CN116806799A - Intelligent agricultural field weeding method and system - Google Patents
Intelligent agricultural field weeding method and system Download PDFInfo
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- CN116806799A CN116806799A CN202311079288.1A CN202311079288A CN116806799A CN 116806799 A CN116806799 A CN 116806799A CN 202311079288 A CN202311079288 A CN 202311079288A CN 116806799 A CN116806799 A CN 116806799A
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- 238000009333 weeding Methods 0.000 title claims abstract description 25
- 238000000034 method Methods 0.000 title claims abstract description 22
- 241000196324 Embryophyta Species 0.000 claims abstract description 109
- 239000004009 herbicide Substances 0.000 claims abstract description 45
- 230000002363 herbicidal effect Effects 0.000 claims abstract description 43
- 239000007921 spray Substances 0.000 claims abstract description 42
- 238000005507 spraying Methods 0.000 claims description 66
- 239000000575 pesticide Substances 0.000 claims description 54
- 239000003814 drug Substances 0.000 claims description 27
- 239000000126 substance Substances 0.000 claims description 14
- 230000005540 biological transmission Effects 0.000 claims description 4
- 239000000284 extract Substances 0.000 claims description 4
- 230000002401 inhibitory effect Effects 0.000 claims description 4
- 238000009792 diffusion process Methods 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 abstract description 2
- 229940079593 drug Drugs 0.000 description 4
- 241000607479 Yersinia pestis Species 0.000 description 2
- 238000012271 agricultural production Methods 0.000 description 2
- 201000010099 disease Diseases 0.000 description 2
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 241000238631 Hexapoda Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 235000015097 nutrients Nutrition 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01M—CATCHING, TRAPPING OR SCARING OF ANIMALS; APPARATUS FOR THE DESTRUCTION OF NOXIOUS ANIMALS OR NOXIOUS PLANTS
- A01M21/00—Apparatus for the destruction of unwanted vegetation, e.g. weeds
- A01M21/04—Apparatus for destruction by steam, chemicals, burning, or electricity
- A01M21/043—Apparatus for destruction by steam, chemicals, burning, or electricity by chemicals
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01M—CATCHING, TRAPPING OR SCARING OF ANIMALS; APPARATUS FOR THE DESTRUCTION OF NOXIOUS ANIMALS OR NOXIOUS PLANTS
- A01M7/00—Special adaptations or arrangements of liquid-spraying apparatus for purposes covered by this subclass
- A01M7/0025—Mechanical sprayers
- A01M7/0032—Pressure sprayers
- A01M7/0042—Field sprayers, e.g. self-propelled, drawn or tractor-mounted
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01M—CATCHING, TRAPPING OR SCARING OF ANIMALS; APPARATUS FOR THE DESTRUCTION OF NOXIOUS ANIMALS OR NOXIOUS PLANTS
- A01M7/00—Special adaptations or arrangements of liquid-spraying apparatus for purposes covered by this subclass
- A01M7/0089—Regulating or controlling systems
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64D—EQUIPMENT FOR FITTING IN OR TO AIRCRAFT; FLIGHT SUITS; PARACHUTES; ARRANGEMENTS OR MOUNTING OF POWER PLANTS OR PROPULSION TRANSMISSIONS IN AIRCRAFT
- B64D1/00—Dropping, ejecting, releasing, or receiving articles, liquids, or the like, in flight
- B64D1/16—Dropping or releasing powdered, liquid, or gaseous matter, e.g. for fire-fighting
- B64D1/18—Dropping or releasing powdered, liquid, or gaseous matter, e.g. for fire-fighting by spraying, e.g. insecticides
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/17—Terrestrial scenes taken from planes or by drones
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/60—Control of cameras or camera modules
- H04N23/695—Control of camera direction for changing a field of view, e.g. pan, tilt or based on tracking of objects
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- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Pest Control & Pesticides (AREA)
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Abstract
The application provides a method and a system for intelligent agricultural field weeding, comprising the following steps: step one: the unmanned aerial vehicle is controlled to fly above the farmland through the control terminal background of the unmanned aerial vehicle, the farmland range is measured through the high-definition camera, then farmland images are transmitted to the control terminal background of the unmanned aerial vehicle, and the background draws the unmanned aerial vehicle flight path according to the farmland images. According to the application, images in farmlands are collected through the high-definition camera, the types of weeds and crops are determined, the herbicide is configured according to the types of the weeds, the positions of the weeds are tracked through the high-definition camera, after the weeds are tracked by the high-definition camera, the unmanned aerial vehicle moves to the position right above the weeds to spray the herbicide, and when the sensor is contacted with the weeds, the spray heads sleeved with the conical covers spray the herbicide to prevent the herbicide from being contaminated on the crops, so that the normal production of the crops is ensured.
Description
Technical Field
The application relates to a method, in particular to an intelligent agricultural field weeding method and system, and belongs to the technical field of agricultural weeding.
Background
Smart agriculture is a smart economy in agriculture, and the form of smart economy is embodied in agriculture. Smart agriculture is an important component of smart economy; intelligent agriculture is a main component of intelligent economy, and in a broad sense, intelligent agriculture also comprises contents in aspects of agricultural electronic commerce, food tracing anti-counterfeiting, agricultural leisure tourism, agricultural information service and the like.
With the development of agriculture, the planting scale of crops is larger and larger. Weeds in farmlands are one of the main factors affecting crop yield. Weeds are the big enemy of agricultural production, the weeds can compete with crops for nutrients, moisture, sunlight and space, the yield and quality of the crops are reduced, and the occurrence and spread of diseases and insect pests can be promoted. In order to make the growth of crops faster, less disease, it is often necessary to regularly weed. The traditional method generally adopts a mode of directly spraying herbicide or manually weeding to kill pests, but the efficiency of manual weeding is lower, and the root of crops can be influenced in the weeding process, while the spraying herbicide can quickly weed, but the pesticide is unreasonably used, so that the pesticide damage accident is easy to occur, the yield of the crops is reduced, and the agricultural production is seriously influenced.
Disclosure of Invention
In view of the foregoing, the present application provides a method and system for intelligent agricultural field weeding to solve or alleviate the technical problems existing in the prior art, and at least provides a beneficial choice.
The technical scheme of the embodiment of the application is realized as follows: an intelligent agricultural field weeding method comprises the following steps:
step one: controlling the unmanned aerial vehicle to fly above the farmland through a control terminal background of the unmanned aerial vehicle, measuring the farmland range through a high-definition camera, then transmitting farmland images to the control terminal background of the unmanned aerial vehicle, and drawing a flight path of the unmanned aerial vehicle according to the farmland images by the background;
step two: the unmanned aerial vehicle carries the high-definition camera to fly along the flight path, the height of the unmanned aerial vehicle from crops is 1-2m when the unmanned aerial vehicle flies, and the flying speed of the unmanned aerial vehicle is 2-3m/s;
step three: collecting images of farmlands by a high-definition camera, and transmitting the collected images to a background in real time;
step four: the background screens and extracts crops and weeds in the pictures, compares and analyzes the extracted weed pictures with weed pictures in the cloud database, determines the types of the weeds, and judges the types of the crops;
step five: a herbicide for the weed is configured according to the type of the weed, and whether the herbicide contains pesticide components with inhibiting effect on crops is judged according to the type of the crops;
step six: when the herbicide does not affect crops, the unmanned aerial vehicle carries the pesticide spraying machine to fly along the initial flight track and spray the herbicide, the height of the spray head from the crops is 50-80cm, and the flight speed of the unmanned aerial vehicle is 0.5-1.5m/s;
step seven: when the herbicide affects crops, the unmanned aerial vehicle moves to the upper part of a farmland with the pesticide spraying machine, and the positions of weeds are positioned and tracked through the high-definition camera;
step eight: after the high-definition camera tracks weeds, the unmanned aerial vehicle carries the pesticide spraying machine to move to the position right above the weeds to spray pesticide at fixed points, and the pesticide spraying time is 1-2s;
step nine: after the fixed-point pesticide spraying is finished, after the spray head valve is closed for 2-3 seconds, the unmanned aerial vehicle drives the pesticide spraying machine to be far away from crops, and pesticide spraying is carried out on the next weeds;
step ten: and when the unmanned aerial vehicle sprays the medicine at fixed points, the unmanned aerial vehicle sends positioning to the background, and the background updates the medicine spraying point positions of the unmanned aerial vehicle in real time.
Further preferably, in the first step, the flight track of the unmanned aerial vehicle is in a shape of a Chinese character 'hui', and the background builds a three-dimensional model according to the image returned by the unmanned aerial vehicle.
Further preferably, in the second step, the high-definition camera carried by the unmanned aerial vehicle is a wide-angle camera.
Further preferably, in the third step, the unmanned aerial vehicle performs image acquisition on crops and weeds in a farmland, then performs real-time transmission on image data through a wireless network, and receives the image data through a background.
Further preferably, in the fourth step, the background builds a three-dimensional model again on the basis of the three-dimensional model of the farmland according to the crop image and the weed image, and positions the crop position and the weed position of the whole farmland.
Further preferably, in the fourth step, the kind of weeds is identified by extracting and comparing images of the weeds, and when the kind of weeds is greater than one kind, a pie chart is drawn for the ratio of each weed.
Further preferably, in the fifth step, when the kind of weeds is more than one and the required herbicide type is plural, a plurality of chemical tanks each corresponding to one type of weeds are provided in the chemical spraying machine.
Still preferably, in step six, a sensor is disposed at the bottom of the spray head, when spraying herbicide, the unmanned aerial vehicle drives the spray head of the pesticide spraying machine to move downwards, when the sensor contacts with weeds, the spray head starts spraying the pesticide, and a conical cover for preventing the pesticide from diffusing is disposed outside the spray head of the pesticide spraying machine.
Further preferably, in the eighth step, the positions of the weeds are tracked in a targeted manner according to the three-dimensional model in the fourth step, when the spray heads spray the pesticide, the high-definition camera performs image acquisition on the spraying positions of the spray heads and sends the images to the background, and whether the pesticide can be sprayed to the surfaces of crops is judged through the background.
Further preferably, in step ten, the background updates the three-dimensional model state in step four in real time according to the spraying point location.
The present application provides a smart agricultural field weeding system comprising the steps of applying said system to implement the smart agricultural field weeding method according to any one of claims 1 to 9.
By adopting the technical scheme, the embodiment of the application has the following advantages: according to the application, images in farmlands are collected through the high-definition camera, crops and weeds in pictures are screened and extracted through the background, the types of the weeds and the crops are determined, herbicides are configured according to the types of the weeds, the unmanned aerial vehicle carries the pesticide spraying machine to move to the upper part of the farmlands, the positions of the weeds are positioned and tracked through the high-definition camera, after the high-definition camera tracks the weeds, the unmanned aerial vehicle carries the pesticide spraying machine to move to the position right above the weeds to spray the pesticide at fixed points, and when the sensor is contacted with the weeds, the spray head sleeved with the conical cover sprays the weeds, so that the herbicides are prevented from being contaminated on the crops, and normal production of the crops is ensured.
The foregoing summary is for the purpose of the specification only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present application will become apparent by reference to the drawings and the following detailed description.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the steps of the present application.
Detailed Description
Hereinafter, only certain exemplary embodiments are briefly described. As will be recognized by those of skill in the pertinent art, the described embodiments may be modified in various different ways without departing from the spirit or scope of the present application. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive.
Embodiments of the present application will be described in detail below with reference to the accompanying drawings.
Example 1
As shown in fig. 1, the embodiment of the application provides a smart agriculture field weeding method, which comprises the following steps:
step one: controlling the unmanned aerial vehicle to fly above the farmland through a control terminal background of the unmanned aerial vehicle, measuring the farmland range through a high-definition camera, then transmitting farmland images to the control terminal background of the unmanned aerial vehicle, and drawing a flight path of the unmanned aerial vehicle according to the farmland images by the background;
step two: the unmanned aerial vehicle carries the high-definition camera to fly along the flight path, the height of the unmanned aerial vehicle from crops is 1m when the unmanned aerial vehicle flies, and the flying speed of the unmanned aerial vehicle is 3m/s;
step three: collecting images of farmlands by a high-definition camera, and transmitting the collected images to a background in real time;
step four: the background screens and extracts crops and weeds in the pictures, compares and analyzes the extracted weed pictures with weed pictures in the cloud database, determines the types of the weeds, and judges the types of the crops;
step five: a herbicide for the weed is configured according to the type of the weed, and whether the herbicide contains pesticide components with inhibiting effect on crops is judged according to the type of the crops;
step six: when the herbicide does not affect crops, the unmanned aerial vehicle carries the pesticide spraying machine to fly along the initial flight track and spray the herbicide, the height of the spray head from the crops is 50cm, and the flight speed of the unmanned aerial vehicle is 1.5m/s;
step seven: when the herbicide affects crops, the unmanned aerial vehicle moves to the upper part of a farmland with the pesticide spraying machine, and the positions of weeds are positioned and tracked through the high-definition camera;
step eight: after the high-definition camera tracks weeds, the unmanned aerial vehicle carries the pesticide spraying machine to move to the position right above the weeds to spray the pesticide at fixed points, and the pesticide spraying time is 2s;
step nine: after the fixed-point pesticide spraying is finished, the unmanned aerial vehicle drives the pesticide spraying machine to be far away from crops after the spray head valve is closed for 3 seconds, and pesticide spraying is carried out on the next weeds;
step ten: and when the unmanned aerial vehicle sprays the medicine at fixed points, the unmanned aerial vehicle sends positioning to the background, and the background updates the medicine spraying point positions of the unmanned aerial vehicle in real time.
In one embodiment, in the first step, the flight track of the unmanned aerial vehicle is in a shape of a Chinese character 'hui', the background builds a three-dimensional model according to the image returned by the unmanned aerial vehicle, and the high-definition camera carried by the unmanned aerial vehicle is a wide-angle camera, so that coverage type image acquisition can be carried out on farmlands.
In one embodiment, in the third step, the unmanned aerial vehicle performs image acquisition on crops and weeds in the farmland, then performs real-time transmission on image data through a wireless network, the background receives the image data, the background builds a three-dimensional model again on the basis of the three-dimensional model of the farmland according to the crop image and the weed image, positions the crops and the weeds in the whole farmland, and further can intuitively display the distribution states of the crops and the weeds in the farmland in the background.
In one embodiment, in the fourth step, the types of weeds are identified by extracting and comparing the images of the weeds, when the types of the weeds are more than one type, a pie chart is drawn for each weed ratio, and then different components of the herbicide can be configured according to the weed ratio in the pie chart.
In one embodiment, in the fifth step, when the types of weeds are more than one and the types of herbicide are multiple, a plurality of chemical tanks are arranged in the chemical spraying machine, the herbicide in each chemical tank corresponds to one type of weeds, and by arranging the plurality of chemical tanks, the chemical spraying can be performed on the weeds of multiple types at the same time.
In an embodiment, in step six, the bottom of shower nozzle sets up the sensor, and when spraying the herbicide, unmanned aerial vehicle drives the shower nozzle of spouting the medicine machine and moves downwards, and when sensor and weeds contact, the shower nozzle begins spouting the medicine, and the shower nozzle outside of spouting the medicine machine is provided with the toper cover that prevents the medicament diffusion, and then can realize the accurate spraying of herbicide through the sensor, through setting up the toper cover on the shower nozzle, can avoid the herbicide to be infected with on the crops.
In an embodiment, in the eighth step, the positions of the weeds are tracked in a targeted manner according to the three-dimensional model in the fourth step, when the spray heads spray the medicines, the high-definition camera performs image acquisition on the medicine spraying positions of the spray heads and sends the acquired images to the background, and whether the pesticides can be sprayed to the surfaces of crops or not is judged through the background, so that the spraying of the medicines can be monitored.
In one embodiment, in the step ten, the background updates the three-dimensional model state in the step four in real time according to the spraying point position, so that the situations of weedy spraying omission and repeated spraying can be avoided.
Example two
As shown in fig. 1, the embodiment of the application provides a smart agriculture field weeding method, which comprises the following steps:
step one: controlling the unmanned aerial vehicle to fly above the farmland through a control terminal background of the unmanned aerial vehicle, measuring the farmland range through a high-definition camera, then transmitting farmland images to the control terminal background of the unmanned aerial vehicle, and drawing a flight path of the unmanned aerial vehicle according to the farmland images by the background;
step two: the unmanned aerial vehicle carries the high-definition camera to fly along the flight path, the height of the unmanned aerial vehicle from crops is 2m when the unmanned aerial vehicle flies, and the flying speed of the unmanned aerial vehicle is 2m/s;
step three: collecting images of farmlands by a high-definition camera, and transmitting the collected images to a background in real time;
step four: the background screens and extracts crops and weeds in the pictures, compares and analyzes the extracted weed pictures with weed pictures in the cloud database, determines the types of the weeds, and judges the types of the crops;
step five: a herbicide for the weed is configured according to the type of the weed, and whether the herbicide contains pesticide components with inhibiting effect on crops is judged according to the type of the crops;
step six: when the herbicide does not affect crops, the unmanned aerial vehicle carries the pesticide spraying machine to fly along the initial flight track and spray the herbicide, the height of the spray head from the crops is 80cm, and the flight speed of the unmanned aerial vehicle is 0.5/s;
step seven: when the herbicide affects crops, the unmanned aerial vehicle moves to the upper part of a farmland with the pesticide spraying machine, and the positions of weeds are positioned and tracked through the high-definition camera;
step eight: after the high-definition camera tracks weeds, the unmanned aerial vehicle carries the pesticide spraying machine to move to the position right above the weeds to spray the pesticide at fixed points, and the pesticide spraying time is 1s;
step nine: after the fixed-point pesticide spraying is finished, the unmanned aerial vehicle drives the pesticide spraying machine to be far away from crops after the spray head valve is closed for 2 seconds, and pesticide spraying is carried out on the next weeds;
step ten: and when the unmanned aerial vehicle sprays the medicine at fixed points, the unmanned aerial vehicle sends positioning to the background, and the background updates the medicine spraying point positions of the unmanned aerial vehicle in real time.
In one embodiment, in the first step, the flight track of the unmanned aerial vehicle is in a shape of a Chinese character 'hui', the background builds a three-dimensional model according to the image returned by the unmanned aerial vehicle, and the high-definition camera carried by the unmanned aerial vehicle is a wide-angle camera, so that coverage type image acquisition can be carried out on farmlands.
In one embodiment, in the third step, the unmanned aerial vehicle performs image acquisition on crops and weeds in the farmland, then performs real-time transmission on image data through a wireless network, the background receives the image data, the background builds a three-dimensional model again on the basis of the three-dimensional model of the farmland according to the crop image and the weed image, positions the crops and the weeds in the whole farmland, and further can intuitively display the distribution states of the crops and the weeds in the farmland in the background.
In one embodiment, in the fourth step, the types of weeds are identified by extracting and comparing the images of the weeds, when the types of the weeds are more than one type, a pie chart is drawn for each weed ratio, and then different components of the herbicide can be configured according to the weed ratio in the pie chart.
In one embodiment, in the fifth step, when the types of weeds are more than one and the types of herbicide are multiple, a plurality of chemical tanks are arranged in the chemical spraying machine, the herbicide in each chemical tank corresponds to one type of weeds, and by arranging the plurality of chemical tanks, the chemical spraying can be performed on the weeds of multiple types at the same time.
In an embodiment, in step six, the bottom of shower nozzle sets up the sensor, and when spraying the herbicide, unmanned aerial vehicle drives the shower nozzle of spouting the medicine machine and moves downwards, and when sensor and weeds contact, the shower nozzle begins spouting the medicine, and the shower nozzle outside of spouting the medicine machine is provided with the toper cover that prevents the medicament diffusion, and then can realize the accurate spraying of herbicide through the sensor, through setting up the toper cover on the shower nozzle, can avoid the herbicide to be infected with on the crops.
In an embodiment, in the eighth step, the positions of the weeds are tracked in a targeted manner according to the three-dimensional model in the fourth step, when the spray heads spray the medicines, the high-definition camera performs image acquisition on the medicine spraying positions of the spray heads and sends the acquired images to the background, and whether the pesticides can be sprayed to the surfaces of crops or not is judged through the background, so that the spraying of the medicines can be monitored.
In one embodiment, in the step ten, the background updates the three-dimensional model state in the step four in real time according to the spraying point position, so that the situations of weedy spraying omission and repeated spraying can be avoided.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that various changes and substitutions are possible within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. An intelligent agricultural field weeding method is characterized by comprising the following steps:
step one: controlling the unmanned aerial vehicle to fly above the farmland through a control terminal background of the unmanned aerial vehicle, measuring the farmland range through a high-definition camera, then transmitting farmland images to the control terminal background of the unmanned aerial vehicle, and drawing a flight path of the unmanned aerial vehicle according to the farmland images by the background;
step two: the unmanned aerial vehicle carries the high-definition camera to fly along the flight path, the height of the unmanned aerial vehicle from crops is 1-2m when the unmanned aerial vehicle flies, and the flying speed of the unmanned aerial vehicle is 2-3m/s;
step three: collecting images of farmlands by a high-definition camera, and transmitting the collected images to a background in real time;
step four: the background screens and extracts crops and weeds in the pictures, compares and analyzes the extracted weed pictures with weed pictures in the cloud database, determines the types of the weeds, and judges the types of the crops;
step five: a herbicide for the weed is configured according to the type of the weed, and whether the herbicide contains pesticide components with inhibiting effect on crops is judged according to the type of the crops;
step six: when the herbicide does not affect crops, the unmanned aerial vehicle carries the pesticide spraying machine to fly along the initial flight track and spray the herbicide, the height of the spray head from the crops is 50-80cm, and the flight speed of the unmanned aerial vehicle is 0.5-1.5m/s;
step seven: when the herbicide affects crops, the unmanned aerial vehicle moves to the upper part of a farmland with the pesticide spraying machine, and the positions of weeds are positioned and tracked through the high-definition camera;
step eight: after the high-definition camera tracks weeds, the unmanned aerial vehicle carries the pesticide spraying machine to move to the position right above the weeds to spray pesticide at fixed points, and the pesticide spraying time is 1-2s;
step nine: after the fixed-point pesticide spraying is finished, after the spray head valve is closed for 2-3 seconds, the unmanned aerial vehicle drives the pesticide spraying machine to be far away from crops, and pesticide spraying is carried out on the next weeds;
step ten: and (3) sending positioning to the background when the unmanned aerial vehicle sprays the medicine at fixed points, and updating the medicine spraying point positions of the unmanned aerial vehicle in real time by the background, wherein the background updates the three-dimensional model state in the step four in real time according to the medicine spraying point positions.
2. A method of intelligent agriculture field weeding according to claim 1, wherein: in the first step, the flight track of the unmanned aerial vehicle is in a shape of a Chinese character 'hui', and the background builds a three-dimensional model according to the image returned by the unmanned aerial vehicle.
3. A method of intelligent agriculture field weeding according to claim 1, wherein: in the second step, the high-definition camera carried by the unmanned aerial vehicle is a wide-angle camera.
4. A method of intelligent agriculture field weeding according to claim 1, wherein: in the third step, the unmanned aerial vehicle performs image acquisition on crops and weeds in farmlands, then performs real-time transmission on image data through a wireless network, and the background receives the image data.
5. A method of intelligent agriculture field weeding according to claim 1, wherein: in the fourth step, the background builds a three-dimensional model again on the basis of the three-dimensional model of the farmland according to the crop images and the weed images, and positions the crop positions and the weed positions of the whole farmland.
6. A method of intelligent agriculture field weeding according to claim 5, wherein: in step four, the types of weeds are identified by extracting and comparing the images of the weeds, and when the types of weeds are more than one, a pie chart is drawn for the ratio of each weed.
7. A method of intelligent agriculture field weeding according to claim 1, wherein: in the fifth step, when the types of weeds are more than one and the types of herbicide are multiple, a plurality of chemical boxes are arranged in the pesticide spraying machine, and the herbicide in each chemical box corresponds to one type of weeds.
8. A method of intelligent agriculture field weeding according to claim 1, wherein: in step six, the bottom of shower nozzle sets up the sensor, and when spraying the herbicide, unmanned aerial vehicle drives the shower nozzle of spouting the medicine machine and moves downwards, and when sensor and weeds contact, the shower nozzle begins to spout the medicine, the shower nozzle outside of spouting the medicine machine is provided with the toper cover that prevents the medicament diffusion.
9. A method of intelligent agriculture field weeding according to claim 1, wherein: in the eighth step, the positions of weeds are tracked in a targeted mode according to the three-dimensional model in the fourth step, when the spray heads spray the pesticides, the high-definition camera performs image acquisition on the pesticide spraying positions of the spray heads and sends the pesticide spraying positions to the background, and whether pesticides can be sprayed to the surfaces of crops is judged through the background.
10. An wisdom agricultural field weeding system, its characterized in that: comprising the step of applying said system to the implementation of the smart agriculture field weeding method according to any one of claims 1 to 9.
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| CN202311079288.1A CN116806799A (en) | 2023-08-25 | 2023-08-25 | Intelligent agricultural field weeding method and system |
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Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103299974A (en) * | 2012-03-12 | 2013-09-18 | 雅马托农磁株式会社 | Rice transplanter carried liquid spreading device and method adopting device for simultaneously carrying out rice transplanting and liquid spreading |
| CN104186451A (en) * | 2014-08-19 | 2014-12-10 | 西北农林科技大学 | Insect-killing weeding pesticide spraying robot based on machine vision |
| CN109258601A (en) * | 2018-08-10 | 2019-01-25 | 华南农业大学 | A kind of intelligentized weeding unmanned plane targeting/spraying volume control device and method |
| CN113167631A (en) * | 2020-05-28 | 2021-07-23 | 深圳市大疆创新科技有限公司 | Liquid level meter, measuring method thereof, spraying assembly and unmanned aerial vehicle |
| CN214229558U (en) * | 2020-12-30 | 2021-09-21 | 深圳市金品多科技有限公司 | High-efficient intelligent electric shock mosquito killer lamp |
| WO2021196062A1 (en) * | 2020-04-01 | 2021-10-07 | 唐山哈船科技有限公司 | Agricultural pest-killing device and method based on unmanned aerial vehicle |
| CN113647281A (en) * | 2021-07-22 | 2021-11-16 | 盘锦光合蟹业有限公司 | Weeding method and system |
| DE202021106121U1 (en) * | 2021-11-09 | 2021-12-14 | Sayed Sayeed Ahmad | Biomimetically designed smart drone for weed removal on agricultural crops using artificial intelligence and deep learning |
| CN113811184A (en) * | 2019-02-28 | 2021-12-17 | 精密人工智能股份有限公司 | System and method for field treatment and monitoring |
| CN114615885A (en) * | 2020-11-09 | 2022-06-10 | 深圳市大疆创新科技有限公司 | Spraying operation method and device, unmanned aerial vehicle and storage medium |
| US20220254155A1 (en) * | 2019-05-20 | 2022-08-11 | Basf Agro Trademarks Gmbh | Method for plantation treatment based on image recognition |
| CN115669398A (en) * | 2022-11-28 | 2023-02-03 | 深圳市纬尔科技有限公司 | Cotton topping actuating mechanism |
| CN115690629A (en) * | 2022-11-03 | 2023-02-03 | 陕西科技大学 | Agricultural unmanned aerial vehicle weeding method based on visual detection algorithm |
| CN218681465U (en) * | 2022-08-29 | 2023-03-24 | 北京大学现代农业研究院 | Weeding robot |
| EP4230036A1 (en) * | 2022-02-18 | 2023-08-23 | BASF Agro Trademarks GmbH | Targeted treatment of specific weed species with multiple treatment devices |
-
2023
- 2023-08-25 CN CN202311079288.1A patent/CN116806799A/en active Pending
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103299974A (en) * | 2012-03-12 | 2013-09-18 | 雅马托农磁株式会社 | Rice transplanter carried liquid spreading device and method adopting device for simultaneously carrying out rice transplanting and liquid spreading |
| CN104186451A (en) * | 2014-08-19 | 2014-12-10 | 西北农林科技大学 | Insect-killing weeding pesticide spraying robot based on machine vision |
| CN109258601A (en) * | 2018-08-10 | 2019-01-25 | 华南农业大学 | A kind of intelligentized weeding unmanned plane targeting/spraying volume control device and method |
| CN113811184A (en) * | 2019-02-28 | 2021-12-17 | 精密人工智能股份有限公司 | System and method for field treatment and monitoring |
| US20220254155A1 (en) * | 2019-05-20 | 2022-08-11 | Basf Agro Trademarks Gmbh | Method for plantation treatment based on image recognition |
| WO2021196062A1 (en) * | 2020-04-01 | 2021-10-07 | 唐山哈船科技有限公司 | Agricultural pest-killing device and method based on unmanned aerial vehicle |
| CN113167631A (en) * | 2020-05-28 | 2021-07-23 | 深圳市大疆创新科技有限公司 | Liquid level meter, measuring method thereof, spraying assembly and unmanned aerial vehicle |
| CN114615885A (en) * | 2020-11-09 | 2022-06-10 | 深圳市大疆创新科技有限公司 | Spraying operation method and device, unmanned aerial vehicle and storage medium |
| CN214229558U (en) * | 2020-12-30 | 2021-09-21 | 深圳市金品多科技有限公司 | High-efficient intelligent electric shock mosquito killer lamp |
| CN113647281A (en) * | 2021-07-22 | 2021-11-16 | 盘锦光合蟹业有限公司 | Weeding method and system |
| DE202021106121U1 (en) * | 2021-11-09 | 2021-12-14 | Sayed Sayeed Ahmad | Biomimetically designed smart drone for weed removal on agricultural crops using artificial intelligence and deep learning |
| EP4230036A1 (en) * | 2022-02-18 | 2023-08-23 | BASF Agro Trademarks GmbH | Targeted treatment of specific weed species with multiple treatment devices |
| CN218681465U (en) * | 2022-08-29 | 2023-03-24 | 北京大学现代农业研究院 | Weeding robot |
| CN115690629A (en) * | 2022-11-03 | 2023-02-03 | 陕西科技大学 | Agricultural unmanned aerial vehicle weeding method based on visual detection algorithm |
| CN115669398A (en) * | 2022-11-28 | 2023-02-03 | 深圳市纬尔科技有限公司 | Cotton topping actuating mechanism |
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