CN110063326A - Intelligent bird-repeller method based on convolutional neural networks - Google Patents
Intelligent bird-repeller method based on convolutional neural networks Download PDFInfo
- Publication number
- CN110063326A CN110063326A CN201910361064.7A CN201910361064A CN110063326A CN 110063326 A CN110063326 A CN 110063326A CN 201910361064 A CN201910361064 A CN 201910361064A CN 110063326 A CN110063326 A CN 110063326A
- Authority
- CN
- China
- Prior art keywords
- bird
- birds
- model
- server
- orchard
- 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
-
- 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
- A01M29/00—Scaring or repelling devices, e.g. bird-scaring apparatus
- A01M29/06—Scaring or repelling devices, e.g. bird-scaring apparatus using visual means, e.g. scarecrows, moving elements, specific shapes, patterns or the like
- A01M29/10—Scaring or repelling devices, e.g. bird-scaring apparatus using visual means, e.g. scarecrows, moving elements, specific shapes, patterns or the like using light sources, e.g. lasers or flashing lights
-
- 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
- A01M29/00—Scaring or repelling devices, e.g. bird-scaring apparatus
- A01M29/16—Scaring or repelling devices, e.g. bird-scaring apparatus using sound waves
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Landscapes
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Biomedical Technology (AREA)
- Data Mining & Analysis (AREA)
- Zoology (AREA)
- Environmental Sciences (AREA)
- Pest Control & Pesticides (AREA)
- Birds (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Insects & Arthropods (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Wood Science & Technology (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Optics & Photonics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Catching Or Destruction (AREA)
Abstract
The present invention provides a kind of based on the intelligent bird-repeller method based on convolutional neural networks, belong to field of artificial intelligence, the present invention, using artificial intelligence computer visual field, currently most popular convolutional neural networks judge whether there is this problem of birds in orchard, and driven birds using bird-scaring unit according to certain process, realize intelligent bird repellent.
Description
Technical field
The present invention relates to field of artificial intelligence more particularly to a kind of intelligent bird-repeller sides based on convolutional neural networks
Method.
Background technique
With the development transformation and the progress of artificial intelligence technology of the mode of production and life, nothing weighs for the time being in many lives
Multiple life style can be replaced by artificial intelligence.In various deep neural network structures, convolutional neural networks are using most
Extensive a kind of, it was proposed by LeCun in 1989.Convolutional neural networks are being successfully applied to the knowledge of hand-written character image in early days
Not.Deeper AlexNet network is succeeded within 2012, and hereafter convolutional neural networks flourish, and is widely used in each
A field all achieves current best performance in many problems.In machine vision and other many problems, convolutional Neural
Network achieves current best effect.Convolutional neural networks are by the automatic study image of convolution sum pondization operation at all levels
On feature, this meets the common sense we have appreciated that image.
A part work of existing agricultural or based on artificial, but artificial intelligence technology but obtained it is considerable into
Step.Artificial intelligence answers more serving agricultures, as driving rural activity necessary but cumbersome in this agricultural of birds in orchard more
It should go to solve by the mode of artificial intelligence.
In orchard planting, birds fruit is the most common problem, at the same be also orchard workers' most headache the problem of, bird
Class is pecked at, and to be conducive to germ numerous for a large amount of wounds that fruit not only directly affects the yield and quality of fruit, but also pecked fruit
It grows, makes disease happening and prevelence；Meanwhile Birds In Spring can also peck at fruit tree tender shoots, trample on grafting twig etc., it is therefore necessary to take suitable
Suitable method carries out prevention and control.But orchard workers cannot accomplish to stare at orchard constantly, and really do so this boring work meeting
The a large amount of time is wasted, this can just realize the detection of this target of birds by convolutional Neural.
Summary of the invention
In order to solve the above technical problems, the invention proposes a kind of based on the intelligent bird-repeller side based on convolutional neural networks
Method, the method based on convolutional neural networks realizes target detection, to judge whether the orchard has birds at a certain moment, then
Bird-scaring unit is automatically turned on to drive birds.Whole process realizes intelligent bird repellent without manpower.
The technical scheme is that
Intelligent bird-repeller method based on convolutional neural networks,
Specific step is as follows:
1) preliminary preparation installs laser bird dispeller and loudspeaker bird-scaring unit；
According to the size in orchard, more than one laser bird dispeller and loudspeaker bird-scaring unit are installed, cover all fruits in orchard
Tree, and all bird-scaring units carry out control switch by server.Wherein raised one's voice to its utmost sound is feared by birds in loudspeaker bird-scaring unit
Sound.
2) it acquires the data set of birds and bird repellent model is trained；
Then the data set of the acquisition birds is made that is, in the online picture for collecting different birds more as far as possible
It is made data set, data set is divided into training dataset and test data set；The training dataset made given and is defined
Bird repellent model is trained.
Wherein the model is to add one last based on being usually used in being finely adjusted on the basis of the ssd model of target detection
The full articulamentum of layer, which is two classification.
If detecting birds by the way that front is several layers of, the last layer then exports yes, indicates that birds appear in
Orchard, otherwise, then output is no, is indicated in orchard without birds.
Test data set is put into trained model to be tested, if test result is undesirable, takes data set again
Model is finely tuned in training, until models fitting.
3) bird repellent work is carried out using trained bird repellent model.
3 steps are broadly divided into, i.e.,
(3.1) server camera is arranged the photo opporunity of same intervals, by photo captured by all cameras
All return to server；
(3.2) server is given all photos to trained bird repellent model and is judged, if in a moment institute
Birds are judged out in the photo of return, server is carried out laser bird dispeller and loudspeaker bird-scaring unit all in orchard is opened
Expulsion.Simultaneously in order to ensure that birds will not be harassed again, all laser bird dispellers and loudspeaker bird-scaring unit will continue 10 minutes
To 20 minutes.
(3.3) etc. after the duration of settings, server carries out work of taking pictures for all cameras are again turned on, such as
This circulation can realize moment detection orchard substantially and carry out bird repellent work automatically.
In a period of laser bird dispeller and loudspeaker bird-scaring unit are expelled, there are birds for default, camera does not need again
It takes pictures, while server does not need calling model yet and judged.
The beneficial effects of the invention are as follows
Using artificial intelligence computer visual field, currently most popular convolutional neural networks judge whether to have in orchard
This problem of birds, and driven birds using bird-scaring unit according to certain process, the technology of artificial intelligence field is opened up
It opens up in agricultural production, realizes intelligent bird repellent, expand the company's operation.
Detailed description of the invention
Fig. 1 is workflow schematic diagram of the invention.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments, based on the embodiments of the present invention, those of ordinary skill in the art
Every other embodiment obtained without making creative work, shall fall within the protection scope of the present invention.
The present invention is by being finely adjusted ssd model, to detect in orchard whether have birds.It specifically includes that
One server carries out the training of model；Multiple cameras are taken pictures；One server, fixed intervals to institute
There is camera to issue photographing instruction, and obtains picture and it is handled and then is made a policy；Multiple bird repellents with loudspeaker
Device.
Steps are as follows for concrete implementation:
One, preliminary preparation installs laser bird dispeller and loudspeaker bird-scaring unit
According to the size in orchard, multiple laser bird dispellers and loudspeaker bird-scaring unit are installed, all fruit trees in orchard can be covered,
And all bird-scaring units can carry out control switch by server.Wherein raised one's voice to its utmost sound is feared by birds in loudspeaker bird-scaring unit
Sound, such as the cry etc. of accipiter biology.
Two, it acquires the data set of birds and bird repellent model is trained
In the online picture for collecting different birds more as far as possible, it is then made into data set, data set is divided into instruction
Practice data set and test data set.The training dataset made is given to the bird repellent model defined to be trained.Wherein should
Model is finally to add one layer of full articulamentum, this is complete based on being usually used in being finely adjusted on the basis of the ssd model of target detection
Connection is two classification, and if detecting birds by the way that front is several layers of, the last layer then exports yes, indicates birds
Orchard is appeared in, otherwise, then output is no, is indicated in orchard without birds.
Test data set is put into trained model to be tested, and if test result is undesirable, is fetched again
It is finely tuned according to collection training or to model, until models fitting.
Three, bird repellent work is carried out using trained bird repellent model
(1) multiple cameras are placed in place different in orchard, are able to achieve all standing in orchard.By these cameras with
Server connects, photo opporunity of the server to these cameras setting same intervals, it is contemplated that Gpu current processing energy
Power, can photographic actions of progress per second, the whole of photo captured by all cameras is returned into server.
(2) server is given all photos to trained bird repellent model and is judged, if a certain moment is returned
Photo in be judged out birds, birds run away on other fruit tree and are temporarily taken refuge in order to prevent, and server will open fruit
All laser bird dispellers and loudspeaker bird-scaring unit are expelled in garden.In order to ensure, birds will not carry out again in a period of time simultaneously
Harassing and wrecking, all laser bird dispellers and loudspeaker bird-scaring unit will continue 10 minutes to 20 minutes.During this period, because default has bird
Class, camera do not need to take pictures again, while server does not need calling model temporarily yet and judged.
(3) etc. after the duration of settings, server carries out work of taking pictures for all cameras are again turned on, so
Circulation can realize moment detection orchard substantially and carry out bird repellent work automatically.
The foregoing is merely presently preferred embodiments of the present invention, is only used to illustrate the technical scheme of the present invention, and is not intended to limit
Determine protection scope of the present invention.Any modification, equivalent substitution, improvement and etc. done all within the spirits and principles of the present invention,
It is included within the scope of protection of the present invention.
Claims (9)
1. the intelligent bird-repeller method based on convolutional neural networks, which is characterized in that
Specific step is as follows:
1) preliminary preparation installs laser bird dispeller and loudspeaker bird-scaring unit；
2) it acquires the data set of birds and bird repellent model is trained；
3) bird repellent work is carried out using trained bird repellent model.
2. the method according to claim 1, wherein
In step 1), according to the size in orchard, more than one laser bird dispeller and loudspeaker bird-scaring unit are installed, covered in orchard
All fruit trees, and all bird-scaring units carry out control switch by server.
3. according to the method described in claim 2, it is characterized in that,
Wherein raised one's voice to its utmost sound by birds fears sound in loudspeaker bird-scaring unit.
4. the method according to claim 1, wherein
In step 2), the data set of the acquisition birds, i.e., in the online picture for collecting different birds more as far as possible, then
It is made into data set, data set is divided into training dataset and test data set；It is fixed that the training dataset made is given
The good bird repellent model of justice is trained.
5. according to the method described in claim 4, it is characterized in that,
Wherein the model is to add one layer entirely last based on being usually used in being finely adjusted on the basis of the ssd model of target detection
Articulamentum, which is two classification.
6. method according to claim 4 or 5, which is characterized in that
If detecting birds by the way that front is several layers of, the last layer then exports yes, indicates that birds appear in fruit
Garden, otherwise, then output is no, is indicated in orchard without birds.
7. according to the method described in claim 6, it is characterized in that,
Test data set is put into trained model to be tested, if test result is undesirable, is fetched and is trained according to collection again
Or model is finely tuned, until models fitting.
8. the method according to claim 1, wherein
Step 3) is broadly divided into 3 steps, i.e.,
(3.1) server camera is arranged the photo opporunity of same intervals, by the whole of photo captured by all cameras
Return to server；
(3.2) server is given all photos to trained bird repellent model and is judged, if returned a moment
Photo in be judged out birds, server drives laser bird dispeller and loudspeaker bird-scaring unit all in orchard is opened
By.Simultaneously in order to ensure that birds will not be harassed again, all laser bird dispellers and loudspeaker bird-scaring unit will continue 10 minutes extremely
20 minutes.
(3.3) etc. after the duration of settings, server carries out work of taking pictures for all cameras are again turned on, and so follows
Ring can realize moment detection orchard substantially and carry out bird repellent work automatically.
9. according to the method described in claim 8, it is characterized in that,
In a period of laser bird dispeller and loudspeaker bird-scaring unit are expelled, there are birds for default, camera does not need to carry out again
It takes pictures, while server does not need calling model yet and judged.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910361064.7A CN110063326A (en) | 2019-04-30 | 2019-04-30 | Intelligent bird-repeller method based on convolutional neural networks |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910361064.7A CN110063326A (en) | 2019-04-30 | 2019-04-30 | Intelligent bird-repeller method based on convolutional neural networks |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110063326A true CN110063326A (en) | 2019-07-30 |
Family
ID=67369797
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910361064.7A Pending CN110063326A (en) | 2019-04-30 | 2019-04-30 | Intelligent bird-repeller method based on convolutional neural networks |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110063326A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110679586A (en) * | 2019-09-30 | 2020-01-14 | 深圳供电局有限公司 | Bird repelling method and system for power transmission network and computer readable storage medium |
| CN111493055A (en) * | 2020-03-25 | 2020-08-07 | 深圳威阿科技有限公司 | Multi-airport-collaborative airspace intelligent bird repelling system and method |
| WO2021094851A1 (en) * | 2019-11-13 | 2021-05-20 | Bird Control Group B.V. | System and methods for automated wildlife detection, monitoring and control |
| CN113243354A (en) * | 2021-06-30 | 2021-08-13 | 安徽信息工程学院 | Laser bird repelling method and device based on computer vision algorithm and artificial intelligence technology |
| CN114982739A (en) * | 2022-07-18 | 2022-09-02 | 江苏合力四通智能科技股份有限公司 | Intelligent laser bird repelling device and method based on deep learning |
| CN115299428A (en) * | 2022-08-04 | 2022-11-08 | 国网江苏省电力有限公司南通供电分公司 | Intelligent bird system that drives of thing networking based on degree of depth study |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2003034819A2 (en) * | 2001-10-25 | 2003-05-01 | L'air Liquide Societe Anonyme A Directoire Et Conseil De Surveillance Pour L'etude Et L'exloitation Des Procedes Georges Claude | Nox software sensor |
| CN104823961A (en) * | 2015-04-10 | 2015-08-12 | 国家电网公司 | System and method for dispersing birds on power transmission lines |
| CN205830906U (en) * | 2016-08-03 | 2016-12-28 | 衢州环艺工业设计有限公司 | A kind of comprehensive multiple types laser bird-repelling device |
| CN106530189A (en) * | 2016-10-14 | 2017-03-22 | 中国民航科学技术研究院 | Airport bird-repellent intelligent decision-making method based on support vector machine |
| CN107232175A (en) * | 2017-06-01 | 2017-10-10 | 北京中安航信科技有限公司 | Scarer and its bird repellent method based on birds feature recognition |
| CN109077050A (en) * | 2018-08-23 | 2018-12-25 | 武汉腾路智行科技有限公司 | A kind of bird-repeller system based on machine vision |
| CN109460774A (en) * | 2018-09-18 | 2019-03-12 | 华中科技大学 | A kind of birds recognition methods based on improved convolutional neural networks |
-
2019
- 2019-04-30 CN CN201910361064.7A patent/CN110063326A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2003034819A2 (en) * | 2001-10-25 | 2003-05-01 | L'air Liquide Societe Anonyme A Directoire Et Conseil De Surveillance Pour L'etude Et L'exloitation Des Procedes Georges Claude | Nox software sensor |
| CN104823961A (en) * | 2015-04-10 | 2015-08-12 | 国家电网公司 | System and method for dispersing birds on power transmission lines |
| CN205830906U (en) * | 2016-08-03 | 2016-12-28 | 衢州环艺工业设计有限公司 | A kind of comprehensive multiple types laser bird-repelling device |
| CN106530189A (en) * | 2016-10-14 | 2017-03-22 | 中国民航科学技术研究院 | Airport bird-repellent intelligent decision-making method based on support vector machine |
| CN107232175A (en) * | 2017-06-01 | 2017-10-10 | 北京中安航信科技有限公司 | Scarer and its bird repellent method based on birds feature recognition |
| CN109077050A (en) * | 2018-08-23 | 2018-12-25 | 武汉腾路智行科技有限公司 | A kind of bird-repeller system based on machine vision |
| CN109460774A (en) * | 2018-09-18 | 2019-03-12 | 华中科技大学 | A kind of birds recognition methods based on improved convolutional neural networks |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110679586A (en) * | 2019-09-30 | 2020-01-14 | 深圳供电局有限公司 | Bird repelling method and system for power transmission network and computer readable storage medium |
| WO2021094851A1 (en) * | 2019-11-13 | 2021-05-20 | Bird Control Group B.V. | System and methods for automated wildlife detection, monitoring and control |
| US11109586B2 (en) | 2019-11-13 | 2021-09-07 | Bird Control Group, Bv | System and methods for automated wildlife detection, monitoring and control |
| US11576367B2 (en) | 2019-11-13 | 2023-02-14 | Bird Control Group Bv | System and methods for automated wildlife detection, monitoring and control |
| CN111493055A (en) * | 2020-03-25 | 2020-08-07 | 深圳威阿科技有限公司 | Multi-airport-collaborative airspace intelligent bird repelling system and method |
| CN113243354A (en) * | 2021-06-30 | 2021-08-13 | 安徽信息工程学院 | Laser bird repelling method and device based on computer vision algorithm and artificial intelligence technology |
| CN114982739A (en) * | 2022-07-18 | 2022-09-02 | 江苏合力四通智能科技股份有限公司 | Intelligent laser bird repelling device and method based on deep learning |
| CN115299428A (en) * | 2022-08-04 | 2022-11-08 | 国网江苏省电力有限公司南通供电分公司 | Intelligent bird system that drives of thing networking based on degree of depth study |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110063326A (en) | Intelligent bird-repeller method based on convolutional neural networks | |
| CN203934569U (en) | A kind of laser bird dispeller | |
| CN108664062A (en) | Crop growth monitoring device and its operating system based on dynamic image video analysis | |
| KR102393265B1 (en) | System for detecting pests of shiitake mushrooms | |
| CN104542411A (en) | Intelligent bait feeding device and method based on image processing technology | |
| CN108174832A (en) | Method and device for killing mosquito | |
| CN113468984A (en) | Crop pest and disease leaf identification system, identification method and pest and disease prevention method | |
| CN104918007A (en) | Computer vision-based large field pest situation monitoring sampling device and sampling method | |
| CN115604301A (en) | Planting environment monitoring system based on artificial intelligence | |
| CN107121535A (en) | A kind of effective crop growth environment monitoring system | |
| CN104202560A (en) | Image recognition based video monitoring system and method | |
| Otis | The swarming biology and population dynamics of the Africanized honey bee | |
| CN113627248A (en) | Method, system, lawn mower and storage medium for automatically selecting recognition model | |
| CN109006678A (en) | A kind of system of wireless monitor hatching of breeding eggs | |
| CN105868824A (en) | Intelligent identification data statistics system and insect attack monitoring device provided with same | |
| CN111242903A (en) | Automatic counting platform and method for pigling | |
| CN110326558A (en) | A kind of fishery cultivating method and system based on Internet of Things | |
| CN108967263A (en) | A method of based on Machine Vision Detection Egg Quality | |
| CN213908084U (en) | Full-automatic remote monitoring instrument for aedes eggs | |
| CN214201204U (en) | Rice plant diseases and insect pests detection device based on raspberry group | |
| CN104396828A (en) | Fry feeding management system based on Internet-of-Things high-definition imaging integrated gateway | |
| CN210329057U (en) | Mobile imaging pest and disease identification system | |
| KR20190042504A (en) | Harmful tide eliminator | |
| TWI806239B (en) | Automatic Observation Network and Its Implementing Method | |
| CN112514863B (en) | Full-automatic remote monitoring instrument and monitoring method for aedes eggs |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190730 |