CN104361341A - Method of analyzing crop growth image based on embedded type equipment - Google Patents
Method of analyzing crop growth image based on embedded type equipment Download PDFInfo
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- CN104361341A CN104361341A CN201410726030.0A CN201410726030A CN104361341A CN 104361341 A CN104361341 A CN 104361341A CN 201410726030 A CN201410726030 A CN 201410726030A CN 104361341 A CN104361341 A CN 104361341A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
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Abstract
The invention provides a method of analyzing a crop growth image based on embedded type equipment. A specific realizing process of the method is as follows: customizing the embedded type equipment; connecting the embedded type equipment with a camera device and then putting the embedded type equipment in a crop growth environment, and connecting the embedded type equipment to a data receiving platform by virtue of a wireless network, namely WIFI (wireless fidelity); operating the embedded type equipment by virtue of a LCD (liquid crystal display) touch type display screen of the embedded equipment via a user to complete interaction work so as to find the optical feature extraction result. Compared with the prior art, according to the method of analyzing the crop growth image based on the embedded type equipment disclosed by the invention, a conventional method of performing theoretical analysis under a lab environment and then gradually applying the theoretically analyzed results to practical production is cancelled, experiment is directly performed on a production site and analyzed results are obtained, so that the defect that the experiment and the application are disjointed due to the conventional method of firstly performing experiment and then applying is changed.
Description
Technical field
The present invention relates to IT application to agriculture technical field, a kind of for crop condition monitoring analysis, the method analyzing crop growing state image based on embedded device specifically.
Background technology
Along with the development of machine vision technique, this technology is also more and more used in agricultural production, and being monitored by computing machine crop growing state and analyze is a wherein important aspect.A typical computer vision system comprises camera head, light irradiation apparatus and graphical analysis platform.Light irradiation apparatus can provide stable lighting effect, and camera head gathers crop map picture, and graphical analysis platform carries out graphical analysis obtain quantized value to the leaf blade size in crop map picture, color, Plants are high, and then judges plant growth situation.Effectively manpower can be saved by computer vision technique monitoring crop growing way, and can standardization and precision, significant to the development of intelligent agriculture and robotization.
The analysis of appliance computer vision technique judges that crop growing state is generally tested from laboratory environment, is then progressively applied in actual production.In laboratory environment, illumination is sufficient and stable, clear background is remarkable, is extremely beneficial to graphical analysis.But in actual production, the circumstance complication of production scene is changeable, as unstable in: lighting effect and constantly change in time, background is unintelligible.Which results in the bottleneck problem that theoretical method is applied to agricultural production reality.
In recent years, the performance of embedded device is significantly enhanced, and embedded device can carry out complicated image manipulation.
Based on this, now provide that a kind of view data is clear, transmission performance be good, fully schedules system resources, analyze the method for crop growing state image based on embedded device.
Summary of the invention
Technical assignment of the present invention solves the deficiencies in the prior art, provides a kind of practical, method of analyzing crop growing state image based on embedded device.
Technical scheme of the present invention realizes in the following manner, a kind of this method analyzing crop growing state image based on embedded device, and its specific implementation process is:
One, embedded device is customized:
1) arrange Embedded Hardware Platform, this Embedded Hardware Platform comprises LCD touching display screen, AV input interface, CMOS camera interface, SD deck, USB interface, Ethernet interface, WiFi module;
2) embedded system kernel is set, and on this embedded system kernel, application system is installed, in this application system, image processing system is set, this image processing system comprises basic function and image processing function, and described basic function comprises the next video flowing of reading shooting driver transmission, intercepts picture and man-machine interaction; Image processing function with shooting crop picture for handling object, be divided into lower module according to the general flow of crop growing state image procossing: Image semantic classification, color space conversion, Threshold segmentation, rim detection, morphology, wherein each module includes some image processing algorithms, be supplied to user to combinationally use, to obtain the result of feature extraction;
Two, embedded device is connected camera head and be placed in crop growth environment, and by WiFi model calling to network, to send the information of image acquisition and process;
Three, user is undertaken alternately by the LCD touching display screen of this embedded device and image processing system, and extract characteristics of image by image processing function wherein, the specific implementation process of this feature extraction is:
A) plant growth image is taken by camera head and is transferred to embedded device;
B) image pre-processing module in embedded device carries out pre-service to received image, to reduce the impact that illumination and picture noise are analyzed successive image, makes image become standardized images for ease of subsequent operation;
C) color conversion module carries out color space conversion to through pretreated image, makes characteristics of image can be mapped in different colours space;
D) Threshold segmentation module carries out the segmentation of binaryzation, is split by crop from background, to carry out follow-up quantitative analysis;
E) edge detection module is partitioned into the edge of crop, as the feature extracted and for follow-up quantitative analysis;
F) morphology module carries out strengthening or weakening feature, to keep the integrality of feature;
G) indicating characteristic extracts result;
H) cycling step b) is to step g), and user selects optimum feature extracting method according to display result, terminates whole characteristic extraction procedure.
Pass through above-mentioned steps, achieve embedded device and extract its characteristics of image in the actual environment of plant growth, thus ensure the sharpness of plant growth image, meet the needs of different production on-site environment, thus the application in laboratory environment is converted in actual crop production environment, actual effect is desirable, practical.
As preferably, in described crop growth environment, be provided with demarcation bar, to facilitate, plant growth compared.By the setting of this demarcation bar, make the growth image of whole crop have obvious contrast, observe more directly perceived.
As preferably, intermediate value is formed in described Image semantic classification, color space conversion, Threshold segmentation, rim detection, morphological operation process, this intermediate value stores the intermediateness data of image manipulation, can as the input and output of any particular algorithms, make image manipulation need not observe Image semantic classification, color space conversion, Threshold segmentation, rim detection, morphologic flow sequence and independent assortment algorithm, the abundant feature extraction result obtained.The setting of this intermediate value, has enriched obtainable feature extraction result, makes user at more high-freedom degree and widerly can select optimum algorithm down.
As preferably, the algorithm that described Image semantic classification process adopts comprises medium filtering, gaussian filtering or histogram equalization algorithm.
Further, the space of described color conversion comprises rgb space and HSV space, and wherein rgb space comprises R passage, G passage and channel B; HSV space comprises H passage, channel S and V passage.
Further, the algorithm that described Threshold segmentation process adopts comprises artificial selection, OTSU algorithm or iteration selection algorithm.
Further, the algorithm that described edge detection process adopts comprises Canny operator edge detection algorithm, LoG algorithm, Sobel algorithm or Hough transform algorithm.
Further, the algorithm that described morphology process adopts comprises in erosion operation or dilation operation.
The beneficial effect that the present invention is compared with prior art produced is:
A kind of method analyzing crop growing state image based on embedded device of the present invention is abandoned traditional theoretical analysis that carries out in laboratory environments and is progressively applied to method in production reality again, directly carry out testing and obtaining analysis result in production scene, change the defect that classic method first tests the disconnection of experiment that rear application causes and application; Utilize the feature of embedded device mounting arrangements convenience, excellent performance, using the graphical analysis platform of embedded device as computer vision system, by embedded device is arranged in production scene, embedded device combinationally uses image processing method, obtain crop growing state analysis result; Practical, the image processing algorithm of the overwhelming majority in desk-top computer and technology can be run on this embedded device; Embedded device entirety in the method is easy to carry, and mounting arrangements is simple; Image processing system provides easy interactive mode for user; User can arrange embedded vision system in production scene, obtains optimum feature extraction result, and regulate and control production environment according to this by the multiple visible sensation method of this system in combination; Facilitate staff to safeguard use, save production cost, be easy to promote.
Accompanying drawing explanation
Accompanying drawing 1 is embedded device one-piece construction schematic block diagram of the present invention.
Accompanying drawing 2 installs and uses schematic diagram for embedded device of the present invention.
Accompanying drawing 3 is characteristic extraction procedure figure of the present invention.
Mark in accompanying drawing represents respectively:
1, bar is demarcated, 2, crop, 3, camera head, 4, embedded device, 5, wireless network.
Embodiment
Below in conjunction with accompanying drawing, a kind of method based on embedded device analysis crop growing state image of the present invention is described in detail below.
The object of the invention is to, carry out testing at plant growth scene and complete the analysis of experimental data by arranging rational equipment, obtaining best crop growing state analysis result.As shown in accompanying drawing 1, Fig. 2, Fig. 3, now provide a kind of method analyzing crop growing state image based on embedded device, its specific implementation comprises:
One, embedded device is customized:
1) Embedded Hardware Platform is set, this Embedded Hardware Platform comprises LCD touching display screen, AV input interface, CMOS camera interface, SD deck, USB interface, Ethernet interface, WiFi module, the hardware platform of this structure comparatively easily realizes, therefore do not do detailed expression in the accompanying drawings, also repeat no more at this;
2) embedded system kernel customized to above-mentioned Embedded Hardware Platform and carry out the exploitation of hardware driving script, embedded system can be run on embedded hardware equipment, and read the video flowing of camera head shooting, then on this embedded system kernel, application system is installed, in actual design, this application system can adopt the android system of increasing income, in this application system, image processing system is set, this image processing system comprises basic function and image processing function, described basic function comprises the video flowing reading the transmission of shooting driver and come, intercept picture and man-machine interaction, image processing function for handling object, is divided into lower module according to the general flow of crop growing state image procossing: Image semantic classification, color space conversion, Threshold segmentation, rim detection, morphology with the crop picture of shooting,
Two, embedded device is connected camera head and be placed in crop growth environment, and by WiFi model calling to network, to send the information of image acquisition and process;
Three, user is undertaken alternately by the LCD touching display screen of this embedded device and image processing system, and extract characteristics of image by image processing function wherein, the specific implementation process of this feature extraction is:
A) plant growth image is taken by camera head and is transferred to embedded device;
B) user's selection algorithm in image pre-processing module carries out pre-service to received image, to reduce the impact that illumination and picture noise are analyzed successive image, makes image become standardized images for ease of subsequent operation;
C) user carries out color space conversion in color conversion module, and characteristics of image can be expressed in color components clearly;
D) user's selection algorithm in Threshold segmentation module does binarization segmentation to image, is split by crop from background, to carry out follow-up quantitative analysis;
E) user's selection algorithm in edge detection module detects the edge of crop, as the feature extracted and for follow-up quantitative analysis;
F) user's selection algorithm strengthening or reduction feature in morphology module, to keep the integrality of feature;
G) indicating characteristic extracts result, and user can according to the feature extracting method of the algorithm in the above step of its effect selection as optimum;
H) user can return former figure, continuous circulation above-mentioned steps b) to g), until find desirable feature extraction algorithm.
Be provided with demarcation bar in described crop growth environment, to facilitate, plant growth compared.
Intermediate value is formed in described Image semantic classification, color space conversion, Threshold segmentation, rim detection, morphological operation process, the intermediateness data of algorithm in the data structure storage flow process of this intermediate value, can as the input and output of any particular algorithms, make image manipulation need not observe Image semantic classification, color space conversion, Threshold segmentation, rim detection, morphologic flow sequence and independent assortment algorithm, the abundant feature extraction result obtained.
Each module in above-mentioned image processing flow comprises multiple image processing algorithm, and detailed algorithm content is as follows:
The algorithm that described Image semantic classification process adopts comprises medium filtering, gaussian filtering or histogram equalization algorithm.
The space of described color conversion comprises rgb space and HSV space, and wherein rgb space comprises R passage, G passage and channel B; HSV space comprises H passage, channel S and V passage.
The algorithm that described Threshold segmentation process adopts comprises artificial selection, OTSU algorithm or iteration selection algorithm.
The algorithm that described edge detection process adopts comprises Canny operator edge detection algorithm, LoG algorithm, Sobel algorithm or Hough transform algorithm.
The algorithm that described morphology process adopts comprises erosion operation or dilation operation.
User can be clicked by touch-screen in data receiver platform, selects the algorithm in flow process, is combined into a set of image processing algorithm, obtains the result of feature extraction.According to many experiments, user according to the effect of feature extraction, can find optimum image processing algorithm, obtains optimum feature extraction result, and regulates and controls production environment according to this.
Such as:
Median filtering algorithm is selected in the pre-service of user on LCD touching display screen, the R passage in rgb space algorithm is selected in color conversion, OTSU algorithm is selected in Threshold segmentation, dilation operation is selected again after selective etching computing in morphology, find that the effect of feature extraction is undesirable, the feature extraction result of non-optimal, returns former figure;
Then Gaussian filter algorithm is selected in the pre-service of user on LCD touching display screen, the H passage of HSV space is selected in color conversion, Canny algorithm is selected in rim detection, after selecting dilation operation in morphology, find that video image effect is still undesirable, also the feature extraction result of non-optimal, returns former figure;
The H component in HSV space is selected in the color conversion of user again on LCD touching display screen, select artificial selection in Threshold segmentation and input threshold value, medium filtering is selected in pre-service, dilation operation is selected after selective etching computing in morphology, the like, can obtain until select the optimal algorithm combination that optimal characteristics extracts result.
Above embodiment is only for illustration of the present invention; and be not limitation of the present invention; the those of ordinary skill of relevant technical field; without departing from the spirit and scope of the present invention; can also make a variety of changes and modification; therefore all equivalent technical schemes also belong to category of the present invention, and scope of patent protection of the present invention should be defined by the claims.
Claims (8)
1. analyze a method for crop growing state image based on embedded device, it is characterized in that, its specific implementation process is:
One, embedded device is customized:
1) arrange Embedded Hardware Platform, this Embedded Hardware Platform comprises LCD touching display screen, AV input interface, CMOS camera interface, SD deck, USB interface, Ethernet interface, WiFi module;
2) embedded system kernel is configured, and on this embedded system kernel, application system is installed, in this application system, image processing system is set, this image processing system comprises basic function and image processing function, and described basic function comprises the next video flowing of reading shooting driver transmission, intercepts picture and man-machine interaction; Image processing function with shooting crop picture for handling object, be divided into lower module according to the general flow of crop growing state image procossing: Image semantic classification, color space conversion, Threshold segmentation, rim detection, morphology, wherein each module includes some image processing algorithms, be supplied to user to combinationally use, to obtain the result of feature extraction;
Two, embedded device is connected camera head and be placed in crop growth environment, and by WiFi model calling to network, to send the information of image acquisition and process;
Three, user is undertaken alternately by the LCD touching display screen of this embedded device and image processing system, and extract characteristics of image by image processing function wherein, the specific implementation process of this feature extraction is:
A) plant growth image is taken by camera head and is transferred to embedded device;
B) image pre-processing module in embedded device carries out pre-service to received image, to reduce the impact that illumination and picture noise are analyzed successive image, makes image become standardized images for ease of subsequent operation;
C) color conversion module carries out color space conversion to through pretreated image, makes characteristics of image can be mapped in different colours space;
D) Threshold segmentation module carries out the segmentation of binaryzation, is split by crop from background, to carry out follow-up quantitative analysis;
E) edge detection module is partitioned into the edge of crop, as the feature extracted and for follow-up quantitative analysis;
F) morphology module carries out strengthening or weakening feature, to keep the integrality of feature;
G) indicating characteristic extracts result;
H) cycling step b) is to step g), and user selects optimum feature extracting method according to display result, terminates whole characteristic extraction procedure.
2. a kind of method analyzing crop growing state image based on embedded device according to claim 1, is characterized in that, be provided with demarcation bar in described crop growth environment, as the benchmark calculating physical length with image length.
3. a kind of method analyzing crop growing state image based on embedded device according to claim 1 and 2, it is characterized in that, intermediate value is formed in described image processing system, this intermediate value is the intermediateness data of data structure storage image manipulation, this intermediate value can as the input and output of any particular algorithms, make image manipulation need not observe Image semantic classification, color space conversion, Threshold segmentation, rim detection, morphologic flow sequence and independent assortment algorithm, the abundant feature extraction result obtained.
4. a kind of method analyzing crop growing state image based on embedded device according to claim 3, it is characterized in that, the algorithm that described Image semantic classification process adopts comprises medium filtering, gaussian filtering or histogram equalization algorithm.
5. a kind of method analyzing crop growing state image based on embedded device according to claim 4, it is characterized in that, the algorithm that described color conversion processes adopts comprises rgb space algorithm or HSV space algorithm, and wherein rgb space algorithm comprises R passage, G passage and channel B; HSV space algorithm comprises H passage, channel S and V passage.
6. a kind of method analyzing crop growing state image based on embedded device according to claim 5, it is characterized in that, the algorithm that described Threshold segmentation process adopts comprises artificial selection, OTSU algorithm or iteration selection algorithm.
7. a kind of method analyzing crop growing state image based on embedded device according to claim 6, it is characterized in that, the algorithm that described edge detection process adopts comprises canny operator edge detection algorithm, LoG algorithm, Sobel algorithm or Hough transform algorithm.
8. a kind of method analyzing crop growing state image based on embedded device according to claim 7, it is characterized in that, the algorithm that described morphology process adopts comprises erosion operation or dilation operation.
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Cited By (4)
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| CN108122170A (en) * | 2017-12-21 | 2018-06-05 | 中国科学院合肥物质科学研究院 | Efficient ecological agriculture forestry planting monitors in real time and intelligent decision system |
| CN108286999A (en) * | 2018-01-24 | 2018-07-17 | 江西师范大学 | A kind of method of environmental monitoring of monitoring Forest Growth situation |
| CN109146948A (en) * | 2018-07-27 | 2019-01-04 | 内蒙古大学 | The quantization of crop growing state phenotypic parameter and the correlation with yield analysis method of view-based access control model |
| CN110849329A (en) * | 2019-10-17 | 2020-02-28 | 中国科学院遥感与数字地球研究所 | Vegetation canopy vertical structure parameter measuring method, device and system |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108122170A (en) * | 2017-12-21 | 2018-06-05 | 中国科学院合肥物质科学研究院 | Efficient ecological agriculture forestry planting monitors in real time and intelligent decision system |
| CN108286999A (en) * | 2018-01-24 | 2018-07-17 | 江西师范大学 | A kind of method of environmental monitoring of monitoring Forest Growth situation |
| CN109146948A (en) * | 2018-07-27 | 2019-01-04 | 内蒙古大学 | The quantization of crop growing state phenotypic parameter and the correlation with yield analysis method of view-based access control model |
| CN109146948B (en) * | 2018-07-27 | 2021-07-30 | 内蒙古大学 | Crop growth phenotype parameter quantification and yield correlation analysis method based on vision |
| CN110849329A (en) * | 2019-10-17 | 2020-02-28 | 中国科学院遥感与数字地球研究所 | Vegetation canopy vertical structure parameter measuring method, device and system |
| CN110849329B (en) * | 2019-10-17 | 2021-06-25 | 中国科学院遥感与数字地球研究所 | Vegetation canopy vertical structure parameter measuring method, device and system |
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