CN105850930A - Machine vision based pre-warning system and method for pest and disease damage - Google Patents
Machine vision based pre-warning system and method for pest and disease damage Download PDFInfo
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- CN105850930A CN105850930A CN201610254139.8A CN201610254139A CN105850930A CN 105850930 A CN105850930 A CN 105850930A CN 201610254139 A CN201610254139 A CN 201610254139A CN 105850930 A CN105850930 A CN 105850930A
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- 241000607479 Yersinia pestis Species 0.000 title claims abstract description 56
- 201000010099 disease Diseases 0.000 title claims abstract description 54
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- 238000005859 coupling reaction Methods 0.000 claims description 4
- 238000009313 farming Methods 0.000 claims description 3
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- 230000000153 supplemental Effects 0.000 claims 1
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- 238000005516 engineering process Methods 0.000 abstract description 8
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- 241000233647 Phytophthora nicotianae var. parasitica Species 0.000 abstract 1
- 238000011065 in-situ storage Methods 0.000 abstract 1
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- 238000003709 image segmentation Methods 0.000 description 3
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- 230000002265 prevention Effects 0.000 description 1
Classifications
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01M—CATCHING, TRAPPING OR SCARING OF ANIMALS; APPARATUS FOR THE DESTRUCTION OF NOXIOUS ANIMALS OR NOXIOUS PLANTS
- A01M1/00—Stationary means for catching or killing insects
- A01M1/02—Stationary means for catching or killing insects with devices or substances, e.g. food, pheronones attracting the insects
- A01M1/026—Stationary means for catching or killing insects with devices or substances, e.g. food, pheronones attracting the insects combined with devices for monitoring insect presence, e.g. termites
Abstract
The invention provides a machine vision technology based pre-warning system and method for pest and disease damage of crops in greenhouses. According to the system, image monitoring data and environment parameters under the natural background are acquired by an in-situ Canon EOS7D camera image sensor and multiple environmental sensors, a dual pest and disease damage pre-warning system realizing warning source pre-warning and warning sign pre-warning is constructed, and meanwhile, an information processing function that a user can inquire pre-warning records at any time is realized. Real-time environmental parameters and environmental parameters suitable for crop growth are matched firstly to provide preliminary pre-warning, real-time image data are processed through filtering, segmentation, feature extraction and identification and classification with an image processing technology in machine vision, so that tomato early blight and leaf mold are automatically identified and classified, the pest and disease damage morbidity situation of crops at present is provided, and decision support and historical information record inquiry functions are supplied to the user. Manpower and material resources are greatly reduced, and meanwhile, the pest and disease damage management efficiency of the crops in the greenhouse is further improved.
Description
Technical field
The present invention relates to a kind of pest and disease damage early warning system, be specifically related to a kind of pest and disease damage early warning system based on machine vision
And method.
Background technology
In industrialization, informationization, urbanization deeply develop, simultaneously advance agricultural modernization, be the one of " 12 " period
Item significant task." 12 " are the critical periods built a well-off society in an all-round way, and being that in-depth reform is open, accelerate to change economy sends out
The crucial period of exhibition mode, is to speed up the important opportunity period of developing modern agriculture.
Digital Agriculture and precision operation are direction and the requirements of modern agricultural development.In crop state of an illness insect pest situation analysis side
Face, Digital Agriculture requires to obtain quickly and accurately the information that plant is infected by pest and disease damage, thus instructs in growing process
Fine-grained management.
In the world, Japan in 20 end of the centurys, in the facilities horticulture field of technology-intensive type, to develop multiple pest and disease damage pre-
Alarm system, not only saves manpower and materials, also substantially increases prevention and control of plant diseases, pest control effect；Holland's agricultural environment Graduate School of Engineering is opened
The expert system of the pest and disease damage early warning sent out, uses image processing techniques and expert system technology to combine, achieves in application aspect
Good result；And the warmhouse booth of the country such as the U.S., Britain combines machine vision technique while using intelligence control system
Rating scale, the scope of pest and disease damage has been carried out early warning.
The scab that the Major Difficulties of diseases and pests of agronomic crop early warning at present is under complex background to be completed is split and uses
A kind of discrimination is higher but recognizer that amount of calculation is less.But currently advanced greenhouse computer monitoring early warning system still relies on
External importing technology, core technology is still grasped in abroad.Therefore diseases and pests of agronomic crop warning aspect in greenhouse
Research remains a need for further making great efforts and innovating.
Summary of the invention
The invention aims to the defect solving that in prior art, diseases and pests of agronomic crop is realized early warning, it is provided that a kind of
Pest and disease damage early warning system based on machine vision and method, be core by the otsu partitioning algorithm improved and KNN intelligent classification algorithm
The heart solves the problems referred to above.
To achieve these goals, insight of the invention is that
Pest and disease damage early warning system based on machine vision is collectively constituted by two parts of software and hardware, again may be used according to function difference
This system is divided image and environmental parameter acquisition system and image processing system；Wherein, image and ambient parameter collection system
Unite and obtained image prison by on-the-spot Canon's EOS7D types of cameras and multiple environmental sensor
Survey data and ambient parameter, then data transmission cause image processing system is processed accordingly；Image processing system by
Alert source early warning and alert million early warning and information processing form.Alert source early warning is by by real-time site environment parameter and according to expert
The ambient parameter of the suitable crop growth provided is compared, and carries out the preliminary early warning of pest and disease damage；Alert million early warning are to utilize machine
The view data of spot sensor collection is processed by device visual pattern treatment technology, it is judged that the classification of pest and disease damage；At information
Reason realizes the result to system and relative recording write into Databasce, provides query function simultaneously.Each part is respectively arranged with it
Task, modules software kit is integrated into an organic whole.
Described image (is mainly measured by Canon's EOS7D camera and environmental parameter sensor with environmental parameter acquisition system
Parameters of temperature, humidity, light intensity) composition；The data that image is measured with data collecting system pass to figure by the way of being wirelessly transferred
As processing system.
Described Canon EOS7D camera major parameter index is: camera lens model: EF-S 18-135mm f/3.5-5.6
IS；Real focal length: f=18-135mm；Size sensor APS picture (22.3*14.9mm)；Highest resolution 5184 × 3456；Shadow
As processor DIGIC 4+DIGIC 4；View finder: (type: eye flat pentaprism visual field rate: vertical/horizontal direction about 100%；Put
Big multiplying power: about 1.0 times (-1m uses 50mm camera lens to focus unlimited distance)；Eyespot: about 22mm(from eyepiece lens central authorities-
1m) built-in diopter regulation :-3.0-+1.0m(dpt)；Focusing screen: fixed；Composition assists: grid lines and electronic level；
Reflective mirror: fast hollow depth of field previewing).
Described image processing system is formed with alert million early warning and information processing by warning source early warning；First carried out by alert source early warning
Preliminary early warning, then pest and disease damage is identified by warning million early warning and classifies.It is equipped with the information processing for query history record simultaneously
Function.
Described alert source early warning is to be obtained ambient parameter (temperature DEG C, humidity %, light intensity by on-the-spot multiple environmental sensor
Lux), the ambient parameter of real time environment parameter with the suitable crop growth provided according to expert is compared, gives timely
Go out early warning information, just can the generation of pre-preventing disease and pest before the most not falling ill.
Described police million early warning is by image filtering, image segmentation, feature extraction, morphological operation, image sharpening, training
Self-learning module forms.Described police million early warning is the core of whole system, it is achieved the Classification and Identification to pest and disease damage, directly affects
The early warning effect of system.
Described image filtering mainly has the filtering modes such as medium filtering, mean filter, KNN smothing filtering, threshold filter.
Thus realize image gathering, quantify, transmit during the noise of generation be filtered, to reduce noise to successive image
The impact processed and identify.
Described image segmentation mainly has the segmentation of OTSU automatic threshold segmentation, colour, automatic threshold segmentation, One-Dimensional Maximum-Entropy
The partitioning schemes such as method segmentation.Realize image after pretreatment is split, be partitioned into complete scab, for subsequent extracted feature
Preparing, it is the most abundant that image segmentation the most completely will directly affect extraction feature.
Scab pattern feature (perimeter L, area S, circularity C, complexity E) and color are mainly extracted in described feature extraction
Feature (tone H, color saturation S, brightness I), these features well have expressed the substantive characteristics of every kind of scab, the spy obtained
Levying parameter is the important evidence differentiating scab kind.
Described image sharpening mainly has Laplace operator, prewitt operator, gradient operator, kirsch operator, sobel
Operator.In order to the segmentation of beneficially image can take different sharpening modes, segmentation is made to reach best effect.
Described morphological operation Main Means has out operation and closed operation.Morphological operation is primarily directed to segment
Image, takes suitable morphological operation according to the effect segmented, in order to extract feature.
Described training is learnt by oneself module and is mainly comprised training and identify two modules.What training module was taked has supervision instruction
Practice, before identification, i.e. take the view data input system of substantial amounts of known class, set up feature database.The process identified i.e. inputs
Image to be identified differentiates image kind with the class libraries of known feature according to Intelligent Recognition algorithm.
The described information processing function makes software directly be connected with database (SQL serve 2008), it is achieved to system
Result and relative recording write into Databasce, provide query function so that user can be with real-time query pest and disease damage simultaneously
History processes record, understands morbidity course.
Conceiving according to foregoing invention, the present invention uses following technical proposals:
A kind of pest and disease damage early warning system based on machine vision and method, it is characterised in that comprise Canon's EOS7D camera (1) peace
It is loaded on fixed position, warmhouse booth corner, for the temperature image data acquiring of indoor crops, ambient parameter integrated sensor (2) peace
It is loaded on warmhouse booth top, is positioned at greenhouse central authorities for the various environmental parameters data acquisition of indoor, greenhouse, image processing system (3)
In control room, it is used for the view data gathered in greenhouse and ambient parameter data processes, record.Canon's EOS7D camera
(1) and ambient parameter integrated sensor (2) connect image processing system；
Described Canon EOS7D camera (1) major parameter index is: valid pixel 18,000,000；Real focal length: f=18-135mm；Literary composition
Part form: JPEG, RAW (14), can record RAW+JPEG simultaneously.
Described ambient parameter integrated sensor (2) is formed by temperature, humidity, light intensity various environmental parameters set of sensors；
Various ambient parameters in measuring greenhouse, utilize wireless network transmissions to image processing system (3), are used for warning source early warning
Foundation.
Described image processing system (3) specifically includes:
Alert source warning module (3-1), obtains ambient parameter, real time environment parameter and farming according to on-the-spot multiple environmental sensor
Thing suitable growth ambient parameter carries out coupling and provides preliminary early warning, and at software interface to information warning.
Alert million warning modules (3-2), on the basis of alert source early warning, obtain picture number according to on-the-spot Canon EOS7D camera
According to, utilize image processing techniques in machine vision that view data is processed.By pretreatment, feature extraction, to pest and disease damage
Kind is identified classification；
Message processing module (3-3), result and the relative recording write into Databasce to system, also provide for inquiring about merit simultaneously
Can so that user's real-time query pest and disease damage history processes record, understand morbidity course.
A kind of pest and disease damage early warning system based on machine vision and method, use said system to operate, and its feature exists
As follows in operating procedure:
Step 1: Canon's EOS7D camera (1) gathered a secondary data with ambient parameter integrated sensor (2) every 3 hours, and led to
Cross wireless network and send data to image processing system (3).
Step 2: in image processing system (3), alert source warning module (3-1) starts, by the ambient parameter number of collection in worksite
Mate according to pre-set expertise numerical value, it may be judged whether in the reasonable scope.
Step 3: if ambient parameter data is the most in the reasonable scope, then start alert million warning modules (3-2).For Canon
The view data that EOS7D camera (1) gathers carries out image procossing, has discriminated whether pest and disease damage, and the class of pest and disease damage further
Not.And provide early warning information in time.
Step 4: message processing module (3-3), for alert source warning module (3-1) and the place of alert million warning modules (3-2)
Reason data and result write into Databasce are to be checked.
The present invention compared with prior art, has and the most obviously highlights substantive distinguishing features and remarkable advantage:
The pest and disease damage early warning by machine vision technique application with crops of novelty of the present invention, uses advanced person's the most at the scene
Sensor replaces manual detection and achieves the pest and disease damage intellectuality early warning of two dimensions from environmental change to aura, and is dividing
Cut and make optimization on algorithm and Intelligent Recognition algorithm, improve promptness and the accuracy of early warning, significantly have updated the efficiency of management
Pattern.
Accompanying drawing explanation
Fig. 1 is disease pest early warning system structural representation
Fig. 2 image and environmental parameter acquisition system
Fig. 3 is alert source early warning runnable interface figure
Fig. 4 is alert million early warning runnable interface figures
Fig. 5 is image filtering, segmentation, feature extraction surface chart
Fig. 6 is image recognition early warning surface chart
Fig. 7 is that system information processes surface chart.
Detailed description of the invention
By making the early warning architectural feature to the present invention and effect of being reached have a better understanding and awareness, in order to excellent
The embodiment of choosing and accompanying drawing coordinate detailed description, are described as follows:
Embodiment one:
Visit Fig. 1 ~ ~ Fig. 7, this pest and disease damage early warning system based on machine vision and method, it is characterised in that comprise Canon
EOS7D camera (1) is installed on fixed position, warmhouse booth corner, for the temperature image data acquiring of indoor crops, ambient parameter
Integrated sensor (2) is installed on warmhouse booth top, for the various environmental parameters data acquisition of indoor, greenhouse, image processing system
(3) greenhouse central control room it is positioned at, for the view data and the ambient parameter data that gather in greenhouse are processed, remembered
Record.Canon's EOS7D camera (1) and ambient parameter integrated sensor (2) connect image processing system；
Embodiment two: the present embodiment is essentially identical with embodiment one, and special feature is as follows:
Described Canon EOS7D camera (1) major parameter index is: valid pixel 18,000,000；Real focal length: f=18-135mm；Literary composition
Part form: JPEG, RAW (14), can record RAW+JPEG simultaneously.
Described ambient parameter integrated sensor (2) is formed by temperature, humidity, light intensity various environmental parameters set of sensors；
Various ambient parameters in measuring greenhouse, utilize wireless network transmissions to image processing system (3), are used for warning source early warning
Foundation.
Described image processing system (3) specifically includes:
Alert source warning module (3-1), obtains ambient parameter, real time environment parameter and farming according to on-the-spot multiple environmental sensor
Thing suitable growth ambient parameter carries out coupling and provides preliminary early warning, and at software interface to information warning.
Alert million warning modules (3-2), on the basis of alert source early warning, obtain picture number according to on-the-spot Canon EOS7D camera
According to, utilize image processing techniques in machine vision that view data is processed.By pretreatment, feature extraction, to pest and disease damage
Kind is identified classification；
Message processing module (3-3), result and the relative recording write into Databasce to system, also provide for inquiring about merit simultaneously
Can so that user's real-time query pest and disease damage history processes record, understand morbidity course.
Embodiment three:
This pest and disease damage early warning system based on machine vision and method, use said system to operate, it is characterised in that operation
Step is as follows:
Step 1: Canon's EOS7D camera (1) gathered a secondary data with ambient parameter integrated sensor (2) every 3 hours, and led to
Cross wireless network and send data to image processing system (3).
Step 2: in image processing system (3), alert source warning module (3-1) starts, by the ambient parameter number of collection in worksite
Mate according to pre-set expertise numerical value, it may be judged whether in the reasonable scope.
Step 3: if ambient parameter data is the most in the reasonable scope, then start alert million warning modules (3-2).For Canon
The view data that EOS7D camera (1) gathers carries out image procossing, has discriminated whether pest and disease damage, and the class of pest and disease damage further
Not.And provide early warning information in time.
Step 4: message processing module (3-3), for alert source warning module (3-1) and the place of alert million warning modules (3-2)
Reason data and result write into Databasce are to be checked.
Shown in Fig. 1 ~ ~ Fig. 7, this pest and disease damage early warning system based on machine vision and method are mainly by image and ambient parameter
Acquisition system and image processing system (mainly comprising the early warning of alert source, alert million early warning, information processing three part composition), each function mould
The mutually complementary overall work task of having arranged in pairs or groups of block.
First, when running software, system calls the site environment data parameters that in database, on-the-spot each sensor is passed back
(temperature DEG C, humidity %, light intensity Lux), and show that now program backstage is existing these on the interface of alert source early warning as shown in Figure 3
The suitable ambient parameter excursion that the ambient parameter of field is suitable with the crops according to expert advice is compared, it is judged that this
Time ambient parameter whether in suitable scope, and provide corresponding treatment advice, and show on the interface of alert source early warning.This
Sample realizes the preliminary early warning to pest and disease damage early warning, prevents before morbidity.
On alert million interfaces, the view data parameter at the system scene of calling shows on interface (as shown in Figure 4) and carries out
Corresponding process.As it is shown in figure 5, image is via medium filtering, and split with OTSU automatic threshold segmentation algorithm, divided
The complete scab cut.On this basis, program utilizes the full screen scanning to image to extract scab pattern feature (girth simultaneously
L, area S, circularity C, complexity E) and color characteristic (tone H, color saturation S, brightness I), and this characteristic parameter is shown in real time
Show on software interface (as shown in Figure 5), and by arest neighbors sorting algorithm (kNN) scab is identified classification, and be given and sentence
Other result (as shown in Figure 5).And in the write into Databasce during fructufy that these process, in order to follow-up inquiry and management.
On the interface of information processing, process record and the relevant parameter of recent system can be shown as shown in Figure 7 in real time,
Can also from database the record of query history, it is simple to complete monitoring and subsequent calls related data for pest and disease damage enter
Row research.
At the scene under the cooperation of sensor, native system achieves from alert source early warning to alert million early warning dual pest and disease damage early warning,
Be equipped with offer user can be with the information processing function of real-time query early warning record simultaneously.First fitted with crops by real time environment parameter
Preferably growing environment parameter carries out coupling and provides preliminary early warning, then uses in machine vision image processing techniques to realtime graphic number
According to carrying out processing (filter, split, feature extraction, identification classification), it is achieved to early blight of tomato and leaf mold automatically identify with
Classification, provides the pest and disease damage incidence of current crops, provides the user decision support and historical information record queries.The biggest
Decrease greatly manpower and materials, improve again the efficiency of crops in greenhouse pest management simultaneously.
The general principle of the present invention, principal character and advantages of the present invention have more than been shown and described.The technology of the industry
The personnel simply present invention it should be appreciated that the present invention is not restricted to the described embodiments, described in above-described embodiment and specification
Principle, the present invention also has various changes and modifications without departing from the spirit and scope of the present invention, these change and
Improvement both falls within the range of claimed invention.The protection domain of application claims by appending claims and
Equivalent defines.
Claims (5)
1. a pest and disease damage early warning system based on machine vision and method, it is characterised in that comprise Canon's EOS7D camera (1),
Ambient parameter integrated sensor (2), image processing system (3)；It is characterized in that,
Described Canon EOS7D camera, (1) is installed on fixed position, warmhouse booth corner, for the view data of temperature indoor crops
Gather；
Described ambient parameter integrated sensor, (2) are installed on warmhouse booth top, for indoor, greenhouse various environmental parameters data
Gather；
Described image processing system, (3) are positioned at greenhouse central control room, for the view data gathered in greenhouse and environment
Supplemental characteristic carries out processing, record；
Described Canon's EOS7D camera (1) and ambient parameter integrated sensor (2) connect image processing system.
Pest and disease damage early warning system based on machine vision the most according to claim 1, is characterized in that, described Canon
EOS7D camera (1) major parameter index is: valid pixel 18,000,000；Real focal length: f=18-135mm；File format: JPEG,
RAW (14), can record RAW+JPEG simultaneously.
Pest and disease damage early warning system based on machine vision the most according to claim 1, is characterized in that, described ambient parameter
Integrated sensor (2) is formed by temperature, humidity, light intensity various environmental parameters set of sensors；Various in measuring greenhouse
Ambient parameter, utilizes wireless network transmissions to image processing system (3), is used for warning the foundation of source early warning.
Pest and disease damage early warning system based on machine vision the most according to claim 1, it is characterised in that described image procossing
System (3) specifically includes:
Alert source warning module (3-1), obtains ambient parameter, real time environment parameter and farming according to on-the-spot multiple environmental sensor
Thing suitable growth ambient parameter carries out coupling and provides preliminary early warning, and at software interface to information warning；
Alert million warning modules (3-2), on the basis of alert source early warning, obtain view data, profit according to on-the-spot Canon EOS7D camera
By image processing techniques in machine vision, view data is processed, by pretreatment, feature extraction, pest species is entered
Row identifies classification；
Message processing module (3-3), result and the relative recording write into Databasce to system, also provide for inquiring about merit simultaneously
Can so that user's real-time query pest and disease damage history processes record, understand morbidity course.
5. pest and disease damage early warning system based on machine vision and a method, use according to claim 1 based on machine
The pest and disease damage early warning system of vision operates, it is characterised in that operating procedure is as follows:
Step 1: Canon's EOS7D camera (1) gathered a secondary data with ambient parameter integrated sensor (2) every 3 hours, and passed through
Wireless network sends data to image processing system (3)；
Step 2: in image processing system (3), alert source warning module (3-1) starts, by the ambient parameter data of collection in worksite with
Pre-set expertise numerical value mates, it may be judged whether in the reasonable scope；
Step 3: if ambient parameter data is the most in the reasonable scope, then start alert million warning modules (3-2), for Canon EOS7D
The view data that camera (1) gathers carries out image procossing, has discriminated whether pest and disease damage, and the classification of pest and disease damage further, and
Provide early warning information in time；
Step 4: message processing module (3-3), for alert source warning module (3-1) and the process number of alert million warning modules (3-2)
According to and result write into Databasce to be checked.
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| CN106779188A (en) * | 2016-11-30 | 2017-05-31 | 深圳前海弘稼科技有限公司 | Plant pest Forecasting Methodology and device in a kind of plantation equipment |
| CN106780443A (en) * | 2016-11-30 | 2017-05-31 | 深圳前海弘稼科技有限公司 | The method for early warning of crop pest and the prior-warning device of crop pest |
| CN106846462A (en) * | 2016-12-30 | 2017-06-13 | 北京农业信息技术研究中心 | Insect identifying device and method based on three-dimensional simulation |
| CN107168367A (en) * | 2017-06-08 | 2017-09-15 | 中科院合肥技术创新工程院 | Accurate pesticide spraying system based on machine vision industry |
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| CN107301605A (en) * | 2017-06-29 | 2017-10-27 | 深圳前海弘稼科技有限公司 | The control method and device of crop pest |
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| CN110427922A (en) * | 2019-09-03 | 2019-11-08 | 陈�峰 | One kind is based on machine vision and convolutional neural networks pest and disease damage identifying system and method |
| IT201900002249A1 (en) * | 2019-02-15 | 2020-08-15 | Agrorobotica S R L | System and method of predicting the risk of insect attack |
| CN112034912A (en) * | 2020-09-03 | 2020-12-04 | 山东锋士信息技术有限公司 | Greenhouse crop disease control method based on real-time feedback |
| CN112055079A (en) * | 2020-09-03 | 2020-12-08 | 华艺生态园林股份有限公司 | Disease and pest monitoring and early warning system based on cloud computing platform |
| CN114342717A (en) * | 2021-12-27 | 2022-04-15 | 深圳市金阳盛城市服务集团有限公司 | Insect pest detecting and reporting method, device, equipment and storage medium |
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