CN209248554U - A kind of field crops insect pest automatic identification and job management system - Google Patents
A kind of field crops insect pest automatic identification and job management system Download PDFInfo
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- CN209248554U CN209248554U CN201822247119.5U CN201822247119U CN209248554U CN 209248554 U CN209248554 U CN 209248554U CN 201822247119 U CN201822247119 U CN 201822247119U CN 209248554 U CN209248554 U CN 209248554U
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Abstract
The utility model discloses a kind of field crops insect pest automatic identification and job management systems, including more than one data detection device, transmission module, data processing module, remote human-machine's interactive device；The data detection device is installed on field, is equipped with microprocessor, camera, and camera shoots field image and transmits data to transmission module by microprocessor；The data processing module is Cloud Server, built-in crop pest identifies machine learning model, monitor and receive from data detection device by transmission module transmit come image information, using existing model analysis crop whether by insect pest and insect pest classification, to remote human-machine's interactive device send insect pest warning information to sound an alarm.The utility model can realize automatic identification insect pest, continue to optimize crop pest identification and its learning model, while for real-time parameters such as field temperature and humidity, judged automatically in conjunction with Weather Forecast Information and whether need to be administered and be administered position.
Description
Technical field
The utility model relates to field identifying pest and field of operation, in particular to a kind of field crops insect pest automatic identification
And job management system.
Background technique
With the development of Agricultural informatics, sensor type is increasing, and precision is also higher and higher, and Internet of Things is in agricultural
Field plays a more and more important role, and various Internet of things system in the market are also to emerge one after another.But how to organize
It is a problem to be solved with bottom sensor information is made good use of.
For rice crop, rice is one of most important cereal crops, but the insect pest that rice faces happens occasionally,
Common insect pest such as yellow rice borer, striped rice borer, planthopper etc. seriously affects Rice Production.Traditional insect pest monitoring is all planted by artificial observation
Physical property shape, and then it is inferred to insect pest suffered by plant.And current machine learning method provides another way for insect pest monitoring, leads to
The study to a large amount of insect pest of the plant plant pictures is crossed, a plant can be trained whether by insect pest, by the model of what insect pest,
To judge that rice insect pest situation is provided convenience condition.
Meteorological condition is particularly significant for the management measure in farmland, and application when must assure that not to rain within 4 hours,
Otherwise pesticide can be caused to be lost because of rain fall, drug effect is lost.The not only growth and development with crop and insect of temperature, humidity
Closely related, also with the volatilization that sprays medical fluid, the dilution of medical fluid and plant are closely related to the absorbing state of pesticide.Different
Pest has different behaviors and habits, and the existing pest hidden by day and come out at night also has the pest of volt daytime at night out, therefore, field meteorological condition
And weather forecast can instruct field management measure.
Precision agriculture is dedicated to subtracting for pesticide and applies synergy and reduce human cost.Also it is proposed for pest control and field management
Corresponding requirement.
Utility model content
The shortcomings that the purpose of the utility model is to overcome the prior arts and deficiency, it is automatic to provide a kind of field crops insect pest
Identification and job management system, this system can realize automatic identification insect pest, continue to optimize crop pest identification and its learning model,
Simultaneously for real-time weathers parameters such as field temperature and humidity, is judged automatically in conjunction with Weather Forecast Information and whether need to be administered and apply
Medicine position.
The purpose of this utility model is realized by the following technical solution: a kind of field crops identifying pest and job management
System, including more than one data detection device, transmission module, data processing module, remote human-machine's interactive device；
The data detection device is installed on field, embedded micro-processor, and camera is installed on the outside of data detection device,
It shoots field image and passes through microprocessor to other module transfer data；
The data processing module is Cloud Server, and built-in crop pest identifies machine learning model, monitor and receive from
Data detection device by transmission module transmit come image information, using existing model analysis crop whether by insect pest and worm
Harmful classification sends insect pest warning information to remote human-machine's interactive device.
Remote human-machine's interactive device receives the warning information that data processing module issues and sounds an alarm.
Preferably, be fixed on can be on the platform on the vertical bar of vertical telescopic for the data detection device, can be according to Plants
The crop ecological position of high and specific insect pest manually adjusts setting position；Flash lamp is installed on the outside of data detection device, it can basis
The crop ecological position of crop plant height and specific insect pest manually adjusts setting position, facilitates the image, more for extracting crop specific part
Easily discovery insect pest；Flash lamp cooperation camera is taken pictures in the environment of dark, improves the accuracy identified to crop pest.
Preferably, it is equipped with storage card in the data detection device, can store because odjective cause cannot be timely transmitted to count
According to the data of processing module.
Preferably, sensor, including Temperature Humidity Sensor, wind are monitored equipped with environmental parameter on the outside of the data detection device
Fast sensor, photosensitive sensor, locating module, temperature, humidity, wind speed and the intensity of illumination of monitoring crop, while to the data
Detection device is positioned, namely is positioned to the data source monitored.
Further, environmental parameter threshold value is arranged by remote human-machine's interactive device, threshold value is uploaded to Cloud Server,
Cloud Server receives the data information of environmental parameter monitoring sensor and the weather forecast information of local meteorological observatory, on the one hand can will
Whether environmental data saves in the database, also can be more than on the other hand institute according to the set various environmental parameters of threshold decision
The threshold value of setting, the specific location for whether needing to be administered and be administered, and corresponding warning information is sent to remote human-machine and is handed over
Mutual equipment realizes weather monitoring and early warning, and provides irrigation, draining, application type, the specific location for whether being administered, being administered
It is recommended that.
Preferably, the transmission module is the module that can be achieved to transmit at a distance.
When preferably, using crop pest identification machine learning model identification insect pest, Cloud Server is not high by resolution
Image makes classification and marks and prompt operator, and operator is by remote human-machine's interactive device to the image indicated
Classification mark is made, and then machine learning model is finely adjusted by the way that new insect pest picture is added, substitutes original machine learning
Model；By the continuous fine tuning of machine learning model, identifying pest rate, Statistical error effect can further improve.
Further, for unrecognized insect pest, prompt operator by remote human-machine's interactive device to data
The insect pest picture that detection module is collected carries out model training, and by corresponding insect pest new model and is uploaded to Cloud Server.
Preferably, remote human-machine's interactive device is computer or plate or mobile phone or any combination.
Preferably, the equipment that the field crops insect pest automatic identification and each equipment of job management system have oneself
Number, acquisition mode includes single-point acquiring, multipoint acquisition.
The utility model compared with prior art, is had the following advantages and beneficial effects:
1, continuous, real-time, multi-parameter the acquisition and monitoring to field collection point automatically may be implemented in the utility model, knot
Cooperation object identifying pest machine learning model judge automatically rice field whether by insect pest and insect pest classification, and issue warning information,
Realize insect pest automatic identification and early warning.
2, the utility model it is signable go out resolution is high, unrecognized insect pest image, remind operator to carry out excellent
Change, further increases the discrimination of insect pest, Statistical error effect.
3, the utility model can manually adjust setting position according to the crop ecological position of crop plant height and specific insect pest, more suitable
Environment and insect pest habit are answered, the accuracy identified to crop pest is improved.
4, the utility model combination field Real-time Monitoring Data and weather forecast information, by the way that environmental parameter threshold value is arranged,
It judges automatically whether current rice field needs to be administered, prompts the dosage of application, the accurate location of application.
Detailed description of the invention
Fig. 1 is a kind of field rice insect pest automatic identification of the utility model embodiment and job management system composition signal
Figure.
Fig. 2 is the environmental parameter of a kind of field rice insect pest automatic identification of the utility model embodiment and job management system
Monitor sensor composition schematic diagram.
Fig. 3 is the rice field of a kind of field rice insect pest automatic identification of the utility model embodiment and job management system
Meteorological parameter collector underlying principles figure.
Fig. 4 is the Cloud Server of a kind of field rice insect pest automatic identification of the utility model embodiment and job management system
Functional schematic.
Fig. 5 is the paddy field number of a kind of field rice insect pest automatic identification of the utility model embodiment and job management system
According to detection device distribution schematic diagram.
Fig. 6 is the paddy field number of a kind of field rice insect pest automatic identification of the utility model embodiment and job management system
Environment schematic is acquired according to detection device.
Wherein: 1-9 be the layout points of data detection device, 10-data detection devices, 11-platforms, 12-vertical bars.
Specific embodiment
In order to better understand the technical solution of the utility model, the utility model is described in detail with reference to the accompanying drawing and provides
Embodiment, however, the embodiments of the present invention are not limited thereto.
Embodiment 1
As shown in figures 1 to 6, a kind of field rice identifying pest and job management system, including data detection device, GPRS
Wireless transport module, data processing module, remote human-machine's interaction computer；
As seen in figs. 5-6, for data detection device at deployment 9, each inside is respectively mounted microprocessor, microprocessor
Using the single-chip microcontroller of STM32 series；Camera selects OV7725 model, is installed on the outside of data detection device, shoots field figure
Picture simultaneously transmits data to transmission module or storage card by microprocessor；
The data processing module is Cloud Server, and built-in rice insect pest identifies machine learning model, monitor and receive from
Data detection device by GPRS wireless transport module transmit come image information, using existing model analysis crop whether by
The classification of insect pest and insect pest sends insect pest warning information to computer.
The data detection device 10 is fixed on can be on the platform 11 on the vertical bar 12 of vertical telescopic, outside data detection device
Side is equipped with flash lamp, can manually adjust setting position according to the Rice Ecology position of Plant Height of Rice and specific insect pest, facilitate extraction
The image of the specific ecological niche of rice is easier to discovery insect pest；Flash lamp cooperation camera is taken pictures in the environment of dark, is improved
To the accuracy of rice insect pest identification.
When using rice insect pest identification machine learning model identification insect pest, Cloud Server makes the not high image of resolution
Operator is indicated and prompts, operator makes classification mark to the image indicated by computer, and then new by being added
Existing insect pest picture machine learning model is finely adjusted, substitute original machine learning model；Pass through machine learning model
Continuous fine tuning, can further improve identifying pest rate, Statistical error effect.
For unrecognized insect pest, the insect pest picture that prompts operator to collect by computer to data detection module into
Row model training, and by corresponding insect pest new model and it is uploaded to Cloud Server.Receive what data processing module issued by computer
Warning information simultaneously sounds an alarm.
Storage card is SD card in the data detection device, cannot send data cases in time in GPRS wireless transport module
Lower preservation data read the data in SD card by computer and are sent to server again.
Sensor, including Temperature Humidity Sensor, wind speed sensing are monitored equipped with environmental parameter on the outside of the data detection device
Device, illuminance sensor, GPS positioning module, Temperature Humidity Sensor use SHT10 Temperature Humidity Sensor, and air velocity transducer uses
YGC-10 air velocity transducer, illuminance sensor use GY30 illuminance sensor, and GPS uses ATGM332D locating module.Institute
The end PC9 of the end the SCK connection microprocessor of Temperature Humidity Sensor is stated, the end the DATA connection microprocessor of Temperature Humidity Sensor
The end PC8.The end PA1 of the end the VOUT connection microprocessor of the air velocity transducer.The micro- place of SCL connection of the illuminance sensor
Manage the end PB6 of device, the end PB7 of the end SDA connection microprocessor.The TXD of GPS connects PB3, and the end RXD connects PB2.The data of camera are defeated
Outlet is D0-D7, is connected to the end PB8-PB15 of STM32.The temperature of above-mentioned environmental parameter monitoring sensor multipoint acquisition paddy field,
Humidity, wind speed and intensity of illumination, while the data detection device is positioned, namely the data source monitored is determined
Position.
Environmental parameter threshold value is set by computer, threshold value is uploaded to Cloud Server, Cloud Server receives environmental parameter prison
The data information of sensor and the weather forecast information of local meteorological observatory are surveyed, on the one hand environmental data can be stored in database
In, whether it is more than on the other hand set threshold value according to the set various environmental parameters of threshold decision, whether needs to be administered
And the specific location of application, and corresponding warning information is sent to remote human-machine's interactive device, realizes weather monitoring and pre-
It is alert, and provide irrigation, draining, the suggestion for being administered type, the specific location for whether being administered, being administered.
The experimental plot that the present embodiment is selected is in Grain Filling, when Temperature Humidity Sensor monitor temperature be more than 30 DEG C and
When humidity is less than or equal to 60%RH, computer can also remind needs to fill in time to paddy field other than showing corresponding parameter
Irrigate processing.
In addition, if when humidity is less than 70%RH, then prompt is wet by irrigating increase air in Rice Heading florescence
Degree；If being in the insemination and emergence phase, whether the prompt more than 12 DEG C can be sowed on 5th for passing samming on the five and forecast future weather；If
It is administered, then requires ambient humidity to be no more than 90%, do not rain within the following 4h.
The utility model identifies rice insect pest and its identification model is in conjunction with designed specific Internet of things system, passes through
Timed shooting obtains the image of rice, and using the identifying pest model analysis insect pest of server end, real-time judge plant is aggrieved
Situation.
Meanwhile being judged by the acquisition of various environmental parameters and insect pest, specific field management measure is formulated, can be accurate
Agricultural provides operation foundation.
Above-described embodiment is the preferable embodiment of the utility model, but the embodiments of the present invention is not by above-mentioned
The limitation of embodiment, it is made under other any spiritual essence and principles without departing from the utility model to change, modify, replacing
In generation, simplifies combination, should be equivalent substitute mode, is included within the protection scope of the utility model.
Claims (7)
1. a kind of field crops insect pest automatic identification and job management system, which is characterized in that examined including more than one data
Survey device, transmission module, data processing module, remote human-machine's interactive device；
The data detection device is installed on field, embedded micro-processor, and camera is installed on the outside of data detection device, shooting
Field image simultaneously transmits data to transmission module by microprocessor；
The data processing module is Cloud Server, and built-in crop pest identifies machine learning model, monitors and receive from data
The image information that detection device is transmitted by transmission module, using existing model analysis crop whether by insect pest and insect pest
Classification sends insect pest warning information to remote human-machine's interactive device；
Remote human-machine's interactive device receives the warning information that data processing module issues and sounds an alarm.
2. field crops insect pest automatic identification according to claim 1 and job management system, which is characterized in that the number
Being fixed on according to detection device can be on the platform on the vertical bar of vertical telescopic；Flash lamp is installed on the outside of data detection device, can be set
It sets when taking pictures while opening.
3. field crops insect pest automatic identification according to claim 1 and job management system, which is characterized in that the number
According in detection device be equipped with storage card, the detection data of storing data detection device.
4. field crops insect pest automatic identification according to claim 1 and job management system, which is characterized in that the number
Sensor, including Temperature Humidity Sensor, air velocity transducer, illuminance sensing are monitored equipped with environmental parameter according on the outside of detection device
Device, locating module.
5. field crops insect pest automatic identification according to claim 1 and job management system, which is characterized in that the biography
Defeated module is the module that can be achieved to transmit at a distance.
6. field crops insect pest automatic identification according to claim 1 and job management system, which is characterized in that described remote
Journey human-computer interaction device is computer or plate or mobile phone or any combination.
7. field crops insect pest automatic identification according to claim 1 and job management system, which is characterized in that the field
Interrow crop insect pest automatic identification and each equipment of job management system have the device number of oneself.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201822247119.5U CN209248554U (en) | 2018-12-28 | 2018-12-28 | A kind of field crops insect pest automatic identification and job management system |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN110915782A (en) * | 2019-11-15 | 2020-03-27 | 西安和光明宸科技有限公司 | Adjustable plant pesticide spraying system and pesticide spraying method |
| CN112307910A (en) * | 2020-10-16 | 2021-02-02 | 山东省烟台苹果大数据有限公司 | Orchard disease and pest detection system based on deep learning and detection method thereof |
-
2018
- 2018-12-28 CN CN201822247119.5U patent/CN209248554U/en active Active
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN110915782A (en) * | 2019-11-15 | 2020-03-27 | 西安和光明宸科技有限公司 | Adjustable plant pesticide spraying system and pesticide spraying method |
| CN112307910A (en) * | 2020-10-16 | 2021-02-02 | 山东省烟台苹果大数据有限公司 | Orchard disease and pest detection system based on deep learning and detection method thereof |
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