CN106585992A - Method and system for intelligent identification and accurate pesticide spraying using unmanned aerial vehicles - Google Patents
Method and system for intelligent identification and accurate pesticide spraying using unmanned aerial vehicles Download PDFInfo
- Publication number
- CN106585992A CN106585992A CN201611163877.8A CN201611163877A CN106585992A CN 106585992 A CN106585992 A CN 106585992A CN 201611163877 A CN201611163877 A CN 201611163877A CN 106585992 A CN106585992 A CN 106585992A
- Authority
- CN
- China
- Prior art keywords
- subinterval
- pest
- collection
- crops
- control
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- 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
Abstract
The invention relates to a method and system for intelligent identification and accurate pesticides spraying using unmanned aerial vehicles. The method comprises the following steps: dividing a crop area into a plurality of subregions; controlling an unmanned surveillance aerial vehicle to acquire crop images in each subregion and feeding the images to a control center; automatically comparing the acquired crop images with prestored images of crops subjected to pest and disease damages to identify the presence of pest and disease damages in each subregion, and if pest and disease damages exist, controlling an unmanned spraying aerial vehicle to accurately spray corresponding proportions or corresponding types of pesticides to the corresponding subregions. For the method, pesticides are sprayed only to subregions with crops subjected to pest and disease damages instead of large area pesticide spraying in a full range, thus reducing the waste of pesticides and protecting ecological environment; in addition, corresponding proportions or corresponding types of pesticides are sprayed according to specific circumstances of pest and disease damages, the same ratio of pesticides is not adopted for different pest and disease damages that the crops are subjected to, so that effective spraying treatment effect can be achieved for the crops subjected to different pest and disease damages.
Description
Technical field
The present invention relates to crop technical field, and in particular to a kind of UAV Intelligent identification, the side for accurately spraying insecticide
Method and system.
Background technology
With development in science and technology, traditional Agriculture Production Modes are progressively substituted by mechanization, informationalized mode, using nothing
It is man-machine to carry out low latitude operation, with using flexibly, small volume, low cost the features such as, can significantly lift the flight of agricultural vegetation
The efficiency and precision of operation.At present the unmanned light-small aircraft industry of country's agricultural use is in research and rank tentatively on probation
Section, China's agricultural aviation technology is totally also in the starting stage.
Unmanned plane can agriculturally provide in theory the details of three aspects.First, crops are checked from the air,
Many problems such as irrigation, soil variation, the pest and disease damage that even naked eyes cannot find and bacteria attack can be disclosed.Next, nobody
The aerocamera that machine is carried can provide multiwave image, capture multispectral data, and these information integrations together, can
To provide a detailed crops detail view, the difference between the crops of health and the crops that growing way is bad is indicated,
And these difference cannot be found at all using naked eyes.Finally, unmanned plane even can be carried out per hour weekly, daily to crops
Make an inspection tour, the image of shooting can show the growth change situation of crops, it is indicated that the region for going wrong, consequently facilitating preferably
Carry out field management.
Used as large agricultural country, there are 1,800,000,000 mu of basic farmlands, but the damaging influence that insect pest is caused to agricultural production in China, sternly
The grain-production safety of China is constrained again, therefore to realize agricultural sustainable development, must carry out control of agricultural pest.
It is one of important measures of the prevention and control of plant diseases, pest control to spray insecticide, and it is also the most tired work in field to spray insecticide.
Pesticide spraying is carried out using unmanned plane and gradually obtains increasing concern, but entered in existing unmanned aerial vehicle
When row large area agricultural chemicals sprays, the region of crop growth problem is often pointed out, then carry out large area agricultural chemicals spray, easily
Generate the problem that：1) crops without morbid state or no problem are still sprayed agricultural chemicals, cause agricultural chemicals to waste, pollute environment；2) by
The problem presence existed in crops varies, and using the same proportioning of same agricultural chemicals, with unmanned plane agricultural chemicals spray is carried out
Shi Buneng reaches good effect of spraying pesticide.
The content of the invention
Spray insecticide for unmanned plane large area the problem of generation, the application provides a kind of UAV Intelligent identification, accurate
The method and system sprayed insecticide.
According in a first aspect, a kind of method for UAV Intelligent identification being provided in a kind of embodiment, accurately being sprayed insecticide, bag
Include step：
In advance crop area is divided into into multiple collection information and sprays subinterval respectively；
Control Drones for surveillance's collection needs the crop map picture in the Nei Ge subintervals of region, and feeds back in control centre；
The crop map picture of collection is compared automatically with the pest and disease damage crop map picture for prestoring, to differentiate each subinterval
With the presence or absence of pest and disease damage, if existing, corresponding proportioning or respective classes are precisely sprayed in control sprinkling unmanned plane to correspondence subinterval
Agricultural chemicals.
In a kind of embodiment, control Drones for surveillance and gather the crop map picture needed in the Nei Ge subintervals of region, including
Step：Control Drones for surveillance is in the subinterval by the crop map picture in default collection route collection subinterval.
In a kind of embodiment, it is many that the default a few font routes or subinterval for gathering route to set in subinterval are separated
Individual collection is interval.
In a kind of embodiment, Drones for surveillance is controlled by the diseases and pests of agronomic crop letter in default collection route collection subinterval
While breath, also including step：
Control Drones for surveillance is gathered root image, the neck image of the crops in subinterval by default collection route
Or/and leaf portion image.
In a kind of embodiment, the crop disease image of collection is compared with the pest and disease damage crop map picture for prestoring, is known
Not each subinterval whether there is pest and disease damage, including step：
Obtain the image of the crops of Drones for surveillance's collection；
The position of the affiliated crops of identification described image；
The image of identification is compared automatically with the image of the corresponding classification for prestoring, judge the image that recognizes whether with
The pest and disease damage image for prestoring is same or similar, if same or similar, the image crops for recognizing are pest and disease damage crops.
In a kind of embodiment, control sprinkling unmanned plane precisely sprays the agriculture of corresponding proportioning or respective classes to correspondence subinterval
Medicine, including step：
The subinterval that the Pest management sprinkling unmanned plane of root, neck or/and leaf portion according to crops is located to it
Select the agricultural chemicals of the corresponding proportioning of sprinkling or respective classes.
In a kind of embodiment, control sprinkling unmanned plane precisely sprays the agriculture of corresponding proportioning or respective classes to correspondence subinterval
Medicine, including step：
The acquisition zone that the Pest management sprinkling unmanned plane of root, neck or/and leaf portion according to crops is located to it
Between select the agricultural chemicals of the corresponding proportioning of sprinkling or respective classes.
In a kind of embodiment, control sprinkling unmanned plane selects corresponding proportioning or the agricultural chemicals of respective classes to be precisely sprayed to sub-district
Between or collection interval in have the crops of pest and disease damage.
According to second aspect, the system for a kind of UAV Intelligent identification is provided in a kind of embodiment, accurately spraying insecticide, bag
Include control centre, Drones for surveillance and sprinkling unmanned plane；
Drones for surveillance and sprinkling unmanned plane are connected respectively with control centre wireless signal；
The crop area for prestoring is divided into multiple subintervals, control centre's control Drones for surveillance's collection by control centre
Crop map picture in subinterval, and by crops image feedback in control centre；
Control centre is additionally operable to automatically be compared the crop map picture of collection with the pest and disease damage crop map picture for prestoring,
Each subinterval is recognized with the presence or absence of pest and disease damage, if existing, control sprinkling unmanned plane to correspondence subinterval spray corresponding proportioning or
The agricultural chemicals of respective classes.
In a kind of embodiment, Drones for surveillance is multi-rotor unmanned aerial vehicle.
In a kind of embodiment, control centre includes that the default unit of collection route, Database Unit, recognition unit and control are single
Unit；
The default unit of collection route is used to preset collection route of the Drones for surveillance in the subinterval；
Database Unit is used to prestore the crop map picture of pest and disease damage；
Control unit is used to control Drones for surveillance in subinterval by collection route collection subinterval set in advance
Crop map picture；
It seems the no storage with Database Unit that recognition unit is used for the crop map of automatic identification Drones for surveillance collection
Pest and disease damage crop map is as same or similar；
Control unit is additionally operable to control sprinkling unmanned plane and sprays proportioning corresponding to pest and disease damage or phase to its subinterval being located
Answer the agricultural chemicals of classification.
In a kind of embodiment, it is multiple adopting of being separated of a few font routes of setting or subinterval in subinterval to gather route
Collection is interval.
In a kind of embodiment, status information is image information, and Drones for surveillance includes that image capture device, data transmit-receive set
Standby and first processor；
Image capture device is connected with data transmitting/receiving equipment, and image capture device is used to gather crop map picture, and by agriculture
Crop map picture is sent to control centre by data transmitting/receiving equipment；
The output end of first processor is connected with control unit signal, and first processor is controlled according to the instruction of control unit
Drones for surveillance gathers information.
In a kind of embodiment, sprinkling unmanned plane is provided with second processing device, pesticide sprayer and at least two different ratios
The agricultural chemicals placer of agricultural chemicals or two kinds of different classes of agricultural chemicals；
Agricultural chemicals placer is connected with pesticide sprayer, and pesticide sprayer is connected with second processing device, second processing device with control
Cell signal connection processed；
Second processing device selects the corresponding agricultural chemicals of connection by the instruction control pesticide sprayer of spraying insecticide of control unit
Placer sprays the agricultural chemicals of corresponding proportioning or respective classes.
In a kind of embodiment, at least two medicine-chests are provided with agricultural chemicals placer.
According to the UAV Intelligent identification of above-described embodiment, the method accurately sprayed insecticide, due to first by crop area
Multiple subintervals are divided into, Drones for surveillance are then controlled and is gathered the crop map picture needed in the Nei Ge subintervals of region, will be adopted
The crop map picture of collection is compared automatically with the pest and disease damage crop map picture for prestoring, and recognizes that each subinterval whether there is disease pest
Evil, if existing, control sprinkling unmanned plane sprays the agricultural chemicals of corresponding proportioning or respective classes to correspondence subinterval, only for disease pest
The interval of evil crops is sprayed insecticide, rather than large area is sprayed insecticide, and reduces the waste of agricultural chemicals, protects ecological environment,
In addition, be the agricultural chemicals that corresponding proportioning is sprayed according to specific pest and disease damage, rather than the pest and disease damage of different crops is using same
Plant the agricultural chemicals of proportioning so that the crops of different pest and disease damages are attained by the therapeutic effect that effectively sprays.
Description of the drawings
Fig. 1 is the method flow diagram that UAV Intelligent is recognized, accurately sprayed insecticide；
Fig. 2 is the systematic schematic diagram that UAV Intelligent is recognized, accurately sprayed insecticide.
Specific embodiment
Accompanying drawing is combined below by specific embodiment to be described in further detail the present invention.
Embodiment one：
A kind of method that this example provides UAV Intelligent identification, accurately sprays insecticide, its flow chart is as shown in figure 1, concrete
Comprise the steps.
S100：In advance crop area is divided into into multiple collection information and sprays subinterval respectively.
For the purpose that large-area crops are realized accurately spraying insecticide, needs are first divided into large-area crops
Several collection information with spray subinterval respectively, then, then be managed for the crops in each subinterval.
S200：Control Drones for surveillance's collection needs the crop map picture in the Nei Ge subintervals of region, and feeds back in control
Center.
Have after crops are sprayed insecticide, the agricultural chemicals is sprayed without being further continued in a period of time, i.e., should also without collection
Crop map picture in region, so, control Drones for surveillance and gather the crop map picture needed in the Nei Ge subintervals of region,
Such as, the region of agricultural chemicals is not sprayed in a period of time as needs the region of collection.This step is specifically included：To reach accurate prison
The crops in each subinterval are controlled, this example is each subinterval setting collection route, and e.g., collection route can be a few font roads
The little collection of line, or one is interval, i.e., each subinterval is divided into several collection intervals again, and then, control is detectd
Unmanned plane is looked in subinterval by the crops status information in default collection route collection subinterval, the status information this example is excellent
Elect crop map picture as.
In a kind of embodiment, the farming image that Drones for surveillance gathers in each subinterval according to a few font routes is controlled,
Such as, control Drones for surveillance to fly in each subinterval overhead according to a few font routes so that Drones for surveillance can gather son
The leaf portion image of the crops in interval, due to the diseases and pests of agronomic crop situation difference that the different parts of crops are reflected,
So, control Drones for surveillance's horizontal flight is while each subinterval top, in addition it is also necessary to controls Drones for surveillance and flies downwards
Capable height, in order to root image and/or stem's image that Drones for surveillance can also gather the crops in subinterval.
In another kind of embodiment, control Drones for surveillance according to several collections separated in each subinterval it is interval by
Crop map picture in each collection interval of individual collection, e.g., the interval size of the collection can be 30cm*30cm, control
Drones for surveillance flies in the interval overhead of the collection, gathers the leaf portion image of the crops in the collection interval, likewise,
Root image and/or stem's image that Drones for surveillance gathers crops in collection interval can be controlled.
Drones for surveillance is gathered after the crop map picture in subinterval, or the crop map picture in collection interval, Drones for surveillance
In time by these crops image feedbacks in control centre, to carry out Data Comparison and analysis to the crop map picture for gathering,
And crops are correspondingly processed according to analysis result.
S300：The crop map picture of collection is compared automatically with the pest and disease damage crop map picture for prestoring, each son is recognized
Interval whether there is pest and disease damage, if existing, corresponding proportioning or corresponding is precisely sprayed in control sprinkling unmanned plane to correspondence subinterval
The agricultural chemicals of classification.
This example gathers the agriculture that crops of the image of crops to there is pest and disease damage spray corresponding proportioning by Drones for surveillance
Medicine, wherein, each subinterval of automatic identification comprises the concrete steps that with the presence or absence of pest and disease damage：Obtain the crops of Drones for surveillance's collection
Image；The position of the affiliated crops of automatic identification image, e.g., by existing image recognition technology, can quickly recognize
The image for going out Drones for surveillance's collection belongs to which concrete position of crops.
This example diseases and pests of agronomic crop it may is that the various situations such as corps leaf surface yellowish, roots blacking,
The various pest and disease damage sample image of crops is stored in the database of control centre in advance, and, the pest and disease damage sample graph
Seem to sort out storage, e.g., the sample image of all kinds of pest and disease damages of crop root part is stored together, all kinds of disease pests of crops stem
Harmful sample image is stored together, so, the generic image that will be stored in the image and database of the crops of identification
Image in storehouse is contrasted one by one, you can quickly judge whether the image for recognizing is same or similar with the pest and disease damage image for prestoring, if
Same or similar, then the image crops for recognizing are pest and disease damage crops.
After the crops for determining collection have pest and disease damage, then control sprinkling unmanned plane is to the crops institute for having pest and disease damage
Subinterval spray the agricultural chemicals of corresponding proportioning, specifically, the root image, neck image or/and leaf portion figure according to crops
The agricultural chemicals that unmanned plane selects the corresponding proportioning of sprinkling or respective classes to its subinterval being located is sprayed in the Pest management of picture, at it
Be further more accurately to realize spraying insecticide in his embodiment, then root image according to crops, neck image or/and
The corresponding proportioning of collection interval selection sprinkling that unmanned plane is located to it or respective classes are sprayed in the Pest management of leaf portion image
Agricultural chemicals.
In other embodiments, can also control to spray unmanned plane and select the pesticide spraying of corresponding proportioning or respective classes extremely
The crops of pest and disease damage in subinterval or collection interval, i.e. the crops only to there is pest and disease damage are sprayed insecticide, due to there is pest and disease damage
Crops it is interval may be discontinuous, this example is capable of achieving discretely to spray insecticide.
By the method for this example, the different choosings of the seriousness of the same pest and disease damage of the crops in each subinterval can be directed to
The agricultural chemicals for spraying corresponding proportioning is selected, and, the agricultural chemicals of sprinkling respective classes is selected for the different pest and disease damages of crops, reach and be directed to
Large Area of Crops realizes the purpose of localized pulverization agricultural chemicals, and reaches the purpose that different pest and disease damages spray different type agricultural chemicals.
Embodiment two：
Based on embodiment one, the system that this example provides a kind of UAV Intelligent identification, accurately sprays insecticide, its schematic diagram is such as
Shown in Fig. 2, including：Control centre 1, Drones for surveillance 2 and sprinkling unmanned plane 3, Drones for surveillance 2 and sprinkling unmanned plane 3 are distinguished
It is connected with control centre 1 wireless signal.
The crop area for prestoring is divided into multiple subintervals, the control Drones for surveillance 2 of control centre 1 by control centre 1
The crop map picture in each subinterval is gathered, Drones for surveillance 2 again feeds back crop map picture in control centre 1；Control centre 1
It is additionally operable to automatically be compared the crop map picture of collection with the pest and disease damage crop map picture or pest and disease damage data that prestore, recognizes
The subinterval of collection whether there is pest and disease damage, if existing, control sprinkling unmanned plane 3 to correspondence subinterval spray corresponding proportioning or
The agricultural chemicals of respective classes.
Further, control centre 1 includes：The default unit 11 of collection route, Database Unit 12, recognition unit 13 and control
Unit 14；Wherein, gathering the default unit 11 of route is used to preset collection route of the Drones for surveillance 2 in subinterval；Number
It is used to prestore pest and disease damage crop map picture, the sample of the various pest and disease damages of the crops particularly sorted out according to library unit 12
This image；Control unit 14 is used to control Drones for surveillance 2 in subinterval by collection route collection subinterval set in advance
Interior crop map picture；It seems no and database that recognition unit 13 is used for the crop map of the collection of automatic identification Drones for surveillance 1
The pest and disease damage image or pest and disease damage data of unit storage is same or similar；Control unit 14 is additionally operable to according to the farming for having pest and disease damage
Thing control sprinkling unmanned plane 3 sprays the agricultural chemicals to abnormal corresponding proportioning or respective classes to its subinterval being located.
The control unit 14 of this example controls Drones for surveillance 2 in subinterval by collection route collection sub-district set in advance
The course of work of interior crop map picture, S200 the step of specifically refer to embodiment one, the recognition unit 13 of this example is recognized
The crop map of the collection of Drones for surveillance 1 seems the no pest and disease damage crop map stored with Database Unit as same or similar,
And control unit 14 is corresponding to abnormal to the subinterval sprinkling that it is located according to the crops control sprinkling unmanned plane 3 for having pest and disease damage
The course of work of the agricultural chemicals of proportioning or respective classes, S300 the step of specifically refer to embodiment one, this example is not repeated.
Further, Drones for surveillance 2 includes image capture device 21, data transmitting/receiving equipment 22 and first processor 23；Figure
As collecting device 21 is connected with data transmitting/receiving equipment 22, image capture device 21 is used to gather crop map picture, and by crops
Image is sent to control centre 1 by data transmitting/receiving equipment 22, and output end and the signal of control unit 14 of first processor 23 connect
Connect, first processor 23 controls the collection information of Drones for surveillance 2 according to the control signal of control unit 14；Wherein, IMAQ
Equipment 21 is high-definition camera.
Further, spray unmanned plane 3 and be provided with second processing device 31, the different ratio agriculture of pesticide sprayer 32 and at least two
The agricultural chemicals placer 33 of medicine or two kinds of different classes of agricultural chemicals, wherein, at least two medicine-chests are provided with agricultural chemicals placer 33, it is different
Medicine-chest is used to store different ratio or different classes of agricultural chemicals；Agricultural chemicals placer 33 is connected with pesticide sprayer 32, pesticide spraying
Device 32 is connected with second processing device 31, and second processing device 31 is connected with the wireless signal of control unit 14；Second processing device 31 passes through
The corresponding agricultural chemicals placer 33 of the instruction control selection connection of pesticide sprayer 32 of spraying insecticide of control unit 14 sprays accordingly matches somebody with somebody
Than or respective classes agricultural chemicals.
By the method for this example, the different choosings of the seriousness of the same pest and disease damage of the crops in each subinterval can be directed to
The agricultural chemicals for spraying corresponding proportioning is selected, and, the agricultural chemicals of sprinkling respective classes is selected for the different pest and disease damages of crops, reach and be directed to
Large Area of Crops realizes the purpose of localized pulverization agricultural chemicals, and reaches the purpose that different pest and disease damages spray different type agricultural chemicals, real
Existing Intelligent Recognition, the purpose of accurate sprinkling.
Use above specific case is illustrated to the present invention, is only intended to help and understands the present invention, not to limit
The system present invention.For those skilled in the art, according to the thought of the present invention, can also make some simple
Deduce, deform or replace.
Claims (15)
1. a kind of method that UAV Intelligent is recognized, accurately sprayed insecticide, it is characterised in that including step：
In advance crop area is divided into into multiple collection information and sprays subinterval respectively；
Control Drones for surveillance's collection needs region Nei Ge subintervals crop map picture, and by the crops image feedback in control
Center processed；
The crop map picture of collection is compared automatically with the pest and disease damage crop map picture for prestoring, to differentiate each subinterval
With the presence or absence of pest and disease damage, if existing, corresponding proportioning or respective classes are precisely sprayed in control sprinkling unmanned plane to correspondence subinterval
Agricultural chemicals.
2. the method for claim 1, it is characterised in that the control Drones for surveillance collection needs each sub-district in region
Interior crop map picture, including step：The Drones for surveillance is controlled in the subinterval by default collection route collection
Crop map picture in the subinterval.
3. method as claimed in claim 2, it is characterised in that the default collection route is the several of the setting subinterval in
Multiple collections that font route or the subinterval are separated are interval.
4. method as claimed in claim 2, it is characterised in that the control Drones for surveillance is by default collection route collection institute
State crop map in subinterval as while, also including step：
Control root image, stem that the Drones for surveillance is gathered the crops in the subinterval by the default collection route
Portion's image or/and leaf portion image.
5. method as claimed in claim 4, it is characterised in that enter the crop map picture of collection with the crop map picture for prestoring
Row compares, and each subinterval whether there is pest and disease damage, including step：
Obtain the image of the crops of Drones for surveillance's collection；
The position of the affiliated crops of identification described image；
The image of identification is compared automatically with the image of the corresponding classification for prestoring, judge the image for recognizing whether with prestore
Pest and disease damage image it is same or similar, if same or similar, recognize image crops be pest and disease damage crops.
6. method as claimed in claim 5, it is characterised in that it is corresponding to the sprinkling of correspondence subinterval that unmanned plane is sprayed in the control
The agricultural chemicals of proportioning or respective classes, including step：
The subinterval that the Pest management sprinkling unmanned plane of root, neck or/and leaf portion according to the crops is located to it
Select the agricultural chemicals of the corresponding proportioning of sprinkling or respective classes.
7. method as claimed in claim 5, it is characterised in that it is corresponding to the sprinkling of correspondence subinterval that unmanned plane is sprayed in the control
The agricultural chemicals of proportioning or respective classes, including step：
The acquisition zone that the Pest management sprinkling unmanned plane of root, neck or/and leaf portion according to the crops is located to it
Between select the agricultural chemicals of the corresponding proportioning of sprinkling or respective classes.
8. method as claimed in claims 6 or 7, it is characterised in that control sprinkling unmanned plane selects corresponding proportioning or respective class
Other agricultural chemicals is precisely sprayed in the subinterval or collection interval the crops for having pest and disease damage.
9. the system that a kind of UAV Intelligent is recognized, accurately sprayed insecticide, it is characterised in that include：Control centre, investigate nobody
Machine and sprinkling unmanned plane；
The Drones for surveillance and sprinkling unmanned plane are connected respectively with control centre's wireless signal；
The crop area for prestoring is divided into multiple subintervals by the control centre, and the control centre controls the investigation nothing
Crop map picture in the man-machine collection subinterval, and by the crops image feedback in control centre；
The control centre is additionally operable to carry out the crop map picture of collection automatically with the pest and disease damage crop map picture for prestoring
Relatively, differentiate that each subinterval whether there is pest and disease damage, if existing, control sprinkling unmanned plane to correspondence subinterval sprinkling is accordingly matched somebody with somebody
Than or respective classes agricultural chemicals.
10. system as claimed in claim 9, it is characterised in that the Drones for surveillance is multi-rotor unmanned aerial vehicle.
11. systems as claimed in claim 10, it is characterised in that the control centre includes collection route default unit, number
According to library unit, recognition unit and control unit；
The collection route presets unit to be used to preset collection route of the Drones for surveillance in the subinterval；
The Database Unit is used to prestore the crop map picture of pest and disease damage；
Described control unit is used to control the Drones for surveillance in the subinterval by collection route collection set in advance
Crop map picture in the subinterval；
It seems no with the database list that the recognition unit is used for the crop map of Drones for surveillance's collection described in automatic identification
The pest and disease damage crop map of unit's storage is as same or similar；
Described control unit is additionally operable to control subinterval sprinkling proportioning corresponding to the pest and disease damage that sprinkling unmanned plane is located to it
Or the agricultural chemicals of respective classes.
12. systems as claimed in claim 11, it is characterised in that the collection route is several words set in the subinterval
Multiple collections that type route or the subinterval are separated are interval.
13. systems as claimed in claim 12, it is characterised in that the Drones for surveillance includes image capture device, data
Transceiver and first processor；
Described image collecting device is connected with data transmitting/receiving equipment, and described image collecting device is used to gather crop map picture, and
The crop map picture is sent to the control centre by data transmitting/receiving equipment；
The output end of the first processor is connected with described control unit signal, and the first processor is single according to the control
The instruction of unit controls the Drones for surveillance and gathers information.
14. systems as claimed in claim 13, it is characterised in that the sprinkling unmanned plane is provided with second processing device, agricultural chemicals
The agricultural chemicals placer of sprinkler and at least two different ratio agricultural chemicals or two kinds of different classes of agricultural chemicals；
The agricultural chemicals placer is connected with pesticide sprayer, and the pesticide sprayer is connected with the second processing device, and described
Two processors are connected with described control unit signal；
The second processing device selects connection corresponding by the instruction control pesticide sprayer of spraying insecticide of described control unit
Agricultural chemicals placer sprays the agricultural chemicals of corresponding proportioning or respective classes.
15. systems as claimed in claim 14, it is characterised in that be provided with least two medicine-chests in the agricultural chemicals placer.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611163877.8A CN106585992A (en) | 2016-12-15 | 2016-12-15 | Method and system for intelligent identification and accurate pesticide spraying using unmanned aerial vehicles |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611163877.8A CN106585992A (en) | 2016-12-15 | 2016-12-15 | Method and system for intelligent identification and accurate pesticide spraying using unmanned aerial vehicles |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106585992A true CN106585992A (en) | 2017-04-26 |
Family
ID=58801771
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611163877.8A Pending CN106585992A (en) | 2016-12-15 | 2016-12-15 | Method and system for intelligent identification and accurate pesticide spraying using unmanned aerial vehicles |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106585992A (en) |
Cited By (27)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106956778A (en) * | 2017-05-23 | 2017-07-18 | 广东容祺智能科技有限公司 | A kind of unmanned plane pesticide spraying method and system |
| CN107272734A (en) * | 2017-06-13 | 2017-10-20 | 深圳市易成自动驾驶技术有限公司 | Unmanned plane during flying task executing method, unmanned plane and computer-readable recording medium |
| CN107409933A (en) * | 2017-05-18 | 2017-12-01 | 何振贤 | A kind of vine integrated pest prevention and control management method |
| CN107494499A (en) * | 2017-09-22 | 2017-12-22 | 常州有恒智能装备科技有限公司 | Unmanned plane pesticide spraying control system |
| CN107682456A (en) * | 2017-11-07 | 2018-02-09 | 蔡璟 | A kind of intelligent gardens nursing system based on image and root detection |
| CN107766837A (en) * | 2017-11-07 | 2018-03-06 | 蔡璟 | The gardens maintenance system that a kind of distinguished point based compares |
| CN107993226A (en) * | 2017-12-12 | 2018-05-04 | 融水苗族自治县大浪镇人民政府 | A kind of unmanned plane Cultivate administration monitoring method |
| CN108492206A (en) * | 2018-03-27 | 2018-09-04 | 贵州省烟草公司毕节市公司 | A kind of tobacco leaf production management method and step based on unmanned plane |
| CN108541683A (en) * | 2018-04-18 | 2018-09-18 | 济南浪潮高新科技投资发展有限公司 | A kind of unmanned plane pesticide spraying system based on convolutional neural networks chip |
| CN108968116A (en) * | 2018-05-17 | 2018-12-11 | 长沙学院 | A kind of wisdom agricultural data acquisition method and system based on Internet of Things |
| CN109287223A (en) * | 2018-08-13 | 2019-02-01 | 珠海羽人农业航空有限公司 | Intelligent agriculture and forestry operating system and method |
| CN109407701A (en) * | 2018-11-30 | 2019-03-01 | 广州极飞科技有限公司 | Control the method, system and device of operation |
| CN109392552A (en) * | 2018-10-18 | 2019-03-01 | 广州极飞科技有限公司 | Drug stowage, apparatus and system |
| CN109446959A (en) * | 2018-10-18 | 2019-03-08 | 广州极飞科技有限公司 | Partitioning method and device, the sprinkling control method of drug of target area |
| CN109446955A (en) * | 2018-10-17 | 2019-03-08 | 南京理工大学泰州科技学院 | A kind of image processing method, device, unmanned plane and server |
| CN109492541A (en) * | 2018-10-18 | 2019-03-19 | 广州极飞科技有限公司 | Determination method and device, plant protection method, the plant protection system of target object type |
| CN109711272A (en) * | 2018-12-04 | 2019-05-03 | 量子云未来（北京）信息科技有限公司 | Crops intelligent management method, system, electronic equipment and storage medium |
| CN110162087A (en) * | 2019-05-17 | 2019-08-23 | 安徽舒州农业科技有限责任公司 | A kind of method and device of plant protection drone automatic identification corps diseases |
| CN110914780A (en) * | 2017-04-28 | 2020-03-24 | 株式会社OPTiM | System, method, and program for creating action plan of unmanned aerial vehicle |
| CN111437420A (en) * | 2019-10-26 | 2020-07-24 | 泰州三凯工程技术有限公司 | Environment maintenance system based on customized filtering |
| CN111930133A (en) * | 2020-07-20 | 2020-11-13 | 贵州电网有限责任公司 | Transformer substation secondary screen cabinet inspection method based on rotor unmanned aerial vehicle |
| EP3741214A1 (en) * | 2019-05-20 | 2020-11-25 | BASF Agro Trademarks GmbH | Method for plantation treatment based on image recognition |
| WO2021087685A1 (en) * | 2019-11-04 | 2021-05-14 | 深圳市大疆创新科技有限公司 | Operation planning method and device combining multispectral and earth surface semantic information, and apparatus |
| CN113139461A (en) * | 2021-04-23 | 2021-07-20 | 塔里木大学 | Wheat leaf disease and insect pest detection system for agricultural planting and management method thereof |
| CN113973793A (en) * | 2021-09-09 | 2022-01-28 | 常州希米智能科技有限公司 | Unmanned aerial vehicle spraying treatment method and system for pest and disease damage area |
| CN115500334A (en) * | 2021-06-07 | 2022-12-23 | 中移(成都)信息通信科技有限公司 | Sprayer, plant disease and insect pest identification method and identification equipment |
| CN116806791A (en) * | 2023-06-27 | 2023-09-29 | 南京晓庄学院 | Crop pest identification method and system based on deep learning |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104320607A (en) * | 2014-08-06 | 2015-01-28 | 江苏恒创软件有限公司 | Method for monitoring growth of farmland crops based on drone |
| CN104330410A (en) * | 2014-11-04 | 2015-02-04 | 无锡北斗星通信息科技有限公司 | Crop pest detection system positioned on unmanned aerial vehicle |
| CN104615146A (en) * | 2015-02-05 | 2015-05-13 | 广州快飞计算机科技有限公司 | Unmanned aerial vehicle spraying operation automatic navigation method without need of external navigation signal |
| CN105173085A (en) * | 2015-09-18 | 2015-12-23 | 山东农业大学 | Automatic control system and method for variable pesticide spraying of unmanned aerial vehicle |
| CN105446309A (en) * | 2015-12-31 | 2016-03-30 | 西北工业大学 | Agricultural intelligent analysis monitoring system based on IOT (Internet of Things) |
| CN105954283A (en) * | 2016-05-05 | 2016-09-21 | 信阳师范学院 | Internet-based wheat pest disease prevention and control method |
-
2016
- 2016-12-15 CN CN201611163877.8A patent/CN106585992A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104320607A (en) * | 2014-08-06 | 2015-01-28 | 江苏恒创软件有限公司 | Method for monitoring growth of farmland crops based on drone |
| CN104330410A (en) * | 2014-11-04 | 2015-02-04 | 无锡北斗星通信息科技有限公司 | Crop pest detection system positioned on unmanned aerial vehicle |
| CN104615146A (en) * | 2015-02-05 | 2015-05-13 | 广州快飞计算机科技有限公司 | Unmanned aerial vehicle spraying operation automatic navigation method without need of external navigation signal |
| CN105173085A (en) * | 2015-09-18 | 2015-12-23 | 山东农业大学 | Automatic control system and method for variable pesticide spraying of unmanned aerial vehicle |
| CN105446309A (en) * | 2015-12-31 | 2016-03-30 | 西北工业大学 | Agricultural intelligent analysis monitoring system based on IOT (Internet of Things) |
| CN105954283A (en) * | 2016-05-05 | 2016-09-21 | 信阳师范学院 | Internet-based wheat pest disease prevention and control method |
Cited By (33)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11543836B2 (en) | 2017-04-28 | 2023-01-03 | Optim Corporation | Unmanned aerial vehicle action plan creation system, method and program |
| CN110914780B (en) * | 2017-04-28 | 2023-08-08 | 株式会社OPTiM | Unmanned aerial vehicle operation plan creation system, method, and program |
| CN110914780A (en) * | 2017-04-28 | 2020-03-24 | 株式会社OPTiM | System, method, and program for creating action plan of unmanned aerial vehicle |
| CN107409933A (en) * | 2017-05-18 | 2017-12-01 | 何振贤 | A kind of vine integrated pest prevention and control management method |
| CN106956778A (en) * | 2017-05-23 | 2017-07-18 | 广东容祺智能科技有限公司 | A kind of unmanned plane pesticide spraying method and system |
| CN107272734A (en) * | 2017-06-13 | 2017-10-20 | 深圳市易成自动驾驶技术有限公司 | Unmanned plane during flying task executing method, unmanned plane and computer-readable recording medium |
| CN107494499A (en) * | 2017-09-22 | 2017-12-22 | 常州有恒智能装备科技有限公司 | Unmanned plane pesticide spraying control system |
| CN107682456A (en) * | 2017-11-07 | 2018-02-09 | 蔡璟 | A kind of intelligent gardens nursing system based on image and root detection |
| CN107766837A (en) * | 2017-11-07 | 2018-03-06 | 蔡璟 | The gardens maintenance system that a kind of distinguished point based compares |
| CN107993226A (en) * | 2017-12-12 | 2018-05-04 | 融水苗族自治县大浪镇人民政府 | A kind of unmanned plane Cultivate administration monitoring method |
| CN108492206A (en) * | 2018-03-27 | 2018-09-04 | 贵州省烟草公司毕节市公司 | A kind of tobacco leaf production management method and step based on unmanned plane |
| CN108541683A (en) * | 2018-04-18 | 2018-09-18 | 济南浪潮高新科技投资发展有限公司 | A kind of unmanned plane pesticide spraying system based on convolutional neural networks chip |
| CN108968116A (en) * | 2018-05-17 | 2018-12-11 | 长沙学院 | A kind of wisdom agricultural data acquisition method and system based on Internet of Things |
| CN109287223A (en) * | 2018-08-13 | 2019-02-01 | 珠海羽人农业航空有限公司 | Intelligent agriculture and forestry operating system and method |
| CN109446955A (en) * | 2018-10-17 | 2019-03-08 | 南京理工大学泰州科技学院 | A kind of image processing method, device, unmanned plane and server |
| CN109492541A (en) * | 2018-10-18 | 2019-03-19 | 广州极飞科技有限公司 | Determination method and device, plant protection method, the plant protection system of target object type |
| CN109446959A (en) * | 2018-10-18 | 2019-03-08 | 广州极飞科技有限公司 | Partitioning method and device, the sprinkling control method of drug of target area |
| CN109392552A (en) * | 2018-10-18 | 2019-03-01 | 广州极飞科技有限公司 | Drug stowage, apparatus and system |
| CN109446959B (en) * | 2018-10-18 | 2021-10-08 | 广州极飞科技股份有限公司 | Target area dividing method and device and medicine spraying control method |
| CN109407701A (en) * | 2018-11-30 | 2019-03-01 | 广州极飞科技有限公司 | Control the method, system and device of operation |
| CN109711272A (en) * | 2018-12-04 | 2019-05-03 | 量子云未来（北京）信息科技有限公司 | Crops intelligent management method, system, electronic equipment and storage medium |
| CN110162087A (en) * | 2019-05-17 | 2019-08-23 | 安徽舒州农业科技有限责任公司 | A kind of method and device of plant protection drone automatic identification corps diseases |
| EP3741214A1 (en) * | 2019-05-20 | 2020-11-25 | BASF Agro Trademarks GmbH | Method for plantation treatment based on image recognition |
| WO2020234296A1 (en) * | 2019-05-20 | 2020-11-26 | Basf Agro Trademarks Gmbh | Method for plantation treatment based on image recognition |
| CN111437420A (en) * | 2019-10-26 | 2020-07-24 | 泰州三凯工程技术有限公司 | Environment maintenance system based on customized filtering |
| WO2021087685A1 (en) * | 2019-11-04 | 2021-05-14 | 深圳市大疆创新科技有限公司 | Operation planning method and device combining multispectral and earth surface semantic information, and apparatus |
| CN111930133A (en) * | 2020-07-20 | 2020-11-13 | 贵州电网有限责任公司 | Transformer substation secondary screen cabinet inspection method based on rotor unmanned aerial vehicle |
| CN113139461A (en) * | 2021-04-23 | 2021-07-20 | 塔里木大学 | Wheat leaf disease and insect pest detection system for agricultural planting and management method thereof |
| CN115500334A (en) * | 2021-06-07 | 2022-12-23 | 中移(成都)信息通信科技有限公司 | Sprayer, plant disease and insect pest identification method and identification equipment |
| CN113973793A (en) * | 2021-09-09 | 2022-01-28 | 常州希米智能科技有限公司 | Unmanned aerial vehicle spraying treatment method and system for pest and disease damage area |
| CN113973793B (en) * | 2021-09-09 | 2023-08-04 | 常州希米智能科技有限公司 | Unmanned aerial vehicle spraying treatment method and system for pest and disease areas |
| CN116806791A (en) * | 2023-06-27 | 2023-09-29 | 南京晓庄学院 | Crop pest identification method and system based on deep learning |
| CN116806791B (en) * | 2023-06-27 | 2024-02-02 | 南京晓庄学院 | Crop pest identification method and system based on deep learning |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106585992A (en) | Method and system for intelligent identification and accurate pesticide spraying using unmanned aerial vehicles | |
| Yinka-Banjo et al. | Sky-farmers: Applications of unmanned aerial vehicles (UAV) in agriculture | |
| CN111582055B (en) | Unmanned aerial vehicle aviation pesticide application route generation method and system | |
| CN106200683B (en) | Unmanned plane plant protection system and plant protection method | |
| US20210000097A1 (en) | Integrated system for controlling, detecting, monitoring, evaluating and treating crop pests | |
| CN106564599A (en) | Plant protection method for unmanned aerial vehicle based on multispectral remote sensing | |
| Teske et al. | Optimised dispensing of predatory mites by multirotor UAVs in wind: A distribution pattern modelling approach for precision pest management | |
| US20230029636A1 (en) | Unmanned aerial vehicle | |
| CN112956461A (en) | Intelligent agricultural insecticidal system based on image recognition | |
| CN108462748A (en) | Administer control method and its device, cloud server, administering method and its system | |
| CN109814551A (en) | Cereal handles automated driving system, automatic Pilot method and automatic identifying method | |
| Kumar et al. | Unmanned aerial vehicle and its application in Indian Agriculture: A perspective | |
| EP4358711A1 (en) | Multi-device agricultural field treatment | |
| EP4230036A1 (en) | Targeted treatment of specific weed species with multiple treatment devices | |
| SP et al. | Unmanned aerial vehicle in the smart farming systems: Types, applications and cyber-security threats | |
| CN109131893A (en) | A kind of pesticide residue real-time detection based on unmanned plane and the system and method accurately removed | |
| Bale et al. | Autonomous Aerial Robots Application for Crop Survey and Mapping | |
| EP4230037A1 (en) | Multi-device agricultural field treatment | |
| Lucas | CHARACTERIZATION AND EFFICACY OF VECTOBAC® WDG APPLICATIONS TARGETING CONTAINER-INHABITING MOSQUITOES USING AN UNMANNED AERIAL VEHICLE | |
| Schellenberger et al. | Leveraging 5G private networks, UAVs and robots to detect and combat broad-leaved dock (Rumex obtusifolius) in feed production | |
| Pandiyan et al. | A Comprehensive Review on Internet of Agro Drones for Precision Agriculture | |
| Rathod et al. | A Study and Analysis on Agricultural Drone | |
| Rose et al. | Application of Drones with Variable Area Nozzles for Effective Smart Farming Activities | |
| Chakraborty | Drone Technology in Agriculture | |
| Siddik et al. | Modern agricultural farming based on robotics and server-synced automation system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170426 |
|
| RJ01 | Rejection of invention patent application after publication |