CN102509096B - Extracting and processing method for inclination angles of corn plant leaves - Google Patents
Extracting and processing method for inclination angles of corn plant leaves Download PDFInfo
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- CN102509096B CN102509096B CN201110276519.9A CN201110276519A CN102509096B CN 102509096 B CN102509096 B CN 102509096B CN 201110276519 A CN201110276519 A CN 201110276519A CN 102509096 B CN102509096 B CN 102509096B
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- milpa
- inclination angle
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Abstract
The invention provides an extracting and processing method for inclination angles of corn plant leaves. The extracting and processing method comprises the following steps of: collecting data; determining a scaling coefficient; processing an image by using a computer, obtaining inclination angle distribution of all leaves of a corn plant; and giving out an inclination angle distribution function fitting curve of the corn plant leaves. The extracting processing method integrates a digital imaging technology, a computer image processing technology, mathematical morphology, a Matlab software image tool processing module with a programming language to carry out extracting processing on the inclination angle distribution of the corn plant leaves. The inclination angle distribution function fitting curve of the corn plant leaves, obtained by the extracting processing method, has high consistency with the actually measured leaves inclination angle distribution data. The extracting and processing method has the advantages of high processing speed, high coincidence, low cost, strong repeatability and capabilities of collecting samples in a large area, giving out the inclination angle distribution function of the corn plant leaves and providing technology support for application to crop biological research, electromagnetic scattering modeling and remote sensing parametric inversion.
Description
Technical field
The invention belongs to a kind of extraction process method of inclination angles of corn plant leaves.
Background technology
Maize leaves tilt profiles is to describe the key parameter of milpa structure, and ability and the material balance of regulation and control soil-vegetation-atmosphere system are played to vital effect.From agroecology angle analysis, Leaf angle inclination distribution affects vegetation canopy energy and material recycle, regulates the micro climate of canopy inside, with macroclimate environmental interaction, can improve competition for light ability and the efficiency of light energy utilization of vegetation.In addition,, due to the relation between leaf inclination angle and Efficiency of energy conversion photosynthesis potency, being distributed in of leaf inclination angle determining whether crop is applicable to intensive plantation to a certain extent.From the angle of the electromagnetic scattering modeling of vegetation canopy and surface parameters inversion, Leaf angle inclination distribution function is one of major parameter of simulation vegetation canopy transmitance.Soil information (as soil moisture, the soil moisture etc.) is being carried in soil self heat radiation, and vegetation is relevant with vegetation transmitance (being Leaf angle inclination distribution) to thermal-radiating decay intensity, the information that therefore will receive from remote sensor is finally inversed by soil information, need to have the priori of vegetation canopy leaves tilt profiles.In addition, accurately measure Leaf angle inclination distribution and also help the ability and the precision that improve electromagnetic scattering modeling, improve the ability of extracting Land Surface Parameters from sensor information.
In sum, Leaf angle inclination distribution is significant for Agro-ecology, territory, vegetation-covered area surface parameters inversion and vegetation canopy electromagnetic scattering construction mold.Therefore, carry out the accurate measurement of Leaf angle inclination distribution very necessary.The method of measuring at present crop leaf mainly contains two kinds: one is manual measurement method; One is indirect estimation.Manual measurement method mainly carries out visual reading with the mobile device such as protractor, inclinator to leaf inclination angle, and the method is consuming time, consumption power, and cost is high, is not suitable for Quick Measurement and spread.Leaf area index and Leaf angle inclination distribution are calculated in the solar radiation (spot densities) that indirect estimation is taken the hemispherical photography of crop canopies inside by fisheye camera or projected canopy inside.The method is mainly to infer the average blade tilt of crop in conjunction with G function (leaf area index correlated variables) and statistical relationship, can not directly measure the tilt profiles of blade.(list of references, Shu-Qing Zhao, Jiang Hu, Long-Biao Guo et al., Rice leaf inclination2, a VIN3-like protein, regulates leaf angle through modulating cell division of the collar.Cell Research (2010): 1-13.
[1].Mauro A.Homem Antunes，Elizabeth A.Walter-Shea，Mark A.Mesarch，Test of an extended mathematical approach to calculate maize leaf area index and leaf angle distribution.
Agricultural and Forest Meteorology 108(2001)45-53.
[2].Sven Wagner，Marc Hagemeier，Method of segmentation affects leaf inclination angle estimation in hemispherical photography.Agricultural and Forest Meteorology 139(2006)12-24.)
Summary of the invention
The invention provides a kind of extraction process method of inclination angles of corn plant leaves.It integrates digital imaging technology, computer image processing technology, mathematical morphology, Matlab software image instrument processing module and programming language, carries out the extraction process of inclination angles of corn plant leaves.
A kind of extraction process method that the invention provides inclination angles of corn plant leaves, step and condition are as follows:
1. image data: utilize digital camera to obtain the digital color image of the milpa of two gauge points under single background condition, stamp fixed range on Maize Stem;
2. determine zoom factor: the image slices vegetarian refreshments distance that digital color image gauge point is set up and the ratio of actual range, be defined as zoom factor k;
3. utilize computing machine to carry out image processing: (1) image cropping: to extract the minimum image that comprises whole milpa in digital color image; (2) image gray processing: the digital color image after cutting is converted to gray level image; (3) image binaryzation: be black and white binary image by greyscale image transitions; (4) skeleton image of whole milpa is extracted in the mathematical morphology operation of carrying out black and white binary image; (5) extract milpa blade skeleton image;
4, obtain the vaned tilt profiles of milpa:
(1) obtain the pixel coordinate data of each blade of milpa: utilize the blade skeleton image of the black and white binary image that labeling method obtains step 3 to be divided into different regions, the number n that number of regions equals blade, sets up two-dimensional array A
ij(x
i, y
i), array all elements being all initialized as to 0, j and representing j the blade, the span of j is [1, n], n is greater than 1 integer; I is i the pixel that j blade skeleton comprises, and the span of i is [1, m], and m is greater than 1 integer; By putting in order of coordinate points on blade skeleton, by all pixel coordinate (x of each blade
i, y
i) store corresponding two-dimensional array A into
ij(x
i, y
i) in;
(2) coordinate data conversion: the zoom factor k that step 2 is obtained and two-dimensional array A
ij(x
i, y
i) in storage blade pixel coordinate data multiply each other, obtain the actual coordinate data of blade, deposit in array B
ijin, B
ij=k × A
ij;
(3) by blade sample interval M, blade is carried out to segmentation, and calculate the leaf inclination angle of every section of blade; The specific formula for calculation of the leaf inclination angle theta of each segmentation is as follows:
Wherein,
the vector that subsection blade starting point and terminal point coordinate form for this reason,
for the vector of unit length of x axle positive dirction;
(4) repeating step (3), until institute's vaned leaf inclination angle extraction process of milpa is complete;
(5) obtain all leaves of milpa inclination angle with and distribution function; All leaf inclination angles that obtain are added up taking 3 ° as interval, statistics is normalized, obtain the probability distribution of every 3 ° of interval angles, draw corresponding histogram; Carry out Function Fitting, obtain Leaf angle inclination distribution function.
Beneficial effect: a kind of extraction process method that the invention provides inclination angles of corn plant leaves.It integrates digital imaging technology, computer image processing technology, mathematical morphology, Matlab software image instrument processing module and programming language, the extraction process of carrying out inclination angles of corn plant leaves distribution.For verifying the accuracy of the disposal route that milpa blade degree of tilt of the present invention distributes, the Leaf angle inclination distribution data of utilizing spline actual measurement Leaf angle inclination distribution data and disposal route of the present invention to obtain contrast.Within the scope of contrasting 0-90 °, taking 3 ° as interval stats, obtain the scatter diagram (seeing Fig. 7) between the leaf inclination angle value of measured value leaf inclination angle and method extraction process of the present invention.The Leaf angle inclination distribution of method extraction process of the present invention and the Leaf angle inclination distribution of actual measurement are 1.74 in the mean deviation of the statistics number of each angle, and the average error of calculating is 6.1%.Measured value and inclination angles of corn plant leaves distribution function match value (seeing Fig. 8) that relatively inclination angles of corn plant leaves distributes, find that the two has high consistency.For verifying the anastomose property of result of method extraction process inclination angles of corn plant leaves of the present invention, specificly select 3 ° as statistical interval, and general angle intervals can not be less than 5 ° in actual applications, from statistics, along with the increase of statistical interval, between result of calculation and actual measured results, anastomose property will further improve.It is fast that method of the present invention has processing speed, anastomose property is high, cost is low, repeatability is strong, can large area sampling, provide inclination angles of corn plant leaves distribution function, for the application of crop ecological research, electromagnetic scattering modeling and Remote sensing parameters inverting provides technical support.
Brief description of the drawings
Fig. 1 utilizes digital camera to obtain the milpa digital color image under single background condition.
Fig. 2 is the milpa gray level image obtaining by method of the present invention.
Fig. 3 is the milpa black and white binary image obtaining by method of the present invention.
Fig. 4, for to carry out morphology processing according to black and white binary image, fills blade " hole " black and white binary image afterwards.
Fig. 5 is the bianry image of the milpa skeleton that obtains by method of the present invention.
Fig. 6 is the bianry image of the milpa blade skeleton that obtains by method of the present invention.
Fig. 7 is the scatter diagram that utilizes spline actual measurement Leaf angle inclination distribution data and the Leaf angle inclination distribution that obtains by method of the present invention.Each numerical value is to obtain taking 3 ° as interval stats within the scope of 0-90 °, is with foursquare solid line
for the leaf inclination angle statistical value obtaining by method of the present invention, with the dotted line of circle
for utilizing spline actual measurement Leaf angle inclination distribution statistical value.Relatively find: the latter's mean deviation is 1.74, and the former average error is 6.1%.
Fig. 8 is actual measurement and the distribution function curve map at the leaf inclination angle that obtains by method of the present invention.Solid line
represent to utilize the Logistic curve of this method calculated value matching, circle (zero) represents actual measured value.
Embodiment
Embodiment 1 the invention provides a kind of specific embodiments of extracting method of milpa blade tilt profiles:
1, image data: gather milpa blade digital picture, on Maize Stem, stamp two marks of distance for 20cm, and stood upright on before the object of single background, utilize digital camera to carry out plant leaf image acquisition, thereby get the milpa blade color digital image with range mark;
2, determine zoom factor: utilize MATLAB software, the color digital image that step 1 is gathered, recording distance is the pixel coordinate (x, y) of two gauge points of 20cm, adopt at 2 between the computing formula of air line distance obtain the image distance between two gauge points; The ratio of image distance and actual range is exactly the zoom factor k of the two;
3, utilize computing machine to carry out image processing: (1) image cropping: to extract the minimum image that comprises whole milpa in digital color image, utilize the imcrop image cropping order in MATLAB to realize, (2) image gray processing: the coloured image after cutting is converted to gray level image, utilizes the rgb2gray order in MATLAB to realize, (3) image binaryzation: be black and white binary image by greyscale image transitions, utilize the im2bwm order in MATLAB to realize, (4), extract whole milpa skeleton image: planar blade is refined as to blade centreline, first utilize the Expanded Operators in morphology processing, " hole " in the binaryzation black white image of blade filled, till being expanded to " hole " all in blade being filled up, record total expansion number of times, the anti-operation form expanding is afterwards learned corrosion, the number of times of corrosion is identical with expansion number of times, the large image restoring of change that is about to fill up " hole " is original image size, utilize afterwards the bwmorph command calls approach for binary image thinning operation in MATLAB software, thereby obtain the skeleton image of whole strain density, (5) extract milpa blade skeleton image: the whole milpa skeleton image of the framework information that comprises maize leaf and Maize Stem obtaining in step (4), utilize the framework information of area-of-interest select command roipoly removal Maize Stem, only comprised the skeleton image of maize leaf,
4, obtain the vaned tilt profiles of milpa:
(1) obtain the pixel coordinate data of each blade of milpa: utilize the blade skeleton image of the black and white binary image that labeling method obtains step 3 to be divided into different regions, the number n that number of regions equals blade, sets up two-dimensional array A
ij(x
i, y
i), array all elements being all initialized as to 0, j and representing the blade, the span of j is [1, n], n is greater than 1 integer; I is the number of the pixel that comprises of j blade skeleton, and the span of i is [1, m], and m is greater than 1 integer; By putting in order of coordinate points on blade skeleton, by all pixel coordinate (x of each blade
i, y
i) store corresponding array A into
ij(x
i, y
i) in;
(2) coordinate data conversion: the coordinate zoom factor that step B is obtained and array array A
ij(x
i, y
i) in storage blade pixel coordinate data multiply each other, obtain the actual coordinate data of blade, leave array B in
ijin, B
ij=k × A
ij;
(3) by blade sample interval M, blade is carried out to segmentation, and calculate the leaf inclination angle of every section of blade; First starting point A of j blade
1jcoordinate be (x
1, y
1), second the some A adjoining
2jcoordinate be (x
2, y
2), establish A
1j(x
1, y
1) and A
2j(x
2, y
2) between distance be D1, if | M-D1| is less than 0.1, calculates so A
1j(x
1, y
1) and A
2j(x
2, y
2) straight line forming and the leaf inclination angle 1 of x axle formation; Then by second some A
2j(x
2, y
2) as new starting point, continue search and second the 3rd some A that point adjoins
3j(x
3, y
3), establish A
2j(x
2, y
2) and A
3j(x
3, y
3) between distance be D2, if | M-D2| is less than 0.1, calculates so A
2j(x
2, y
2) and A
3j(x
3, y
3) straight line forming and the leaf inclination angle 2 of x axle formation; The like, until search the last point A of j blade
mj(x
m, y
m), calculate A
m-1(x
m-1, y
m-1) and A
mj(x
m, y
m) straight line forming and the leaf inclination angle of x axle formation; The specific formula for calculation of leaf inclination angle theta is as follows:
Wherein,
for A
ij(x
i, y
i) and A
ij(x
i+1, y
i+1) form vector,
for the vector of unit length of x axle positive dirction;
(4) repeating step (3), until institute's vaned leaf inclination angle extraction process of milpa is complete;
(5) obtain all leaf inclination angles of milpa with and distribution function: all leaf inclination angles that obtain are added up taking 3 ° as interval, statistics are normalized, obtain the probability distribution of every 3 ° of interval angles, draw corresponding histogram; Carry out Function Fitting, obtain Leaf angle inclination distribution function.
The probability distribution function that the present embodiment obtains is Logistic curve, and its mathematic(al) representation is as follows:
For verifying the accuracy of milpa blade tilt profiles function extracting method of the present invention, the Leaf angle inclination distribution data of utilizing the extraction process method of actual measurement Leaf angle inclination distribution data and a kind of inclination angles of corn plant leaves of the present invention to obtain contrast.Actual blade tilt is to utilize spline to be measured each blade.Scatter diagrams (seeing Fig. 7) between the leaf measurement of dip angle value that the measured value obtaining taking 3 ° as interval stats within the scope of contrasting 0-90 ° and disposal route of the present invention are extracted, the mean deviation of measured value is 1.74, and the leaf measurement of dip angle value average error that disposal route of the present invention is extracted is 6.1%.The measured value function matched curve value (seeing Fig. 8) that relatively inclination angles of corn plant leaves distributes, finds that the two has high consistency.For verifying the precision of disposal route measurement result of the present invention, specificly select 3 ° as statistical interval, and general angle intervals can not be less than 5 ° in actual applications, from statistics, along with the increase of statistical interval, between result of calculation and actual measured results, anastomose property will further improve.The advantages such as it is fast that disposal route of the present invention has processing speed, and cost is low, repeatability is strong, are conducive to quick, the large area sampling of Leaf angle inclination distribution function, are the application demand of crop ecological research, electromagnetic scattering modeling and Remote sensing parameters inverting.
Claims (1)
1. an extraction process method for inclination angles of corn plant leaves, step and condition are as follows:
. image data: utilize digital camera to obtain the digital color image of the milpa of two gauge points under single background condition, stamp fixed range on Maize Stem;
. determine zoom factor: the image slices vegetarian refreshments distance that digital color image gauge point is set up and the ratio of actual range, be defined as zoom factor k;
C. utilize computing machine to carry out image processing: (1) image cropping: to extract the minimum image that comprises whole milpa in digital color image; (2) image gray processing: the digital color image after cutting is converted to gray level image; (3) image binaryzation: be black and white binary image by greyscale image transitions; (4) carry out the mathematical morphology operation of black and white binary image, extract the skeleton image of whole milpa; (5) extract milpa blade skeleton image;
, obtain the vaned tilt profiles of milpa:
(1) obtain the pixel coordinate data of each blade of milpa: utilize labeling method by step
the blade skeleton image of the black and white binary image obtaining is divided into different regions, and the number n that number of regions equals blade, sets up two-dimensional array A
ij(x
i, y
i), array all elements is all initialized as 0, j and represents j blade, and the span of j is [1, n], and n is greater than 1 integer; I is i the pixel that j blade skeleton comprises, and the span of i is [1, m], and m is greater than 1 integer; By putting in order of coordinate points on blade skeleton, by all pixel coordinate (x of each blade
i, y
i) store corresponding array A into
ij(x
i, y
i) in;
(2) coordinate data conversion: by step
the zoom factor k obtaining and array A
ij(x
i, y
i) in storage blade pixel coordinate data multiply each other, obtain the actual coordinate data of blade, deposit in array B
ijin, B
ij=k × A
ij;
(3) by blade sample interval M, blade is carried out to segmentation, and calculate the leaf inclination angle of every section of blade; The specific formula for calculation of the leaf inclination angle theta of each segmentation is as follows:
(1)
Wherein,
the vector that subsection blade starting point and terminal point coordinate form for this reason,
for the vector of unit length of x axle positive dirction;
(4) repeating step (3), until institute's vaned leaf inclination angle extraction process of milpa is complete;
(5) obtain all leaves of milpa inclination angle with and distribution function; All leaf inclination angles that obtain are added up taking 3 ° as interval, statistics is normalized, obtain the probability distribution of every 3 ° of interval angles, draw corresponding histogram; Carry out Function Fitting, obtain Leaf angle inclination distribution function.
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| CN107016674A (en) * | 2017-02-28 | 2017-08-04 | 中国农业大学 | A kind of corn emergence rate computational methods and device based on unmanned aerial vehicle remote sensing images |
| CN108871235A (en) * | 2018-04-27 | 2018-11-23 | 江南大学 | The information acquisition method and plant leaf information acquisition system of plant leaf |
| 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 |
| CN110849262A (en) * | 2019-10-17 | 2020-02-28 | 中国科学院遥感与数字地球研究所 | Vegetation phenotype structure parameter measuring method, device and system |
| CN112634286A (en) * | 2020-12-24 | 2021-04-09 | 宁波视睿迪光电有限公司 | Image cropping method and device |
| CN114549551A (en) * | 2022-02-15 | 2022-05-27 | Oppo广东移动通信有限公司 | Image cutting method and device, terminal and readable storage medium |
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| CN101177683A (en) * | 2007-11-20 | 2008-05-14 | 中国水稻研究所 | Rice leaf morphogenesis regulatory gene RLAL1 and uses thereof |
| CN101921777A (en) * | 2010-08-31 | 2010-12-22 | 浙江省农业科学院 | Application of rice leaf inclination control gene SAL1 |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN101177683A (en) * | 2007-11-20 | 2008-05-14 | 中国水稻研究所 | Rice leaf morphogenesis regulatory gene RLAL1 and uses thereof |
| CN101921777A (en) * | 2010-08-31 | 2010-12-22 | 浙江省农业科学院 | Application of rice leaf inclination control gene SAL1 |
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