CN106332855A - Automatic early warning system for pests and diseases - Google Patents
Automatic early warning system for pests and diseases Download PDFInfo
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- CN106332855A CN106332855A CN201510398009.7A CN201510398009A CN106332855A CN 106332855 A CN106332855 A CN 106332855A CN 201510398009 A CN201510398009 A CN 201510398009A CN 106332855 A CN106332855 A CN 106332855A
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Abstract
The invention discloses an automatic early warning system for pests and diseases comprising a server and a pest situation real-time measuring system; the server comprises a human-computer interaction interface, a data analysis module, an insect identification and classification module and an image storage module; the pest situation real-time measuring system comprises a central processing unit and a communication module, wherein the central processing unit is connected with an image acquisition unit, an image processing unit and a mechanical operation unit. The system of the invention has following beneficial effects: the system is practical in that it can classify and count killed insets as well as kill the insects; insect recognition rate is high; the system is easy to use; the system can monitor in real time and save labor cost, and is green and environmentally friendly. The problems that collecting field pest type and quantity by hand is difficult and data lacks real-time performance due to agricultural production characters of large plantation area, numerous in variety and complicated production environment are solved. Therefore, the automation and real-time of plant protection platform are fulfilled.
Description
Technical field
The present invention relates to a kind of pest and disease damage early warning system, automatically pre- particularly to a kind of pest and disease damage
Alarm system.
Background technology
At present, pest and disease damage early warning system market using is primarily present following defect: 1, knows
Not rate is low, and because different types of polypide is collected together, complicated classification, if wet weather
Sultry, polypide is easily rotted；2nd, cost of labor is high, manual sort and count insect numbers,
Human cost is huge, and has very strong technical requirements and working sense of responsibility to operator；3、
Monitoring period is long, due to cannot real-time monitoring, lose the prevention and control of plant diseases, pest control best opportunity, cause through
Ji loss；4th, early warning inefficient, artificial early warning, information transmission level is many, and data analysiss are imitated
Rate is low.
Content of the invention
The technical problem to be solved is for the deficiencies in the prior art, provides a kind of disease
Insect pest automatic early-warning system.
To achieve these goals, the measure that the present invention is taken:
A kind of pest and disease damage automatic early-warning system, including server and insect pest situation real-time measurement system,
Described server includes human-computer interaction interface, data analysis module, insecticide identification sort module
With figure memory module, insect pest situation real-time measurement system includes CPU and communication module,
CPU is connected with image acquisition units, graphics processing unit and mechanically actuated unit；
Described CPU is connected with to the fill-in light list of image acquisition units light filling
Unit；
Described CPU is connected with gps positioning unit；
Described CPU is connected with environmental sensor；
Described communication module adopts bluetooth, nfc, gprs, 3g or wifi transmission data；
Described mechanically actuated unit is insect trap device, and insect trap device is by central authorities
The machine assembly of the automatically replaceable mythimna separata paper that processing unit 7 controls.
Beneficial effects of the present invention: practical, while killing insecticide, can be to the elder brother killing
Worm carries out classifying, counts, and insecticide discrimination is high, easy to use, real-time monitoring, saves a large amount of
Cost of labor, environmental protection.Effectively solving is had due to agricultural production, and " cultivated area is big, product
Kind various, production environment is complicated " characteristic, lead to artificial field to collect insect pest species, quantity
Difficulty data, the problem of data deficiency real-time, thus realize plant protection platform automatization, in real time
The purpose changed.
Brief description
Fig. 1, the block diagram of the present invention.
Fig. 2, insecticide detection and the identification module handling process block diagram of the present invention.
Specific embodiment
A kind of pest and disease damage automatic early-warning system, including server 1 and insect pest situation real-time measurement system
2, described server 1 includes human-computer interaction interface 3, data analysis module 4, insecticide identification
Sort module 5 and figure memory module 6, insect pest situation real-time measurement system 2 includes central authorities and processes list
Unit 7 and communication module 14, CPU 7 is connected with image acquisition units 8, at image
Reason unit 9 and mechanically actuated unit 10.
Described CPU 7 is connected with to the auxiliary of image acquisition units 8 light filling
Light unit 11.
Described CPU 7 is connected with gps positioning unit 12.
Described CPU 7 is connected with environmental sensor 13.
Described communication module 14 adopts bluetooth, nfc, gprs, 3g or wifi to transmit number
According to.
Described mechanically actuated unit 10 be insect trap device, insect trap device be by
The machine assembly of the automatically replaceable mythimna separata paper that Central Processing Unit 7 controls.
The insect image identification data of front-end collection, geographical data are sent out in the way of wireless telecommunications
It is analyzed processing to remote server, to reach the purpose of insect pest early warning.
Insect trap device is the automatically replaceable mythimna separata paper being controlled by CPU 7
Machine assembly.As: detection means detects insect density on mythimna separata paper and reaches default threshold value then
Or reach the automatic replacing that default time restriction then carries out mythimna separata paper.Insect pest situation measures system in real time
In system 2, the trap lamp of setting will automatically turn in the pest activity high-incidence season, and use insect-attracting light
Spectrum attracts crops harmful insect.Due to insecticide phototaxis, insecticide will fly to trap lamp process
In strike transparency glass plate outside insect-attracting light, lead to fall after insecticide is dizzy, insect-sticking plate will
After harmful insect sticks, image acquisition units 8 take carrying out shooting to the insecticide on insect-sticking plate
Sample.
Image acquisition units 8 are used for obtaining the insect image identification on insect-sticking plate.When acquiring the images,
CPU 7 sends instruction unpack auxiliary light unit 11 to shooting environmental light filling, simultaneously
Close trap lamp.Original image after acquisition sends and carries out image to graphics processing unit 9 and locate in advance
Reason, Image semantic classification includes white balance, reduces noise, compression of images, form is changed, is formed
Insect image identification data.
Environmental sensor 13 includes temperature sensor and humidity sensor, surveys for obtaining insect pest situation
The temperature of report lamp local environment, humidity information, soil boy structure data.
Gps positioning unit 12 is used for obtaining the positional information at insect pest situation real-time measurement system 2 place,
Form gps data.
Insect image identification data, environmental data, gps data and insect pest situation real-time measurement system 2
Status information will by communication module 14 (bluetooth, nfc, gprs, 3g or wifi) with
The form of wireless telecommunications sends to server 1.
Described mythimna separata paper insect density algorithm for estimating adopts gradient as feature, before gradient
Representing insect, insect is more, and its gradient foreground point is also more at sight spot, and, Gradient Features
There is certain robustness to the false prospect causing due to illumination.That gradient is selected is scharr
Operator.Respectively convolution is carried out to image using operator, you can obtain gradient image, to gradient map
As binaryzation obtains gradient prospect.The gaussian filtering collecting image employing a 5*5 afterwards is made an uproar
Sound is filtered.Finally, we account for image pixel using gradient prospect points in an image
Ratio come Insects density it may be assumed that
In order to estimate to density using gradient, employ the recurrence based on method of least square
Algorithm, and it is provided with an algorithms library acceptable analysis upper limit, it is designated as max.Insecticide
Quantity yi, i is the numbering of experiment picture.The density of insecticide:
Then it is extracted the density feature of every pictures, be set to featurei, obtain
(featurei, densityi), then learn prediction system using the linear regression method in machine learning
Number coeff.After obtaining coeff, to new picture j it is only necessary to calculate its featurej, so
Can estimate its density afterwards as follows:
densityj=coeff*featurej
Insecticide detection is with identification module handling process as shown in Fig. 2 detection is based primarily upon segmentation
Algorithm, identification is then feature and the support vector machine (support based on engineer
Vector machine, svm) realizing.
The purpose of described insecticide Target Segmentation is to be isolated insecticide from background using algorithm
Come.
Described feature extraction includes the following CF feature and carrys out Insects:
1st, girth；2nd, area；3rd, spherical property；4th, circle；5th, like circularity；6、
Lobate property；7th, color characteristic.
Described insect type identification is divided into classification of insect training and identification two parts.
Described training need is selected the feeding training aidss of quality preferable sample manually and is instructed
Practice.
Described identification refers to specific insecticide is identified.
Insect pest situation real-time measurement system 2 is by solar cell for supplying power.
Server 1 basic function of described Remote Installation is divided into:
1st, image uploads reception system
Image uploads reception system and all acquisition terminals (image acquisition units 8) is gone up blit
Piece is classified according to picture collection time, gps position and user and is stored in picture cloud
On server (figure memory module 6).
2nd, image insect classification and number system automatically
According to acquisition terminal (image acquisition units 8) uploading pictures, automatic classification system will
Uploading pictures are carried out with graphical analyses, identifies target pest and classified according to pest species
And count, after the completion of analysis, will show that detailed insect obtains relatively in picture in page end
Coordinate and with clearly indicating particular location and species using different colours framework.According to system
Complexity automatically determines real-time analysis or analyzes after a while.Due to the skill using automatic load balancing
Art, will effectively improve system utilization rate and reduce power consumption.
3rd, the report automatic generatioin system based on automatic classification and number system
Based on the data of classification and technological system, report generating system is by respectively using cake chart
Display percentage ratio in whole worm amounts for the insect, shows insect development trend, post using curve chart
Shape figure shows and 2-3 pests contrast in the past.And the following insect pest of trendgram display prediction becomes
Gesture.
4th, insect pest early-warning and predicting system
The weather being gathered based on reporting system and differential counting system, integrated information collection terminal,
The information such as soil, based on gray system mathematical theory, the following insect pest occurrence tendency of anticipation, and
To each functional department real time propelling movement insect pest early warning information, to reach the function of very first time preventing and treating.
Concrete grammar:
(1) short-term fast prediction:
Realize with day as unit across all fast prediction, using gm (1,1), gm (2,1),
Verhulst, gm (1,1) residual error, preferentially predicts etc. method.
(2) intermediate trend prediction:
Realize with week as unit across the moon multifactor trend prediction, using gm (1, n), system
Prediction, is predicted etc. method.
(3) long-term dynamics prediction:
Realize across the year dynamic prediction with the moon as unit, pre- using Grey Catastkophe Forecasting, waveform
Survey, be predicted etc. method.
5th, insect pest information dissemination system
Insect pest information dissemination system pushed insect pest species, number respectively by 12 hours, 24 hours
Amount, development trend information to provincial, and municipal level agricultural plant protection department, keep all departments' information to upgrade in time
And it is unified.
6th, acquisition terminal management system
Acquisition terminal management system will be classified according to company, functional department.Can in time more
Change acquisition terminal quantity and related setting.
Claims (6)
1. a kind of pest and disease damage automatic early-warning system, including server (1) and insect pest situation real-time measurement system
(2) it is characterised in that: described server (1) includes human-computer interaction interface (3), number
According to analysis module (4), insecticide identification sort module (5) and figure memory module (6), worm
Feelings real-time measurement system (2) includes CPU (7) and communication module (14), in
Central Processing Unit (7) is connected with image acquisition units (8), graphics processing unit (9) and machine
Tool operating unit (10).
2. a kind of pest and disease damage automatic early-warning system according to claim 1 it is characterised in that:
Described CPU (7) is connected with to the auxiliary of image acquisition units (8) light filling
Light unit (11).
3. a kind of pest and disease damage automatic early-warning system according to claim 1 and 2, its feature exists
In: described CPU (7) is connected with gps positioning unit (12).
4. a kind of pest and disease damage automatic early-warning system according to claim 3 it is characterised in that:
Described CPU (7) is connected with environmental sensor (13).
5. a kind of pest and disease damage automatic early-warning system according to claim 4 it is characterised in that:
Described communication module (14) adopts bluetooth, nfc, gprs, 3g or wifi transmission data.
6. a kind of pest and disease damage automatic early-warning system according to claim 4 it is characterised in that:
Described mechanically actuated unit (10) is insect trap device, and insect trap device is by central authorities
The machine assembly of the automatically replaceable mythimna separata paper that processing unit (7) controls.
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Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106940734A (en) * | 2017-04-24 | 2017-07-11 | 南京信息工程大学 | A kind of Migrating Insects monitor recognition methods and device in the air |
| CN107347859A (en) * | 2017-08-15 | 2017-11-17 | 河南工业大学 | A kind of grain storage pest trapping system |
| CN107809833A (en) * | 2017-11-24 | 2018-03-16 | 北京守朴科技有限公司 | Desinsection lamp control method, apparatus and system |
| CN108510490A (en) * | 2018-03-30 | 2018-09-07 | 深圳春沐源控股有限公司 | Method and device for analyzing insect pest trend and computer storage medium |
| CN109472252A (en) * | 2018-12-28 | 2019-03-15 | 华南农业大学 | A kind of field crops insect pest automatic identification and job management system |
| CN110169408A (en) * | 2019-04-26 | 2019-08-27 | 石河子大学 | Agriculture and forestry device for detecting and reporting pest information and its detecting and reporting pest information method for using the device |
| CN110235873A (en) * | 2019-06-26 | 2019-09-17 | 北京农业智能装备技术研究中心 | A kind of agricultural harmful insect insect pest situation automatic monitoring forecast system |
| CN110363754A (en) * | 2019-07-16 | 2019-10-22 | 上海秒针网络科技有限公司 | Mosquito killer lamp adjusting method and device, storage medium and electronic device |
| CN110516712A (en) * | 2019-08-01 | 2019-11-29 | 仲恺农业工程学院 | Insect pest image recognition method, insect pest monitoring method, insect pest image recognition device, insect pest monitoring equipment and insect pest monitoring medium |
| CN110663659A (en) * | 2019-11-12 | 2020-01-10 | 图锐(北京)信息技术有限公司 | Intelligent pest monitoring system and method |
| CN110688989A (en) * | 2019-10-31 | 2020-01-14 | 无锡蜂巢生态农业有限公司 | Ecological-environment-friendly-based intelligent agricultural monitoring management system and method |
| CN110798536A (en) * | 2019-11-12 | 2020-02-14 | 图锐(北京)信息技术有限公司 | Cloud pest remote monitoring system and method |
| CN111046777A (en) * | 2019-12-06 | 2020-04-21 | 深圳大学 | Insect density recognition system based on LoRa node |
| CN113273555A (en) * | 2021-06-15 | 2021-08-20 | 米恩基(浙江)传感科技有限公司 | Artificial intelligence insect situation prediction system and prediction method |
| CN114586583A (en) * | 2022-03-09 | 2022-06-07 | 仲恺农业工程学院 | Method for preventing and controlling citrus fruit fly |
| CN115191412A (en) * | 2022-08-17 | 2022-10-18 | 广西壮族自治区农业科学院 | Prediction and forecast device for bactrocera dorsalis |
| GB2609311A (en) * | 2021-08-20 | 2023-02-01 | Univ Zhejiang | Intelligent replacement device and method for trap board in tea garden based on image channel computation |
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| CN106940734A (en) * | 2017-04-24 | 2017-07-11 | 南京信息工程大学 | A kind of Migrating Insects monitor recognition methods and device in the air |
| CN107347859A (en) * | 2017-08-15 | 2017-11-17 | 河南工业大学 | A kind of grain storage pest trapping system |
| CN107347859B (en) * | 2017-08-15 | 2023-09-15 | 河南工业大学 | Grain storage pest trapping system |
| CN107809833A (en) * | 2017-11-24 | 2018-03-16 | 北京守朴科技有限公司 | Desinsection lamp control method, apparatus and system |
| CN108510490A (en) * | 2018-03-30 | 2018-09-07 | 深圳春沐源控股有限公司 | Method and device for analyzing insect pest trend and computer storage medium |
| CN108510490B (en) * | 2018-03-30 | 2021-02-19 | 深圳春沐源控股有限公司 | Method and device for analyzing insect pest trend and computer storage medium |
| CN109472252A (en) * | 2018-12-28 | 2019-03-15 | 华南农业大学 | A kind of field crops insect pest automatic identification and job management system |
| CN109472252B (en) * | 2018-12-28 | 2024-06-11 | 华南农业大学 | Automatic identification and operation management system for field crop insect pests |
| CN110169408A (en) * | 2019-04-26 | 2019-08-27 | 石河子大学 | Agriculture and forestry device for detecting and reporting pest information and its detecting and reporting pest information method for using the device |
| CN110235873A (en) * | 2019-06-26 | 2019-09-17 | 北京农业智能装备技术研究中心 | A kind of agricultural harmful insect insect pest situation automatic monitoring forecast system |
| CN110235873B (en) * | 2019-06-26 | 2021-11-26 | 北京农业智能装备技术研究中心 | Automatic monitoring and forecasting system for insect pest situation of agricultural and forestry harmful insects |
| CN110363754A (en) * | 2019-07-16 | 2019-10-22 | 上海秒针网络科技有限公司 | Mosquito killer lamp adjusting method and device, storage medium and electronic device |
| CN110516712A (en) * | 2019-08-01 | 2019-11-29 | 仲恺农业工程学院 | Insect pest image recognition method, insect pest monitoring method, insect pest image recognition device, insect pest monitoring equipment and insect pest monitoring medium |
| CN110516712B (en) * | 2019-08-01 | 2023-04-07 | 仲恺农业工程学院 | Insect pest image recognition method, insect pest monitoring method, insect pest image recognition device, insect pest monitoring equipment and insect pest image recognition medium |
| CN110688989A (en) * | 2019-10-31 | 2020-01-14 | 无锡蜂巢生态农业有限公司 | Ecological-environment-friendly-based intelligent agricultural monitoring management system and method |
| CN110688989B (en) * | 2019-10-31 | 2022-05-17 | 无锡蜂巢生态农业有限公司 | Intelligent agriculture monitoring management system and method based on ecological environment protection |
| CN110663659A (en) * | 2019-11-12 | 2020-01-10 | 图锐(北京)信息技术有限公司 | Intelligent pest monitoring system and method |
| CN110798536A (en) * | 2019-11-12 | 2020-02-14 | 图锐(北京)信息技术有限公司 | Cloud pest remote monitoring system and method |
| CN111046777A (en) * | 2019-12-06 | 2020-04-21 | 深圳大学 | Insect density recognition system based on LoRa node |
| CN111046777B (en) * | 2019-12-06 | 2023-07-25 | 深圳大学 | Insect density identification system based on LoRa node |
| CN113273555A (en) * | 2021-06-15 | 2021-08-20 | 米恩基(浙江)传感科技有限公司 | Artificial intelligence insect situation prediction system and prediction method |
| GB2609311A (en) * | 2021-08-20 | 2023-02-01 | Univ Zhejiang | Intelligent replacement device and method for trap board in tea garden based on image channel computation |
| GB2609311B (en) * | 2021-08-20 | 2023-08-23 | Univ Zhejiang | Intelligent replacement device and method for trap boards in tea garden based on image channel computation |
| CN114586583A (en) * | 2022-03-09 | 2022-06-07 | 仲恺农业工程学院 | Method for preventing and controlling citrus fruit fly |
| CN115191412A (en) * | 2022-08-17 | 2022-10-18 | 广西壮族自治区农业科学院 | Prediction and forecast device for bactrocera dorsalis |
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Application publication date: 20170118 |