CN107844771A - Method, system, computer installation and the storage medium of crop production management - Google Patents
Method, system, computer installation and the storage medium of crop production management Download PDFInfo
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- CN107844771A CN107844771A CN201711071686.3A CN201711071686A CN107844771A CN 107844771 A CN107844771 A CN 107844771A CN 201711071686 A CN201711071686 A CN 201711071686A CN 107844771 A CN107844771 A CN 107844771A
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Abstract
The invention discloses a kind of method of crop production management, system, computer installation and storage medium.In the present invention, crop growth figure in multiple predeterminable area positions that the growth period of crops passes through multiple cameras collection planting area, each crop growth figure is contrasted to determine the value of chromatism of each predeterminable area position with the crop growth standard drawing of the corresponding predeterminable area position prestored in computer installation, and determine aberration average value, default aberration grade according to belonging to aberration average value, it is determined that default ratio of nutrient solution corresponding with default aberration grade, obtain the current ratio of nutrient solution of crops, if current ratio of nutrient solution is different from default ratio of nutrient solution, the current ratio of nutrient solution of crops is then adjusted to identified default ratio of nutrient solution.The present invention can realize the adjustment to the ratio of nutrient solution of crops by image comparison, improve the efficiency of crop production management, reduce the cost of crop production management.
Description
Technical field
The invention belongs to agricultural production, more particularly to a kind of method of crop production management, system, computer to fill
Put and storage medium.
Background technology
Agricultural is the importance of Chinese national economy, and as population is more and more, production estimation and agricultural are provided
The requirement of the management in source also more and more higher.As information science technology develops, modern agriculture is from traditional rough type management to letter
Breathization and scientific management change, and form a set of science, automation novel crops production management system.But mesh
Preceding Management System of Crop Production andIts still has many irrational problems.For example, the resource such as irrigation, chemical fertilizer and agricultural chemicals
Application method is still excessively rough, easily causes the waste of agricultural production resources, thus causes the efficiency of management of production estimation low
Under, production cost it is high.
Therefore, there is the problem of efficiency of management is low, production cost is high in existing Management System of Crop Production andIts.
The content of the invention
The present invention provides a kind of method of crop production management, system, computer installation and storage medium, it is intended to solves
The problem of efficiency of management existing for existing Management System of Crop Production andIts is low, production cost is high.
First aspect present invention provides a kind of method of crop production management, applied in computer installation, crops
Planting area the multiple cameras for the computer installation communicate connection are installed, methods described includes:
In the growth period of the crops, pass through multiple predeterminable areas of the multiple camera collection planting area
The crop growth figure of position;
By the crop growth figure of each predeterminable area position with prestored in the computer installation it is corresponding described in
The crop growth standard drawing of predeterminable area position is contrasted to determine the value of chromatism of corresponding each predeterminable area position, and really
Fixation difference average value;
Default aberration grade according to belonging to the aberration average value, it is determined that corresponding with the default aberration grade preset
Ratio of nutrient solution;
Obtain the current ratio of nutrient solution of the crops;
If the current ratio of nutrient solution of the crops is different from the identified default ratio of nutrient solution, institute is adjusted
The current ratio of nutrient solution of crops is stated to the identified default ratio of nutrient solution.
In a preferably embodiment, methods described also includes:
Corresponding with the default aberration grade anchor a tent the default aberration grade belonging to the aberration average value and in advance
Nutrient solution proportioning, which is shown in, with the computer installation communicate in the management terminal of connection.
In a preferably embodiment, methods described also includes:
If the default aberration grade described in the aberration average value exceedes predetermined threshold value, send warning information to it is described
Computer installation communicate the management terminal of connection.
In a preferably embodiment, pest and disease damage identification model storehouse, methods described are stored with the computer installation
Also include:
Disease pest is carried out to the crop growth figure of the multiple predeterminable area position according to the pest and disease damage identification model storehouse
Evil identification;
Determine pest and disease damage recognition result and prevention and controls corresponding with the pest and disease damage recognition result;
The pest and disease damage recognition result and the prevention and controls are included being connected carrying out communication with the computer installation
Management terminal on.
In a preferably embodiment, it is described according to the pest and disease damage identification model storehouse to the multiple predeterminable area position
Crop growth figure carry out pest and disease damage identification include:
Extract the scab feature of each crop growth figure;
Pest and disease damage identification is carried out to the scab feature according to the pest and disease damage identification model.
In a preferably embodiment, the scab feature includes scab color characteristic, scab textural characteristics and scab
Shape facility.
In a preferably embodiment, the scab feature of each crop growth figure of extraction includes:
The crop growth figure is pre-processed, and obtains the scab image in the crop growth figure;
Extract the scab feature in the scab image.
Second aspect of the present invention provides a kind of system of crop production management, applied in computer installation, crops
Planting area the multiple cameras for the computer installation communicate connection are installed, the system includes:
Acquisition module, for the growth period in the crops, the planting area is gathered by the multiple camera
Multiple predeterminable area positions crop growth figure;
Aberration average value determining module, for the crop growth figure of each predeterminable area position and the computer to be filled
The crop growth standard drawing of the correspondence predeterminable area position prestored in putting is contrasted corresponding each pre- to determine
If the value of chromatism of regional location, and determine aberration average value;
Default ratio of nutrient solution determining module, for the default aberration grade according to belonging to the aberration average value, it is determined that
Default ratio of nutrient solution corresponding with the default aberration grade;
Acquisition module, the ratio of nutrient solution current for obtaining the crops;
Adjusting module, if the ratio of nutrient solution current for the crops and the identified default ratio of nutrient solution
Difference, then the current ratio of nutrient solution of the crops is adjusted to the identified default ratio of nutrient solution.
Third aspect present invention provides a kind of computer installation, and the computer installation includes processor, the processor
The method that crop production management described in any of the above-described embodiment is realized during for performing the computer program stored in memory.
Fourth aspect present invention provides a kind of computer-readable recording medium, is stored thereon with computer program, the meter
The method that calculation machine program realizes crop production management described in any of the above-described embodiment when being executed by processor.
In the present invention, in the growth period of crops, multiple predeterminable areas that multiple cameras gather planting area are passed through
The crop growth figure of position, pair that will be prestored in the crop growth figure of each predeterminable area position and computer installation
The crop growth standard drawing of predeterminable area position is answered to be contrasted to determine the value of chromatism of corresponding each predeterminable area position, and
Aberration average value is determined, the default aberration grade according to belonging to aberration average value, it is determined that corresponding with default aberration grade preset
Ratio of nutrient solution, obtain the current ratio of nutrient solution of crops, if the current ratio of nutrient solution of crops with it is identified default
Ratio of nutrient solution is different, then adjusts the current ratio of nutrient solution of crops to identified default ratio of nutrient solution.The present invention can
With the current ratio of nutrient solution of the default aberration level adjustment crops belonging to the aberration average value according to the crop growth phase extremely
Default ratio of nutrient solution corresponding with the default aberration grade.Therefore, the present invention can improve the effect of crop production management
Rate, reduce the cost of crop production management.
Brief description of the drawings
Technical scheme in order to illustrate the embodiments of the present invention more clearly, make required in being described below to embodiment
Accompanying drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the present invention, for
For those skilled in the art, on the premise of not paying creative work, it can also be obtained according to these accompanying drawings other attached
Figure.
Fig. 1 is the implementation process figure of the method for crop production management provided in an embodiment of the present invention;
Fig. 2 is the functional block diagram of the system of crop production management provided in an embodiment of the present invention;
Fig. 3 is the structural representation of computer installation provided in an embodiment of the present invention.
Main element symbol description
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, it is right below in conjunction with drawings and Examples
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.
Fig. 1 shows the implementation process of the method for crop production management provided in an embodiment of the present invention, according to different
Demand, the order of step can change in the flow chart, and some steps can be omitted.For convenience of description, illustrate only and this
The related part of inventive embodiments, details are as follows:
As shown in figure 1, the method for crop production management, applied in computer installation 1.Agriculture in the embodiment of the present invention
Crop management is carried out in greenhouse, and the planting area of crops is provided with and communicated with the computer installation 1
Multiple cameras 2 of connection, as shown in figure 3, only exemplary in figure 3 herein draw two cameras 2.Wherein, the agriculture
The industrial crops such as crop can be cereal crops, can also be vegetable crop, oil crops and melon and fruit flowers, the vegetable
The green vegetables such as the dish crop roots vegetable such as including radish, spinach, celery and crowndaisy chrysanthemum, Chinese cabbage group, wild cabbage, cauliflower etc.
The tuber and tuberous rooted vegetables such as mustard vegetables, potato, Chinese yam such as cabbage vegetable, water east leaf mustard, stem mustard and leaf mustard, kind
The solanaceous vegetableses such as eggplant, eggplant and capsicum;The vegetable crop also include melon vegetables, legume vegetable, aquatic vegetable and
Edible fungi etc..The multiple camera 2 passes through wired or wirelessly carry out communication with the computer installation 1 and be connected.
Step S101, in the growth period of the crops, the planting area are gathered by the multiple camera more
The crop growth figure of individual predeterminable area position.
The multiple camera 2 is pre-installed in the more of the planting area of crops according to default position and angle respectively
Individual different position and angle, in embodiments of the present invention, the quantity of the multiple camera 2 is 6, the multiple camera
2 quantity and the position of placement and angle can also modify according to being actually needed with the demand of production management.It is multiple not
It can be respectively top-left corner, upper right angle, side upper angle, side lower angle, left side angle and right-hand corner etc. with angle, also may be used
To set other angles in addition to above-mentioned several angles.The multiple camera 2 installation good position and set angle it
The planting area of the crops of its collection is fixed afterwards.Therefore, can be with order to which the growth to crops is controlled and is managed
In the growth period of the crops, the proportion of crop planting region are gathered by the multiple camera 2 pre-set more
The crop growth figure of individual predeterminable area position.
Wherein, the predeterminable area position is associated with position that the camera 2 is installed and the angle set, is imaging
Behind first 2 position and angle-determining, the planting area that camera 2 gathers is fixed, and the predeterminable area position is can root
Certain subregion position of the crops pre-set according to being actually needed with production management demand, alternatively, it is also possible to pass through adjustment
The predeterminable area position that the position of camera 2 and angulation change camera 2 gather.
Step S102, by what is prestored in the crop growth figure of each predeterminable area position and the computer installation
The crop growth standard drawing of the corresponding predeterminable area position is contrasted to determine the color of corresponding each predeterminable area position
Difference, and determine aberration average value.
, will be each pre- after the crop growth figure of multiple different predeterminable area positions of crop growth phase is got
If the crop growth figure of regional location and the corresponding each predeterminable area position prestored in computer installation 1
Crop growth standard drawing contrasted.Wherein, the crop growth standard drawing that is prestored in computer installation 1 and logical
The crop growth figure for crossing multiple different predeterminable area positions that camera 2 collects is corresponding, i.e., corresponding crops
The predeterminable area position in regional location and crop growth figure in growth standard figure is identical regional location.
For the crop growth standard drawing pre-set in the computer installation, in the growth period of crops, lead to
Growth figure of each camera 2 in the multiple crops of predeterminable area station acquisition is crossed, and it is therefrom true according to the selection of user
The ideal growth figure of fixed each predeterminable area position crop growth is as the crop growth standard being subsequently compared
Figure.In other examples, the crop growth standard drawing can also be pre-set by other means, for example, user with
Micro-judgment, when crop growth state is optimal shooting obtained by image or picture, shooting crops growth
Figure after editing and processing etc. is carried out on the basis of figure.
Preset for example, the crop growth standard drawing in the growth period prestored in computer installation 1 is camera 2
Position and the crop growth standard drawing of 6 predeterminable area positions of angle acquisition, then, will be described more in the growth period of crops
Individual camera 2 is pre-set according to the predeterminated position and angle, and gathers 6 crop growths of the crops in growth period
Figure.6 will prestored in collected in the growth period of the crops 6 crop growth figures and computer installation 1
Crop growth standard drawing is compared respectively, it is determined that the value of chromatism of each predeterminable area position, that is, determine each crops
Growth figure and the value of chromatism of corresponding crop growth standard drawing, so far can determine 6 values of chromatism:Value of chromatism 1, value of chromatism 2,
Value of chromatism 3, value of chromatism 4, value of chromatism 5 and value of chromatism 6.After above-mentioned 6 values of chromatism are determined, then determine above-mentioned 6 aberration
The aberration average value of value, i.e.,:Aberration average value=(value of chromatism 1+ value of chromatism 2+ value of chromatism 3+ value of chromatism 4+ value of chromatism 5+ values of chromatism
6)/6, you can to obtain aberration average value of the crops in growth period.Above-mentioned value of chromatism can utilize color of the prior art
Poor computational methods are determined, and are no longer described in detail herein.
Step S103, the default aberration grade according to belonging to the aberration average value, according to belonging to the aberration average value
Default aberration grade, it is determined that default ratio of nutrient solution corresponding with the default aberration grade.
After above-mentioned aberration average value is got, the default aberration grade belonging to the aberration average value is determined.It is described
Default aberration grade is the aberration grade pre-set, its be a range of aberration value range is divided into advance it is multiple etc.
Grade, the number of degrees at interval and the default aberration grade pre-set between each default aberration grade specifically divided can root
According to free setting is actually needed, special limitation is not done herein.For example, aberration scope is divided into five aberration sections, it is corresponding
The default aberration grade of setting five, according to the aberration section where the aberration average value in the growth period of above-mentioned determination, determine color
Default aberration grade belonging to poor average value, it is determined that default aberration grade belonging to aberration average value and then row determine with
The default aberration grade presets ratio of nutrient solution accordingly.The default ratio of nutrient solution is pre-setting with the aberration
The corresponding ratio of nutrient solution of grade.It is corresponding for the first default ratio of nutrient solution, the second aberration grade corresponding to first aberration grade
For second default ratio of nutrient solution etc. by that analogy.Specifically can be according to the growth characteristics and nutrient solution demand in growth period
Setting meets the default aberration grade in growth period and default ratio of nutrient solution, the accordingly default aberration grade and default nutrition
Liquid proportioning with the demand of production management can also set and change accordingly according to being actually needed.It should be noted that
In embodiments of the invention, the determination of aberration and the determination of aberration grade show that the growth of the crops and standard growth are present
A certain distance, therefore, it is necessary to the control to the crop growth is realized by the corresponding adjustment of following ratio of nutrient solution.
Step S104, obtain the current ratio of nutrient solution of the crops.
In order to determine whether the current growth of the crops is in preferable or standard growth conditions, in this step
The current ratio of nutrient solution of the crops is obtained, so as to which the growth conditions of the crops are monitored and managed.
Step S105, if the current ratio of nutrient solution of the crops and the identified default ratio of nutrient solution are not
Together, then the current ratio of nutrient solution of the crops is adjusted to the identified default ratio of nutrient solution.
It is understood that the default ratio of nutrient solution for promote crops described in current growth phase reach it is preferable or
Ratio of nutrient solution required for the growth conditions of person's standard, if the current ratio of nutrient solution of the crops got and according to
The default ratio of nutrient solution that the aberration average value determines is different, then illustrates the current growth conditions of the crops and do not meet
Preferable or standard growth conditions, now need the ratio of nutrient solution current to the crops to be adjusted, with right
The growth conditions of the crops are controlled and managed.
For example, it is assumed that the default aberration grade belonging to aberration average value of the crops determined in growth period is second
Default aberration grade, ratio of nutrient solution of presetting corresponding with the described second default aberration grade is the second default ratio of nutrient solution,
If the current ratio of nutrient solution of the crops is different from the described second default ratio of nutrient solution, illustrate the life of the crops
It is long and preferably or comparatively slight deviation occur in the growth conditions of standard, then need current to the crops
Ratio of nutrient solution carries out relatively small adjustment, will the current ratio of nutrient solution of the crops adjust to identified described
Second default ratio of nutrient solution, so that the growth to the crops is controlled and is managed.
Assuming that the default aberration grade belonging to aberration average value of the crops determined in growth period is default for the 4th
Aberration grade, ratio of nutrient solution of presetting corresponding with the described 4th default aberration grade is the 4th default ratio of nutrient solution, if institute
State the current nutrient solution of crops match somebody with somebody and the described 4th preset ratio of nutrient solution it is different, then illustrate growth and the reason of the crops
There is comparatively serious deviation in growth conditions think or standard, then need the nutrient solution current to the crops
Proportioning carries out relatively large adjustment, will the current ratio of nutrient solution of the crops adjust to the described 4th default nutrient solution
Proportioning, so that the growth to the crops is controlled and is managed.
In embodiments of the present invention, in the growth period of crops, pass through multiple cameras 2 and gather the multiple pre- of planting area
If the crop growth figure of regional location, by the crop growth figure of each predeterminable area position with being deposited in advance in computer installation
The crop growth standard drawing of the corresponding predeterminable area position of storage is contrasted to determine the color of corresponding each predeterminable area position
Difference, and aberration average value is determined, the default aberration grade according to belonging to aberration average value, it is determined that corresponding to default aberration grade
Default ratio of nutrient solution, the current ratio of nutrient solution of crops is obtained, if the current ratio of nutrient solution of crops is with being determined
Default ratio of nutrient solution it is different, then adjust the current ratio of nutrient solution of crops to identified default ratio of nutrient solution.This
Inventive embodiments can be current according to the default aberration level adjustment crops belonging to the aberration average value of crop growth phase
Ratio of nutrient solution presets ratio of nutrient solution to corresponding with the default aberration grade.It is thus possible to improve production estimation pipe
The efficiency of reason, reduce the cost of crop production management.
For easy reference and monitor, in a preferably embodiment, the method for the crop production management also includes:
Corresponding with the default aberration grade anchor a tent the default aberration grade belonging to the aberration average value and in advance
Nutrient solution proportioning, which is shown in, with the computer installation 1 communicate in the management terminal of connection.
Determine aberration average value of the crops in growth period, the default aberration grade belonging to aberration average value with
And default ratio of nutrient solution corresponding with the predetermined level, then by the default aberration grade and with the default aberration etc.
The corresponding default ratio of nutrient solution of level, which is shown in, with the computer installation 1 communicate in the management terminal of connection, so as to right
The growth of the crops is monitored and managed.The management terminal includes control room, smart mobile phone or Intelligent flat etc.
Intelligent terminal, and touch-control all-in-one machine etc..For example, second belonging to aberration average value of the crops in growth period is preset into aberration
Grade and the second default ratio of nutrient solution corresponding with the second aberration grade are shown in and carried out with the computer installation 1
In the management terminal for communicating connection;Or the 4th belonging to aberration average value of the crops in growth period is preset into aberration etc.
Level is preset ratio of nutrient solution and be shown in the described 4th default aberration grade the corresponding 4th is led to the computer installation 1
In the management terminal for interrogating connection.
For the ease of monitoring and early warning, in a preferably embodiment, the method for the crop production management also includes:
If the default aberration grade belonging to the aberration average value exceedes predetermined threshold value, warning information is sent to the pipe
Manage terminal.
If the aberration of the crop growth figure of crop growth phase and default crop growth standard drawing is excessive, illustrate
Be present larger difference in the current upgrowth situation of the crops and preferable or expected standard growth situation, then need
Timely early warning and adjustment.I.e. when the default aberration grade belonging to the aberration average value exceedes predetermined threshold value, early warning letter is sent
Breath is to management terminal, so that the growth control to the crops is timely adjusted.The predetermined threshold value is to pre-set
Default aberration grade, it can be adjusted and change according to the default aberration grade pre-set.Equally exemplified by above-mentioned,
Default aberration grade belonging to the aberration average value can be pre-set, and more than the 3rd default aberration grade, (including the 3rd is default
Aberration grade) when, then send in warning information to management terminal.For example, aberration average value institute of the crops in growth period
The default aberration grade of category is the 4th default aberration grade, then sends warning information and be connected to carrying out communication with computer installation 1
Management terminal on.
For the ease of monitoring and analyzing the growth characteristics of crops, in a preferably embodiment, the production estimation
The method of management also includes:The crop growth figure is preserved into the computer installation 1.
For the ease of the growth to the crops it is monitored and analyzes, will can also be collected by camera 2
Crop growth figure of the crops in growth period is preserved into computer installation 1, to monitor and subsequently to consult the crops
Further statistics and analysis is carried out in the upgrowth situation in growth period, and to the crop growth figure, is the crops
Production and management provide valuable reference data and accumulation production management experience.
In order to strengthen the monitoring and management to the crops, and the pest and disease damage of the crops is identified and prevented
Control, in a preferably embodiment, pest and disease damage identification model storehouse, the production estimation are stored with the computer installation
The method of management also includes:
Disease pest is carried out to the crop growth figure of the multiple predeterminable area position according to the pest and disease damage identification model storehouse
Evil identification.
Wherein, the pest and disease damage identification model is by the crop growth to largely including various types of pest and disease damages
Figure is trained and learnt, the pest and disease damage identification model of acquisition.It is to extract in the crop growth figure comprising pest and disease damage
Pest and disease damage is characterized as that object builds grader, to reach the target of pest and disease damage Classification and Identification.The pest and disease damage identification model is pre-
First train, establish, and the pest and disease damage identification model being stored in advance in the computer installation.Specifically, statistics can be passed through
Recognition methods, structural recognition method, fuzzy set identification method, artificial neural network recognition methods and the identification of SVMs
Method establishes the pest and disease damage identification model.
Determine pest and disease damage recognition result and prevention and controls corresponding with the pest and disease damage recognition result.
After crop growth figure is identified using the pest and disease damage identification model, pest and disease damage recognition result is determined,
And prevention and controls corresponding with the pest and disease damage recognition result are determined simultaneously.For example, it is assumed that crops are Chinese cabbage, in small holes caused by worms number
For 2 to 15, and pest and disease damage influence blade face area more than 159 square centimeters when, then according to the pest and disease damage identification model determine
Pest and disease damage result is small holes caused by worms be present;Small holes caused by worms number be 0 to 2, and pest and disease damage influence blade face area more than 50 square centimeters and
During less than 159 square centimeters, then pest and disease damage result is determined according to the pest and disease damage identification model a small amount of small holes caused by worms to be present.Wherein,
Prevention and controls corresponding with the recognition result there may be it is multiple, i.e., for a pest and disease damage result, it is understood that there may be Duo Gefang
Control method.The prevention and controls are with prestoring prevention and controls corresponding with the pest and disease damage recognition result.
The pest and disease damage recognition result and the prevention and controls are included being connected carrying out communication with the computer installation
Management terminal on.
Finally, the pest and disease damage recognition result and prevention and controls corresponding with the pest and disease damage recognition result are included
In management terminal, the management terminal includes control room, the intelligent terminal such as smart mobile phone or Intelligent flat, and touch control integrated
Machine etc., the management terminal carry out wired or wireless communication connection with the computer installation 1.
It is described in a preferably embodiment in order to improve recognition effect and accuracy rate to the diseases and pests of agronomic crop
Pest and disease damage identification bag is carried out to the crop growth figure of the multiple predeterminable area position according to the pest and disease damage identification model storehouse
Include:
Extract the scab feature of each crop growth figure.
In order to be identified the crop growth figure comprising pest and disease damage, it is necessary to extract the crop growth
The scab feature of figure.In a preferably embodiment, the scab feature include scab color characteristic, scab textural characteristics and
Spot pattern feature.Color characteristic is smaller to the dependence at crop growth figure size in itself, direction, visual angle, so as to have
Higher robustness.In a preferably embodiment, the scab color characteristic includes color histogram, color moment and color
Collection.Textural characteristics are a kind of visual signatures independent of homogeneity phenomenon in color or the reflection image of brightness, and it is all
The shared internal characteristicses of body surface.In a preferably embodiment, the pathology textural characteristics include Tamura textural characteristics,
Gray level co-occurrence matrixes and with the textural characteristics of wavelet representation etc..The spot pattern feature includes geometric properties, region
The feature such as Expressive Features, not bending moment and Fourier's shape description.
Pest and disease damage identification is carried out to the scab feature according to the pest and disease damage identification model.
After the scab feature of the crop growth figure is got, i.e., the computer dress is stored in advance according to
The pest and disease damage identification model in putting carries out the identification of pest and disease damage to the scab feature of the extraction, to judge the crops
It whether there is the threat of pest and disease damage in current growth period, and judge the pest and disease damage type in the crop growth figure.
In order to improve the accuracy rate of pest and disease damage identification, in a preferably embodiment, each crop growth of extraction
The scab feature of figure includes:
The crop growth figure is pre-processed, and obtains the scab image in the crop growth figure.
In view of the collection environment of the crop growth figure is affected by various factors, such as intensity of illumination or equipment
Quality etc., the picture collected often exist noise, contrast not enough, target it is not clear enough, interference of other objects etc. be present,
Cause the of low quality of the crop growth figure.Therefore, in order to improve the quality of the crop growth figure, then need to institute
Crop growth figure is stated to be pre-processed.
Specifically, it is described to the crop growth figure carry out pretreatment include first with image cropping technology to described
The target area comprising pest and disease damage is cut in crop growth figure, obtains the target area for including the pest and disease damage, secondly,
Utilization space domain filter method is smoothed to the target area.Specifically, the filter in spatial domain method is filtered including average
Ripple method and median filtering method.The profile that pest and disease damage in the target area is extracted using gradient operator or contour following algorithm is special
Sign.The gradient operator includes Sobel operators, Prewitt operators and Canny operators etc..To the crop growth figure
After being pre-processed, the scab image in the crop growth figure comprising pest and disease damage contour feature is obtained.
Extract the scab feature in the scab image.
After being pre-processed to the crop growth figure, the scab of the contour feature comprising the pest and disease damage is got
Image, then the contour feature based on the pest and disease damage extract the scab feature in the scab image.
In a preferably embodiment, the big data method of logistic regression can be utilized to the disease pest in pest and disease damage spectrum library
Evil image is learnt, and establishes the pest and disease damage identification model storehouse.
Wherein, the pest and disease damage spectrum library is the database for including a large amount of ill crop growth figures, and the logic is returned
The big data method returned is a kind of method based on discriminate, it assumes that the example of class is linear separability, passes through direct estimation
The parameter of discriminate, a kind of method of final forecast model is obtained, in embodiments of the present invention, pass through the big number of logistic regression
The pest and disease damage image in pest and disease damage spectrum library is trained according to method, learnt, and establishes the pest and disease damage identification model, to treat
Subsequently the pest and disease damage situation of crops is identified using the pest and disease damage identification model.
In addition, in other embodiments, it can specifically pass through SVMs method, neural network and fuzzy clustering
One or more kinds of crop growth figures to including pest and disease damage in the pest and disease damage spectrum library in the sorting techniques such as method are carried out
Identification and classification, and establish pest and disease damage identification model.Wherein, SVMs method is a kind of machine based on Statistical Learning Theory
Device learning method, its basic thought is to build optimal classification surface in sample space, so that class interval maximum turns to principle pair
Crop growth figure comprising pest and disease damage carries out statistical classification, test result indicates that, the kernel function using SVMs as representative
For method in the case where crop disease shape of spot is complicated and training sample is less, its classifying quality has the advantage of high discrimination.God
It is a kind of mathematical modeling of the 26S Proteasome Structure and Function of mimic biology neutral net through network, the basic thought of neural network is to pass through
Sample training is carried out to multi-layer perception (MLP), linear decision function is obtained, using decision function as to the crops comprising pest and disease damage
Growth figure enters Classification and Identification.
In embodiments of the present invention, according to farming of the pest and disease damage identification model storehouse to the multiple predeterminable area position
Thing growth figure carries out pest and disease damage identification, determines pest and disease damage recognition result and the side of preventing and treating corresponding with the pest and disease damage recognition result
Method, the crop growth figure is pre-processed, and obtain the scab image in the crop growth figure, extract each agriculture
The scab feature of plant growth figure, pest and disease damage identification is carried out to the scab feature according to the pest and disease damage identification model.This hair
Bright embodiment can carry out pest and disease damage identification using pest and disease damage identification model to the crop growth figure, determine that pest and disease damage identifies
As a result and corresponding prevention and controls, to carry out pest management to the crops, therefore, agriculture can further be improved
The efficiency of Crop management, reduce the cost of crop production management.
Fig. 2 shows the functional module of the system of crop production management provided in an embodiment of the present invention, for the ease of saying
It is bright, the part related to the embodiment of the present invention is illustrate only, details are as follows:
With reference to figure 2, the modules included by the system 10 of the crop production management are used to perform the corresponding implementations of Fig. 1
Each step in example, the associated description in embodiment is corresponded to referring specifically to Fig. 1 and Fig. 1, here is omitted.The present invention
In embodiment, the system 10 of institute's crop production management includes acquisition module 101, aberration average value determining module 102, anchored a tent in advance
Nutrient solution proportioning determining module 103, acquisition module 104 and adjusting module 105.
The acquisition module 101, for the growth period in the crops, gathered by the multiple camera 2 described
The crop growth figure of multiple predeterminable area positions of planting area.
The aberration average value determining module 102, for by the crop growth figure of each predeterminable area position with it is described
The crop growth standard drawing of the correspondence predeterminable area position prestored in computer installation 1 is contrasted with determination pair
The value of chromatism of each predeterminable area position is answered, and determines aberration average value.
The default ratio of nutrient solution determining module 103, for default aberration according to belonging to the aberration average value etc.
Level, it is determined that default ratio of nutrient solution corresponding with the default aberration grade.
The acquisition module 104, the ratio of nutrient solution current for obtaining the crops.
The adjusting module 105, if the ratio of nutrient solution current for the crops described pre- is anchored a tent with identified
Nutrient solution proportioning is different, then adjusts the current ratio of nutrient solution of the crops to the identified default ratio of nutrient solution.
In embodiments of the present invention, the acquisition module 101 was gathered by multiple cameras 2 in the growth period of crops
The crop growth figure of multiple predeterminable area positions of planting area, the aberration average value determining module 102 will each be preset
The crop growth figure of regional location and the crop growth mark of the corresponding predeterminable area position prestored in computer installation 1
Quasi- figure is contrasted to determine the value of chromatism of corresponding each predeterminable area position, and determines aberration average value, the default nutrition
Liquid matches default aberration grade of the determining module 103 according to belonging to aberration average value, it is determined that corresponding pre- with default aberration grade
If ratio of nutrient solution, the acquisition module 104 obtains the current ratio of nutrient solution of crops, if the current nutrient solution of crops is matched somebody with somebody
More different than from identified default ratio of nutrient solution, then the adjusting module 105 adjusts the current ratio of nutrient solution of crops extremely
Identified default ratio of nutrient solution.The embodiment of the present invention can be according to belonging to the aberration average value of crop growth phase it is default
The current ratio of nutrient solution of aberration level adjustment crops presets ratio of nutrient solution to corresponding with the default aberration grade.Cause
This, can improve the efficiency of crop production management, reduce the cost of crop production management.
For easy reference and monitor, in a preferably embodiment, the system 10 of the crop production management is also wrapped
Include the first display module.
First display module, for being preset by the default aberration grade belonging to the aberration average value and with described
Aberration grade is preset ratio of nutrient solution and is shown in the management terminal that communication connection is carried out with the computer installation 1 accordingly.
For the ease of monitoring and early warning, in a preferably embodiment, the system 10 of the crop production management is also wrapped
Include warning module.
The warning module, if exceed predetermined threshold value for the default aberration grade belonging to the aberration average value, hair
Warning information is sent to the management terminal.
For the ease of monitoring and analyzing the growth characteristics of crops, in a preferably embodiment, the production estimation
The system 10 of management also includes preserving module.
The preserving module, for preserving the crop growth figure into the computer installation 1.
In order to strengthen the monitoring and management to the crops, and the pest and disease damage of the crops is identified and prevented
Control, in a preferably embodiment, pest and disease damage identification model storehouse, the crops life are stored with the computer installation 1
The system 10 of production management also includes pest and disease damage identification module, determining module and the second display module.
The pest and disease damage identification module, for according to the pest and disease damage identification model storehouse to the multiple predeterminable area position
Crop growth figure carry out pest and disease damage identification.
The determining module, for determining pest and disease damage recognition result and the side of preventing and treating corresponding with the pest and disease damage recognition result
Method.
Second display module, by the pest and disease damage recognition result and the prevention and controls are included with it is described based on
Calculation machine device 1 communicate in the management terminal of connection.
It is described in a preferably embodiment in order to improve recognition effect and accuracy rate to the diseases and pests of agronomic crop
Pest and disease damage identification module includes extraction unit and recognition unit.
The extraction unit, for extracting the scab feature of each crop growth figure.In a preferably embodiment, institute
Stating scab feature includes scab color characteristic, scab textural characteristics and spot pattern feature.
The recognition unit, for carrying out pest and disease damage identification to the scab feature according to the pest and disease damage identification model.
In order to improve the accuracy rate of pest and disease damage identification, in a preferably embodiment, the extraction unit includes pretreatment
Subelement and extraction subelement.
The pretreatment subelement, for being pre-processed to the crop growth figure, and obtain the crops life
Scab image in long figure.
The extraction subelement, for extracting the scab feature in the scab image.
In a preferably embodiment, the big data method of logistic regression can be utilized to the disease pest in pest and disease damage spectrum library
Evil image is learnt, and establishes the pest and disease damage identification model storehouse.
The detailed functions of above-mentioned each functional module, unit and subelement specifically may refer to above-mentioned corresponding method and implement
Associated description in example, is no longer described in detail herein.
In embodiments of the present invention, the pest and disease damage identification module according to the pest and disease damage identification model storehouse to the multiple
The crop growth figure of predeterminable area position carries out pest and disease damage identification, the determining module determine pest and disease damage recognition result and with institute
The corresponding prevention and controls of pest and disease damage recognition result are stated, the pretreatment subelement pre-processes to the crop growth figure,
And the scab image in the crop growth figure is obtained, the scab that the extraction unit extracts each crop growth figure is special
Sign, the recognition unit carry out pest and disease damage identification according to the pest and disease damage identification model to the scab feature.The present invention is implemented
Example can utilize pest and disease damage identification model to the crop growth figure progress pest and disease damage identification, determine pest and disease damage recognition result with
And corresponding prevention and controls, to carry out pest management to the crops, therefore, it can further improve crops life
The efficiency of management is produced, reduces the cost of crop production management.
Fig. 3 is the computer dress of the preferred embodiment of the method provided in an embodiment of the present invention for realizing crop production management
Put 1 structural representation.As shown in figure 3, computer installation 1 includes memory 11, processor 12 and input-output equipment 13.
The computer installation 1 be it is a kind of can according to the instruction for being previously set or storing, it is automatic carry out numerical computations and/
Or the equipment of information processing, its hardware include but is not limited to microprocessor, application specific integrated circuit (Application Specific
Integrated Circuit, ASIC), programmable gate array (Field-Programmable Gate Array, FPGA), number
Word processing device (Digital Signal Processor, DSP), embedded device etc..
The computer installation 1 can be any electronic product that man-machine interaction can be carried out with user, for example, personal
Computer, tablet personal computer, smart mobile phone, personal digital assistant (Personal Digital Assistant, PDA), game machine,
IPTV (Internet Protocol Television, IPTV), intellectual Wearable etc..The calculating
Machine device 1 can be server, and the server includes but is not limited to single network server, multiple webservers form
Server group or the cloud being made up of a large amount of main frames or the webserver based on cloud computing (Cloud Computing), wherein, cloud
Calculating is one kind of Distributed Calculation, a super virtual computer being made up of the computer collection of a group loose couplings.It is described
Network residing for computer installation 1 includes but is not limited to internet, wide area network, Metropolitan Area Network (MAN), LAN, VPN
(Virtual Private Network, VPN) etc..
Memory 11 is used to store the program of the method for crop production management and various data, and in computer installation 1
High speed is realized in running, is automatically completed the access of program or data.Memory 11 can be the outside of computer installation 1
Storage device and/or internal storage device.Further, memory 11 can not have having for physical form in integrated circuit
The circuit of store function, such as RAM (Random-Access Memory, direct access storage device), FIFO (First In
First Out) etc., or, memory 11 can also be the storage device for having physical form, such as memory bar, TF card
(Trans-flash Card) etc..
Processor 12 can be central processing unit (CPU, Central Processing Unit).CPU is one piece of super large rule
The integrated circuit of mould, it is the arithmetic core (Core) and control core (Control Unit) of computer installation 1.Processor 12 can
The operating system of computer installation 1 and the types of applications program of installation, program code etc. are performed, such as performs production estimation
The types of applications program of operating system and installation in the modules or unit of the system 10 of management, program code, with
The method for realizing crop production management.
Input-output equipment 13 is mainly used in realizing the input/output function of computer installation 1, for example receives and dispatches the number of input
Word or character information, or show the information inputted by user or be supplied to the information of user and respectively planting vegetables for computer installation 1
It is single.
If the integrated module/unit of the computer installation 1 is realized in the form of SFU software functional unit and as independently
Production marketing or in use, can be stored in a computer read/write memory medium.Based on such understanding, the present invention
All or part of flow in above-described embodiment method is realized, the hardware of correlation can also be instructed come complete by computer program
Into described computer program can be stored in a computer-readable recording medium, and the computer program is being executed by processor
When, can be achieved above-mentioned each embodiment of the method the step of.Wherein, the computer program includes computer program code, described
Computer program code can be source code form, object identification code form, executable file or some intermediate forms etc..The meter
Calculation machine computer-readable recording medium can include:Can carry any entity or device of the computer program code, recording medium, USB flash disk,
Mobile hard disk, magnetic disc, CD, computer storage, read-only storage (ROM, Read-Only Memory), random access memory
Device (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium etc..Need what is illustrated
It is that the content that the computer-readable medium includes can be fitted according to legislation in jurisdiction and the requirement of patent practice
When increase and decrease, such as in some jurisdictions, according to legislation and patent practice, computer-readable medium, which does not include electric carrier wave, to be believed
Number and telecommunication signal.
In several embodiments provided by the present invention, it should be understood that disclosed system, apparatus and method can be with
Realize by another way.For example, device embodiment described above is only schematical, for example, the module
Division, only a kind of division of logic function, can there is other dividing mode when actually realizing.
The module illustrated as separating component can be or may not be physically separate, show as module
The part shown can be or may not be physical location, you can with positioned at a place, or can also be distributed to multiple
On NE.Some or all of module therein can be selected to realize the mesh of this embodiment scheme according to the actual needs
's.
In addition, each functional module in each embodiment of the present invention can be integrated in a processing unit, can also
That unit is individually physically present, can also two or more units it is integrated in a unit.Above-mentioned integrated list
Member can both be realized in the form of hardware, can also be realized in the form of hardware adds software function module.
It is obvious to a person skilled in the art that the invention is not restricted to the details of above-mentioned one exemplary embodiment, Er Qie
In the case of without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, embodiment all should be regarded as exemplary, and be nonrestrictive, the scope of the present invention is by appended power
Profit requires rather than described above limits, it is intended that all in the implication and scope of the equivalency of claim by falling
Change is included in the present invention.Any attached associated diagram mark in claim should not be considered as into the involved right of limitation will
Ask.Furthermore, it is to be understood that the word of " comprising " one is not excluded for other units or step, odd number is not excluded for plural number.Stated in system claims
Multiple modules or device can also be realized by a module or device by software or hardware.The first, the second grade word
For representing title, and it is not offered as any specific order.
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention and it is unrestricted, although reference
The present invention is described in detail for preferred embodiment, it will be understood by those within the art that, can be to the present invention's
Technical scheme is modified or equivalent substitution, without departing from the spirit and scope of technical solution of the present invention.
Claims (10)
- A kind of 1. method of crop production management, applied in computer installation, it is characterised in that the planting area of crops The multiple cameras for the computer installation communicate connection are installed, methods described includes:In the growth period of the crops, pass through multiple predeterminable area positions of the multiple camera collection planting area Crop growth figure;The crop growth figure of each predeterminable area position is corresponding described default with being prestored in the computer installation The crop growth standard drawing of regional location is contrasted to determine the value of chromatism of corresponding each predeterminable area position, and determines color Poor average value;Default aberration grade according to belonging to the aberration average value, it is determined that default nutrition corresponding with the default aberration grade Liquid matches;Obtain the current ratio of nutrient solution of the crops;If the current ratio of nutrient solution of the crops is different from the identified default ratio of nutrient solution, the agriculture is adjusted The current ratio of nutrient solution of crop is to the identified default ratio of nutrient solution.
- 2. the method as described in claim 1, it is characterised in that methods described also includes:By the default aberration grade belonging to the aberration average value and default nutrient solution corresponding with the default aberration grade Proportioning, which is shown in, with the computer installation communicate in the management terminal of connection.
- 3. the method as described in claim 1, it is characterised in that methods described also includes:If the default aberration grade described in the aberration average value exceedes predetermined threshold value, send warning information to the calculating Machine device communicate the management terminal of connection.
- 4. the method as described in claim 1, it is characterised in that pest and disease damage identification model is stored with the computer installation Storehouse, methods described also include:Pest and disease damage knowledge is carried out to the crop growth figure of the multiple predeterminable area position according to the pest and disease damage identification model storehouse Not;Determine pest and disease damage recognition result and prevention and controls corresponding with the pest and disease damage recognition result;The pest and disease damage recognition result and the prevention and controls are included with the computer installation communicate the pipe of connection Manage in terminal.
- 5. method as claimed in claim 4, it is characterised in that it is described according to the pest and disease damage identification model storehouse to the multiple The crop growth figure of predeterminable area position, which carries out pest and disease damage identification, to be included:Extract the scab feature of each crop growth figure;Pest and disease damage identification is carried out to the scab feature according to the pest and disease damage identification model.
- 6. method as claimed in claim 5, it is characterised in that the scab feature includes scab color characteristic, scab texture Feature and spot pattern feature.
- 7. method as claimed in claim 5, it is characterised in that the scab feature bag of each crop growth figure of extraction Include:The crop growth figure is pre-processed, and obtains the scab image in the crop growth figure;Extract the scab feature in the scab image.
- A kind of 8. system of crop production management, applied in computer installation, it is characterised in that the planting area of crops The multiple cameras for the computer installation communicate connection are installed, the system includes:Acquisition module, for the growth period in the crops, the planting area are gathered by the multiple camera more The crop growth figure of individual predeterminable area position;Aberration average value determining module, for by the crop growth figure of each predeterminable area position and the computer installation The crop growth standard drawing of the correspondence predeterminable area position prestored is contrasted to determine corresponding each preset areas The value of chromatism of domain position, and determine aberration average value;Default ratio of nutrient solution determining module, for the default aberration grade according to belonging to the aberration average value, it is determined that and institute State default aberration grade and preset ratio of nutrient solution accordingly;Acquisition module, the ratio of nutrient solution current for obtaining the crops;Adjusting module, if the ratio of nutrient solution current for the crops and the identified default ratio of nutrient solution are not Together, then the current ratio of nutrient solution of the crops is adjusted to the identified default ratio of nutrient solution.
- 9. a kind of computer installation, it is characterised in that the computer installation includes processor, and the processor is deposited for execution The method that the crop production management as described in any one in claim 1-8 is realized during the computer program stored in reservoir.
- 10. a kind of computer-readable recording medium, is stored thereon with computer program, it is characterised in that the computer program The method that the crop production management as described in any one in claim 1-8 is realized when being executed by processor.
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Cited By (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108492295A (en) * | 2018-03-30 | 2018-09-04 | 深圳春沐源控股有限公司 | Fruit annesl control method and device |
CN109886259A (en) * | 2019-02-22 | 2019-06-14 | 潍坊科技学院 | A kind of tomato disease based on computer vision identification method for early warning and device |
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Citations (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103559511A (en) * | 2013-11-20 | 2014-02-05 | 天津农学院 | Automatic identification method of foliar disease image of greenhouse vegetable |
CN103674849A (en) * | 2012-09-04 | 2014-03-26 | 南京农业大学 | Crop nitrogen nutrition diagnosis sensor |
CN102759528B (en) * | 2012-07-09 | 2014-04-02 | 陕西科技大学 | Method for detecting diseases of crop leaves |
CN103955938A (en) * | 2014-05-15 | 2014-07-30 | 安徽农业大学 | Wheat growing status diagnosing method based on mobile internet mode and leaf color analysis |
CN105159268A (en) * | 2015-10-26 | 2015-12-16 | 深圳市讯方技术股份有限公司 | Intelligent agricultural control system and method |
CN105511366A (en) * | 2016-02-01 | 2016-04-20 | 平顶山学院 | Plant physiology monitoring method and system |
CN106355609A (en) * | 2015-07-17 | 2017-01-25 | 苏州宝时得电动工具有限公司 | Automatic walking device and vegetation health condition recognition method thereof |
CN106775537A (en) * | 2016-12-29 | 2017-05-31 | 深圳前海弘稼科技有限公司 | Control method, control device and server that nutrient solution concentration shows |
CN107292874A (en) * | 2017-06-29 | 2017-10-24 | 深圳前海弘稼科技有限公司 | The control method and device of crop disease |
-
2017
- 2017-11-03 CN CN201711071686.3A patent/CN107844771A/en active Pending
Patent Citations (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102759528B (en) * | 2012-07-09 | 2014-04-02 | 陕西科技大学 | Method for detecting diseases of crop leaves |
CN103674849A (en) * | 2012-09-04 | 2014-03-26 | 南京农业大学 | Crop nitrogen nutrition diagnosis sensor |
CN103559511A (en) * | 2013-11-20 | 2014-02-05 | 天津农学院 | Automatic identification method of foliar disease image of greenhouse vegetable |
CN103955938A (en) * | 2014-05-15 | 2014-07-30 | 安徽农业大学 | Wheat growing status diagnosing method based on mobile internet mode and leaf color analysis |
CN106355609A (en) * | 2015-07-17 | 2017-01-25 | 苏州宝时得电动工具有限公司 | Automatic walking device and vegetation health condition recognition method thereof |
CN105159268A (en) * | 2015-10-26 | 2015-12-16 | 深圳市讯方技术股份有限公司 | Intelligent agricultural control system and method |
CN105511366A (en) * | 2016-02-01 | 2016-04-20 | 平顶山学院 | Plant physiology monitoring method and system |
CN106775537A (en) * | 2016-12-29 | 2017-05-31 | 深圳前海弘稼科技有限公司 | Control method, control device and server that nutrient solution concentration shows |
CN107292874A (en) * | 2017-06-29 | 2017-10-24 | 深圳前海弘稼科技有限公司 | The control method and device of crop disease |
Non-Patent Citations (2)
Title |
---|
张培松 等: ""基于数字图像分析技术的橡胶树叶片氮含量预测"", 《热带作物学报》 * |
徐胜勇 等: ""基于颜色特征的油菜缺素症图像诊断"", 《中国油料作物学报》 * |
Cited By (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108492295A (en) * | 2018-03-30 | 2018-09-04 | 深圳春沐源控股有限公司 | Fruit annesl control method and device |
CN109886259A (en) * | 2019-02-22 | 2019-06-14 | 潍坊科技学院 | A kind of tomato disease based on computer vision identification method for early warning and device |
CN111020100A (en) * | 2019-12-30 | 2020-04-17 | 中冶南方工程技术有限公司 | Double-furnace-shell steelmaking production method |
CN111274979A (en) * | 2020-01-23 | 2020-06-12 | 浙江工业大学之江学院 | Plant disease and insect pest identification method and device, computer equipment and storage medium |
CN111598001A (en) * | 2020-05-18 | 2020-08-28 | 哈尔滨理工大学 | Apple tree pest and disease identification method based on image processing |
CN111598001B (en) * | 2020-05-18 | 2023-04-28 | 哈尔滨理工大学 | Identification method for apple tree diseases and insect pests based on image processing |
CN112001242A (en) * | 2020-07-16 | 2020-11-27 | 厦门理工学院 | Intelligent gardening management method and device |
CN112001242B (en) * | 2020-07-16 | 2022-12-06 | 厦门理工学院 | Intelligent gardening management method and device |
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