown in fig. 4, as a preferred embodiment of the present application, the step of performing color analysis on the crop nodding image to determine the maturity of the crop specifically includes:
s301, invoking color information of each maturity according to the determined crop types, wherein the color information of each maturity comprises a plurality of maturity, and each maturity corresponds to a color feature;
s302, determining the number of pixel points of each color feature in the crop nodding image;
s303, determining the maturity corresponding to the color feature with the largest pixel number as the crop maturity.
In the embodiment of the application, each crop type corresponds to each maturity color information, the corresponding relation between the crop type and each maturity color information needs to be established in advance, each maturity color information comprises a plurality of maturity, each maturity corresponds to a color feature, the color features can be a color range, then the color features are compared with colors in the crop nodding image for identification, the pixel point occupied by each color feature is determined, and finally the maturity corresponding to the color feature with the largest pixel point is determined as the crop maturity.
As shown in fig. 5, as a preferred embodiment of the present application, the step of determining the spraying fertilizer information and the spraying amount information according to the crop type and the crop maturity specifically includes:
s401, inputting crop types and crop maturity into a crop fertilizer warehouse, wherein the crop fertilizer warehouse comprises all crop types, each crop type corresponds to spraying fertilizer information, each crop type corresponds to a plurality of maturity, and each maturity corresponds to spraying amount information;
s402, outputting corresponding spraying fertilizer information and corresponding spraying quantity information.
In the embodiment of the application, a crop fertilizer library is established in advance, the crop fertilizer library comprises all crop types in a farm, each crop type corresponds to spraying fertilizer information, each crop type corresponds to a plurality of maturity, and each maturity corresponds to spraying quantity information, so that the spraying fertilizer information and the spraying quantity information can be directly obtained by inputting the crop types and the crop maturity into the crop fertilizer library.
As shown in fig. 6, the embodiment of the application further provides an unmanned aerial vehicle precise fertilization system based on artificial intelligence technology, which comprises:
the camera angle adjustment module 100 is configured to receive the spraying delay and the unmanned aerial vehicle flight height uploaded by the user, determine a shooting angle, and perform angle adjustment on the camera according to the shooting angle;
the crop type determining module 200 is configured to acquire a crop nodding image through a camera, perform shape analysis on the crop nodding image, and determine a crop type;
the crop maturity determining module 300 is configured to perform color analysis on the crop nodding image to determine crop maturity;
a spray fertilizer information module 400 for determining spray fertilizer information and spray amount information according to the crop type and crop maturity;
the fertilizer valve control module 500 is used for controlling the opening and closing and the flow of various fertilizer valves according to the spraying fertilizer information and the spraying quantity information, so that the unmanned aerial vehicle sprays corresponding fertilizer to fertilize.
In the embodiment of the application, a user needs to input a spraying delay time and the flight height of the unmanned aerial vehicle in advance, wherein the spraying delay time is the time difference between the opening of a fertilizer valve and the spraying of fertilizer; it is easy to understand that when a camera on the unmanned aerial vehicle shoots aiming at the point A right below, a fertilizer valve is opened after the type and the fertilizer application amount of the fertilizer are determined, and when the fertilizer is sprayed out, the fertilizer tends to fall in front of the point A, so that the angle of the camera is inclined downwards. After the angle of the camera is adjusted, acquiring a crop nodding image through the camera, performing shape analysis on the crop nodding image to determine the crop type, and performing color analysis on the crop nodding image to determine the crop maturity; then can confirm to spray fertilizer information and spray quantity information according to crops kind and crops maturity, just can confirm the switching condition of all kinds of fertilizer valves according to spraying fertilizer information, then combine the flow of the fertilizer valve that spray quantity information to open to control, so, can make unmanned aerial vehicle carry out accurate fertilization, the effect is better.
As shown in fig. 7, as a preferred embodiment of the present application, the camera angle adjustment module 100 includes:
a flying speed pickup unit 101 for picking up the flying speed of the unmanned aerial vehicle;
a shooting angle calculation unit 102 for calculating a shooting angle, tan shooting angle=unmanned aerial vehicle flight height ≡ (spraying delay×flight speed);
and the angle adjustment control unit 103 is configured to send the shooting angle to a camera angle controller, so that the camera performs angle adjustment.
As shown in fig. 8, as a preferred embodiment of the present application, the crop species determination module 200 includes:
an image enlarging and clipping unit 201, configured to enlarge and clip a crop nodding image to obtain a local crop image;
an edge sharpening processing unit 202, configured to perform edge sharpening processing on the local crop image, and enhance shape features;
the crop type determining unit 203 identifies the processed partial crop image based on the convolutional neural network, and obtains the crop type.
As shown in fig. 9, as a preferred embodiment of the present application, the crop maturity determining module 300 includes:
a color information retrieving unit 301, configured to retrieve color information of each maturity according to the determined crop species, where the color information of each maturity includes a plurality of maturity, and each maturity corresponds to a color feature;
a pixel point number identifying unit 302, configured to determine the pixel point number of each color feature in the crop nodding image;
the maturity determining unit 303 is configured to determine that the maturity corresponding to the color feature with the largest number of pixels is the crop maturity.
As shown in fig. 10, as a preferred embodiment of the present application, the spray fertilizer information module 400 includes:
a crop information input unit 401 for inputting crop species and crop maturity into a crop fertilizer library, the crop fertilizer library including all crop species, each crop species corresponding to spray fertilizer information, and each crop species corresponding to a plurality of maturity, each maturity corresponding to spray amount information;
and a fertilizer information determining unit 402 for outputting the corresponding spraying fertilizer information and the corresponding spraying amount information.
The foregoing description of the preferred embodiments of the present application should not be taken as limiting the application, but rather should be understood to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the application.
It should be understood that, although the steps in the flowcharts of the embodiments of the present application are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
Other embodiments of the present disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (10)
1. An unmanned aerial vehicle accurate fertilization method based on an artificial intelligence technology is characterized by comprising the following steps of:
receiving spraying delay and unmanned aerial vehicle flight height uploaded by a user, determining a shooting angle, and performing angle adjustment on a camera according to the shooting angle;
acquiring crop nodding images through a camera, and performing shape analysis on the crop nodding images to determine crop types;
performing color analysis on the crop nodding image to determine the maturity of the crop;
determining spraying fertilizer information and spraying quantity information according to the crop types and the crop maturity;
the opening and closing and the flow of various fertilizer valves are controlled according to the fertilizer spraying information and the spraying amount information, so that the unmanned aerial vehicle sprays corresponding fertilizer to fertilize.
2. The unmanned aerial vehicle precise fertilization method based on the artificial intelligence technology according to claim 1, wherein the step of determining the shooting angle and performing angle adjustment on the camera according to the shooting angle specifically comprises the following steps:
the flying speed of the unmanned aerial vehicle is called;
calculating a shooting angle, tan shooting angle = unmanned aerial vehicle flying height +.2% (spraying delay x flying speed);
and sending the shooting angle to a camera angle controller so that the camera can conduct angle adjustment.
3. The unmanned aerial vehicle precise fertilization method based on artificial intelligence technology according to claim 1, wherein the step of performing shape analysis on the crop nodding image to determine the crop species specifically comprises:
amplifying and cutting the crop nodding image to obtain a local crop image;
carrying out edge sharpening treatment on the local crop image, and enhancing shape characteristics;
and identifying the processed local crop image based on the convolutional neural network to obtain the crop type.
4. The unmanned aerial vehicle precise fertilization method based on artificial intelligence technology according to claim 1, wherein the step of performing color analysis on the crop nodding image to determine the crop maturity specifically comprises:
according to the determined crop types, invoking color information of each maturity, wherein the color information of each maturity comprises a plurality of maturity, and each maturity corresponds to a color feature;
determining the pixel point number of each color feature in the crop nodding image;
and determining the maturity corresponding to the color characteristic with the maximum pixel number as the crop maturity.
5. The unmanned aerial vehicle precise fertilization method based on artificial intelligence technology according to claim 1, wherein the step of determining the spraying fertilizer information and the spraying amount information according to the crop type and the crop maturity specifically comprises the following steps:
inputting crop types and crop maturity into a crop fertilizer warehouse, wherein the crop fertilizer warehouse comprises all crop types, each crop type corresponds to spraying fertilizer information, each crop type corresponds to a plurality of maturity, and each maturity corresponds to spraying amount information;
and outputting corresponding spraying fertilizer information and corresponding spraying quantity information.
6. Unmanned aerial vehicle accurate fertilization system based on artificial intelligence technique, its characterized in that, the system includes:
the camera angle adjusting module is used for receiving the spraying delay and the unmanned aerial vehicle flight height uploaded by the user, determining a shooting angle and adjusting the angle of the camera according to the shooting angle;
the crop type determining module is used for acquiring crop nodding images through the camera and carrying out shape analysis on the crop nodding images to determine crop types;
the crop maturity determining module is used for performing color analysis on the crop nodding image to determine crop maturity;
the fertilizer spraying information module is used for determining fertilizer spraying information and spraying amount information according to the crop types and the crop maturity;
and the fertilizer valve control module is used for controlling the opening and closing and flow of various fertilizer valves according to the spraying fertilizer information and the spraying quantity information, so that the unmanned aerial vehicle sprays corresponding fertilizer to fertilize.
7. The unmanned aerial vehicle precision fertilization system based on artificial intelligence technology of claim 6, wherein the camera angle adjustment module comprises:
the flying speed adjusting unit is used for adjusting the flying speed of the unmanned aerial vehicle;
a shooting angle calculation unit for calculating a shooting angle, tan shooting angle=unmanned aerial vehicle flight height ≡ (spraying delay×flight speed);
and the angle adjustment control unit is used for sending the shooting angle to the camera angle controller so that the camera can perform angle adjustment.
8. The unmanned aerial vehicle precision fertilization system based on artificial intelligence technology of claim 6, wherein the crop species determination module comprises:
the image amplifying and cutting unit is used for amplifying and cutting the crop nodding image to obtain a local crop image;
the edge sharpening processing unit is used for carrying out edge sharpening processing on the local crop image and enhancing the shape characteristics;
and the crop type determining unit is used for identifying the processed local crop image based on the convolutional neural network to obtain the crop type.
9. The unmanned aerial vehicle precision fertilization system based on artificial intelligence technology of claim 6, wherein the crop maturity determination module comprises:
the color information calling unit is used for calling color information of each maturity according to the determined crop variety, the color information of each maturity comprises a plurality of maturity, and each maturity corresponds to a color feature;
the pixel point number identification unit is used for determining the pixel point number of each color feature in the crop nodding image;
the maturity determining unit is used for determining that the maturity corresponding to the color feature with the largest pixel number is crop maturity.
10. The unmanned aerial vehicle precision fertilization system based on artificial intelligence technology of claim 6, wherein the spray fertilizer information module comprises:
the crop information input unit is used for inputting crop types and crop maturity into a crop fertilizer warehouse, the crop fertilizer warehouse comprises all crop types, each crop type corresponds to spraying fertilizer information, each crop type corresponds to a plurality of maturity, and each maturity corresponds to spraying amount information;
and the fertilizer information determining unit is used for outputting corresponding spraying fertilizer information and corresponding spraying quantity information.
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| CN117837366B (en) * | 2024-03-04 | 2024-06-07 | 湖南惠农科技有限公司 | Agricultural supervision platform based on agricultural Internet of things |
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