CN104200457A - Wide-angle camera shooting based discrete type canopy leaf area index detection system and method - Google Patents
Wide-angle camera shooting based discrete type canopy leaf area index detection system and method Download PDFInfo
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Abstract
The invention discloses a wide-angle camera shooting based discrete type canopy leaf area index detection system and method. The wide-angle camera shooting based discrete type canopy leaf area index detection system comprises a computer, a wide-angle camera which is connected with the computer and a lifting mechanism; the lifting mechanism comprises a vertical lifting rod and a horizontal sliding rod; the horizontal sliding rod is installed on the vertical lifting rod; the wide-angle camera which is installed on the horizontal sliding rod can move on the horizontal sliding rod. According to the wide-angle camera shooting based discrete type canopy leaf area index detection method, color figure canopy images are obtained through the wide-angle camera, grey level transformation, image intensification, filtering processing, binarization processing and porosity extraction are sequentially performed on the color figure canopy images, finally the canopy leaf area index is calculated according to the canopy porosity. The wide-angle camera shooting based discrete type canopy leaf area index detection method has the advantages of being suitable for leaf area index calculation of discrete type canopies which are uneven in distribution, accurate in detection result, reducing incoming noise influence to data during the image processing process and eliminating the leading interference of the image processing process.
Description
Technical field
The present invention relates to a kind of leaf area index detection technique, particularly a kind of discrete type canopy leaf area index detection system and method based on wide-angle imaging.
Background technology
Leaf area index LAI (Leaf Area Index) refers to the total area of crop leaf and the ratio of floor area on plot, be an important structure parameter of the ecosystem, be used for reflecting plant leaf surface quantity, canopy structure variation, plant community vitality and environmental effect thereof.Research object in present stage to leaf area index be mainly for crops such as the comparatively single uniform paddy rice of canopy, wheat, forests, and the fruit tree that is discrete type for canopy is studied less.At present generally higher for detection of the equipment cost of fruit tree canopy LAI, and operating process complexity, the impact of examined environment is large, has therefore limited its popularization.In recent years, image processing techniques development is very fast, and its method and algorithm are also constantly in innovation, and the analysis software instrument of application image treatment technology also emerges in an endless stream, and applies also more and more extensive.Along with updating of digital photographic technology and image processing software, utilizing computer system to adopt method easy and with low cost to measure leaf area becomes possibility.
In the prior art, while carrying out leaf area index detection for single uniform canopies such as paddy rice, conventionally adopt the two large characteristics of counting projection function based on canopy leaves such as Beer-Lambert law and Poisson model and paddy rice, calculate the leaf area index of the canopies such as paddy rice, can complete rice LAI Real-time Obtaining, pluck rice leaf without field and carry out destructive manual measurement.But the Poisson model that this leaf area index method adopts can only be processed for the canopy being evenly distributed, for the comparatively complicated discrete canopy of skewness structure, the measuring accuracy of this method is lower.
Summary of the invention
The first object of the present invention is to overcome the shortcoming of prior art with not enough, provide a kind of simple in structure, function is single-minded, cost is low, easy to operate, work efficiency is high and the measurement result discrete type canopy leaf area index detection system based on wide-angle imaging more accurately.
The present invention's the second object is to provide a kind of leaf area index detection method based on said system.The method adopts the mode of image processing to get the leaf area index of discrete type canopy, reduces the impact of the noise of introducing in image processing process on data, gets rid of topmost interference, can record and have advantages of more accurate leaf area index measured value.
The first object of the present invention is achieved through the following technical solutions: a kind of discrete type canopy leaf area index detection system based on wide-angle imaging, comprise computing machine, the wide-angle camera being connected with computing machine and elevating mechanism, described elevating mechanism comprises VTOL (vertical take off and landing) bar and horizontal slide rod, described horizontal slide rod is arranged on VTOL (vertical take off and landing) bar, wide-angle camera is arranged in horizontal slide rod, can in horizontal slide rod, move.
Preferably, VTOL (vertical take off and landing) bar is connected by steering angle with horizontal slide rod, and wherein horizontal slide rod is fixed on steering angle, and steering angle is fixed on VTOL (vertical take off and landing) bar by inner-angle nut, adjusts the position of horizontal slide rod on VTOL (vertical take off and landing) bar by inner-angle nut.
The second object of the present invention is achieved through the following technical solutions:
Discrete type canopy leaf area index based on wide-angle imaging detects a detection method, and step is as follows:
S1, colorful digital canopy Image Acquisition: wide-angle camera is placed in to canopy top, starts computing machine, detected and caught image and take by wide-angle camera, wide-angle camera will photograph colorful digital canopy image and be sent to computing machine;
S2, greyscale transformation: the colorful digital canopy image that wide-angle camera is photographed converts gray level image to;
S3, figure image intensifying: adopt various image enhancement processing modes to carry out respectively image enhancement processing for the gray level image obtaining in step S2, final according to the best image of image grey level histogram selective enhancement effect after figure image intensifying;
S4, filtering processing: the best image of figure image intensifying effect obtaining in step S3 is carried out to filtering processing;
S5, image binaryzation: by step S4 after filtering image after treatment carry out binary conversion treatment, wherein selected first threshold, judge through the gray-scale value of each pixel in step S4 filtering image after treatment whether be less than first threshold, comprise blade and two kinds of pixels of background so that image is divided into;
If so, this pixel is incorporated into as backdrop pels;
If not, this pixel is incorporated into into blade pixel;
S6, analysis of porosity: select Second Threshold, the image according to this Second Threshold after to step S5 binaryzation carries out porosity to be cut apart, by the canopy hole image element extraction in image out; Wherein gray-scale value is greater than to the division hole pixel of Second Threshold;
S7, porosity calculate: obtain the quantity of image Leaf and canopy hole pixel according to step S5 and S6, calculate the canopy porosity under wide-angle photography image list angle zenith angle θ=57.5 ° according to Beer-Lambert theorem;
Wherein T (θ) is the factor of porosity of single angle zenith angle θ lower canopy, P
0(θ) be single angle zenith angle θ lower canopy hole pixel quantity, P
1(θ) be single angle zenith angle θ lower blade pixel quantity;
S8, calculating leaf area index: according to the leaf area index that is related to canopy in computed image of image canopy porosity and leaf area index in Beer-Lambert theorem.
Preferred: in described step S2, to select maximum value process, the method for average or the method for weighting to carry out gradation conversion to colorful digital canopy image.
Preferred: the each image enchancing method adopting in described step S3 is Gray Histogram linear transformation method, histogram equalization method and limited contrast adaptive histogram equalization method.
Preferred: in described step S4, to adopt the image after low-pass filter strengthens the canopy obtaining in step S3 to carry out medium filtering processing: the pixel in low-pass filter window, by gray level sequence, is got to the gray-scale value of its intermediate value as parent window center pixel.
Preferably, in described step S5, by selected first threshold, image is divided into and comprises blade and two kinds of pixels of background, its Leaf pixel is shown as white, and background pixel unit is shown as black.
Preferably, in described step S5, determine first threshold by maximum between-cluster variance adaptive approach and/or manual method when image binaryzation.
Preferably, in described step S6, adopt maximum between-cluster variance adaptive approach and/or manual method to determine Second Threshold; In the time adopting maximum between-cluster variance adaptive approach and manual method to determine Second Threshold simultaneously, if the definite Second Threshold of maximum between-cluster variance adaptive approach carries out after porosity cuts apart image, in the time that the error of the porosity of extraction and the porosity of canopy reality exceedes certain value, adopt manual method to reselect Second Threshold porosity is extracted; The canopy hole pixel wherein extracting in step S6 is white, and other pixels are black.
Preferably, in described step S8, leaf area index LAI is:
Wherein G (θ) is averaging projection's function, i.e. the projected area of unit blade in θ direction, and it is relevant with the Leaf angle inclination distribution of canopy intra vane, and wherein, in the time of θ=57.5 °, G (θ) is 0.5.
The present invention has following advantage and effect with respect to prior art:
(1) the present invention adopts the relatively low wide-angle camera of cost to obtain colorful digital canopy image, carry out successively greyscale transformation, figure image intensifying, filtering processing, binary conversion treatment and porosity for colorful digital canopy image again and extract, finally calculate the leaf area index of canopy according to canopy porosimeter.In the present invention, pass through the processing mode of figure image intensifying, discrete type canopy green portion can be highlighted from background and noise, and soil and noise are suppressed, be applicable to the canopy of discrete type pockety to carry out the calculating of leaf area index, there is testing result advantage more accurately.And the present invention can reduce the impact of the noise of introducing in image processing process on data, topmost interference in rejection image processing procedure.
(2) leaf area determination system of the present invention simple in structure, function is single-minded, cost is low, easy to operate, work efficiency is high and measurement result is more accurate; System of the present invention can be controlled height and the position of wide-angle camera flexibly by elevating mechanism in addition, and display measurement result that can be real-time by computing machine.Wide-angle camera coverage of the present invention is from 2cm to infinite distance, can under comparatively complicated outdoor conditions, obtain image comparatively clearly, and its lens coverage is wider, can be at the wider image of small space coverage.
Brief description of the drawings
Fig. 1 is system composition schematic diagram of the present invention
Fig. 2 is the inventive method process flow diagram.
Fig. 3 a and 3b are respectively the gray level images that the inventive method is prized after chromatic number prefix tomographic image and conversion.
Fig. 4 a to 4c is respectively and in the inventive method, adopts three kinds of image enhancement method gray level images after treatment.
Fig. 5 a to 5c is the human-computer interaction interface figure of the present invention in MATLAB software.
Embodiment
Below in conjunction with embodiment and accompanying drawing, the present invention is described in further detail, but embodiments of the present invention are not limited to this.
Embodiment
As shown in Figure 1, the present embodiment discloses a kind of discrete type canopy leaf area index detection system based on wide-angle imaging, the wide-angle camera 3 and the elevating mechanism that comprise computing machine 1, be connected with computing machine by data line, elevating mechanism comprises VTOL (vertical take off and landing) bar 2 and horizontal slide rod 4, in horizontal slide rod 4, be provided with stationary installation, wide-angle camera 3 is arranged in the stationary installation in horizontal slide rod 4, wherein this stationary installation can move in horizontal slide rod 4, so that wide-angle camera 3 can be moved in the horizontal direction.VTOL (vertical take off and landing) bar 2 lower ends are fixed on bracing frame 6, VTOL (vertical take off and landing) bar is connected by steering angle with horizontal slide rod, horizontal slide rod is fixed on steering angle, steering angle is fixed on VTOL (vertical take off and landing) bar 2 by inner-angle nut, adjust the position of horizontal slide rod on VTOL (vertical take off and landing) bar by inner-angle nut, in the time need to adjusting the height of horizontal slide rod 4, can suitably unscrew inner-angle nut, now horizontal slide rod 3 with respect to VTOL (vertical take off and landing) bar 2 in sliding mode, in the time sliding into appropriate location, can be by fixing inner-angle nut to reach the object of fixing horizontal sliding bar.In the present embodiment, VTOL (vertical take off and landing) bar is marked with scale, and scale is accurate to mm, and highest point can reach 2m.Horizontal slide rod can be adjusted to suitable height according to the different fruit tree types layer distribution pattern of coming of age.
VTOL (vertical take off and landing) bar 2, horizontal slide rod 4 and bracing frame 6 parts all adopt aluminium alloy steel pipe in the present embodiment, and each parts connect by steering angle, can repeat dismounting utilization, have anticorrosive, low price, the advantage such as light.
The wide-angle camera adopting in the present embodiment can obtain 120 ° of images with interior object, and camera lens adopts the indeformable wide-angle lens of high-quality, supports dynamically and still image seizure; Image rate was 30 frame/seconds, and 64 of high-quality are very color, and interpolating pixel reaches more than 2,000 ten thousand; Maximum dynamic resolution is 1280 × 960; Sensor devices adopts high-quality CMOS, super CCD photosensitive effect, VGA/CIF form; Scan pattern is line by line scan/staggered scanning; Can carry out auto-exposure control, automatic gain control, Automatic white balance and Gamma correction processing to image; Coverage is from 2cm to infinite distance.Can under comparatively complicated outdoor conditions, obtain image comparatively clearly.Its lens coverage is wider, can be at the wider image of small space coverage.
As shown in Figure 2, the present embodiment also discloses a kind of discrete type canopy leaf area index detection method based on wide-angle imaging, by the method, mandarin tree is carried out to leaf area determination, and concrete steps are as follows:
S1, colorful digital canopy Image Acquisition: wide-angle camera is placed in directly over canopy, start computing machine, detect and catch image and take by wide-angle camera, wide-angle camera will photograph colorful digital canopy image and be sent in computing machine, form with JPEG is stored in hard disc of computer, and carries out the processing of following steps by the Matlab software in computing machine; The colorful digital canopy image obtaining in the present embodiment as shown in Figure 3 a.
S2, greyscale transformation: the colorful digital canopy image that wide-angle camera is photographed converts gray level image to, the gray level image of the present embodiment conversion as shown in Figure 3 b.The image that camera collects is at first 24 true color images, gray-scale map is 8 bit images, only have 256 gray levels, gray level image is exactly said black white image at ordinary times, it is to allow R value, G value, B value in image equate that coloured image is converted to gray-scale map, so just image can be converted to gray-scale map, wherein in embodiment, adopt the method for average to carry out greyscale transformation to colorful digital canopy image, can certainly adopt maximum value process or Quan Zhongfa etc. to carry out greyscale transformation to colorful digital canopy image.The method of average, the gray-scale map that is exactly each pixel of image is the mean value of this R value, G value, B value; Maximum value process, just refers to that the gray-scale value of each pixel is the maximal value in this R value, G value, B value; The method of weighting, refers to that it is the gray-scale value of this point that the R value of each pixel of image, G value, B value are multiplied by respectively to the value obtaining after different weighted values.
S3, figure image intensifying: adopt Gray Histogram linear transformation method, histogram equalization method and limited contrast adaptive histogram equalization respectively the gray level image obtaining in step S2 to be strengthened to processing, by image enhancement processing, each canopy green portion is highlighted from background and noise, and soil and noise are suppressed; And the grey level histogram of three kinds of image enhancement method images after treatment more than setting up in Matlab software, final according to the best image of image grey level histogram selective enhancement effect after figure image intensifying.Wherein in grey level histogram presentation video, there is the number of pixels of each gray level, reflect the frequency of the pixel appearance of every kind of grey level in image, therefore can judge the effect of figure image intensifying by grey level histogram; Above-mentioned three kinds of image enhancement processing mode results are as shown in Figs. 4a-c respectively in the present embodiment, known by contrasting, the effect difference of different images enhancement method, the picture contrast that wherein Gray Histogram linear transformation method strengthens is best, and histogram equalization has increased partial noise, make the more difficult differentiation of color of blade and background; The enhancing DeGrain of limited contrast self-adapting histogram equalization method to blade, blade gray-scale value does not change substantially, so for this experiment, take Gray Histogram linear transformation to strengthen image.
S4, filtering processing: the best image of figure image intensifying effect obtaining in step S3 is carried out to filtering processing; Due to the complicacy of mandarin tree canopy background; processing procedure is easy to introduce noise; obtain usually having in bianry image the existence of some isolated picture dots; in the photosignal transfer process of camera, also may produce quantization error and channel errors etc. in addition; in order to eliminate these noises; make image more level and smooth, the present embodiment adopts low-pass filter to carry out medium filtering processing to image, also can remove noise when protection the image border.When the present embodiment medium filtering is processed, the pixel in low-pass filter window is sorted by gray level, get the gray-scale value of its intermediate value as parent window center pixel.
S5, image binaryzation: by step S4 after filtering image after treatment carry out binary conversion treatment, wherein selected first threshold, judge through the gray-scale value of each pixel in step S4 filtering image after treatment whether be less than first threshold, comprise blade and two kinds of pixels of background so that image is divided into;
If so, this pixel is incorporated into as backdrop pels, wherein backdrop pels is black image,
If not, this pixel is incorporated into into blade pixel, its Leaf pixel is white image.
In the present embodiment, determine first threshold by maximum between-cluster variance adaptive approach and artificial establishing method, that adaptive approach has is quick, objective, advantage easily, although and manually method program is lengthy and tedious, can be supplementing and proofreading and correct adaptive approach.When artificial establishing method is set first threshold in the present embodiment, according to grey level histogram data, select suitable first threshold, blade and two kinds of pixels of background are divided.
S6, analysis of porosity: determine Second Threshold by maximum between-cluster variance adaptive approach, image according to this Second Threshold after to step S5 binaryzation carries out porosity to be cut apart, by the canopy hole image element extraction in image out, the canopy hole pixel wherein extracting is white image, and other pixels are black image.
In this step, adopting maximum between-cluster variance adaptive approach definite Second Threshold image to be carried out after porosity cuts apart, if the error of the porosity of extracting and the porosity of canopy reality differs larger, while exceeding certain value, adopt manual method to reselect Second Threshold porosity is extracted, by proofreading and correct by artificial means.
S7, porosity calculate: obtain the quantity of image Leaf and canopy hole pixel according to step S5 and S6, calculate the canopy porosity under wide-angle photography image list angle zenith angle θ=57.5 ° according to Beer-Lambert theorem;
Wherein T (θ) is the factor of porosity of single angle zenith angle θ lower canopy, P
0(θ) be single angle zenith angle θ lower canopy hole pixel quantity, P
1(θ) be single angle zenith angle θ lower blade pixel quantity;
S8, calculating leaf area index: the leaf area index LAI that is related to canopy in computed image according to image canopy porosity and leaf area index in Beer-Lambert theorem:
Wherein G (θ) is averaging projection's function, i.e. the projected area of unit blade in θ direction, and it is relevant with the Leaf angle inclination distribution of canopy intra vane, and wherein, in the time of θ=57.5 °, G (θ) is 0.5.
The maximum variance between clusters adopting in the present embodiment step S5 and S6 is again Otsu algorithm, maximum variance between clusters is processed the pixel in image to be divided into two classes with a certain threshold value t by gray scale, the first kind by gray-scale value [0, t] between pixel composition, Equations of The Second Kind by gray-scale value at [t+1,255] pixel between forms, and then calculates the inter-class variance σ (t) of two classes
2:
σ(t)
2＝w
1(t)w
2(t)[u
1(t)-u
2(t)]
2；
Wherein w
1(t) pixel count for comprising in the first kind, w
2(t) pixel count for comprising in Equations of The Second Kind, u
1(t) be the average gray value of pixel in the first kind, u
2(t) be the average gray value of pixel in Equations of The Second Kind.The wherein variances sigma between two classes (t)
2show that more greatly image segmentation is better, therefore in the present embodiment t in [0,255] scope value successively, as σ (t)
2during for maximum, decision threshold t.
The discrete type canopy leaf area index detection method based on wide-angle imaging of the present embodiment is carried out in the Matlab of computing machine software, wherein Matlab software utilizes visualization interface environment Guide to programme according to said method, as shown in Figure 5 a, there is good interpersonal interactive interface, on human-computer interaction interface, comprise system display interface, image acquisition interface, image pre-service interface, characteristics of image identification interface and five modules of result display interface.
The button of image acquisition comprises " image preview ", " photographic images ", " preservation image " and " reading images ".Click " image preview " in interface, motion capture mandarin tree image continuously, and be presented in first display window.Click " photographic images ", can take canopy image, and click " preservation image " can be stored in hard disc of computer with jpeg format.The image of taking due to camera is YUY2 form, and the image that system can be identified is rgb format, so system program need to carry out type conversion to the image photographing.Clicking " reading images " simultaneously and also can import existing canopy image in computing machine, is mainly the photograph of jpeg format.
Image pretreatment module comprises " histogram " button, " input blade detection threshold " input frame and " blade detection " button, is written into after image, and system can be carried out gray processing processing to the image of calling in.Click the distribution situation that " histogram " can draw the each pixel of image.If use automatic threshold to carry out binary conversion treatment, directly click " blade detection ", now the present embodiment said method adopts Otsu algorithm to get first threshold, draws the image after binaryzation by this first threshold.Also can select in addition artificial setting threshold to carry out binaryzation, according to histogrammic data, select suitable threshold value, and be input in " the input blade detection threshold " input frame in interface, then click " blade detection ", the first threshold in now the present embodiment said method employing input frame draws the picture of binaryzation.As shown in Figure 5 b, the image after the value of the present embodiment method two is presented in second display window of interpersonal interactive interface.
Characteristics of image identification interface comprise " input hole detection threshold " input frame, " porosity detection " button with " LAI detection " button, using the image of Second Threshold after to step S5 binaryzation to carry out porosity while cutting apart, click " porosity detection ", as shown in Figure 5 c, system will be cut apart situation second display window demonstration porosity, wherein canopy hole pixel is white image, and other pixels are black image.The hole drawing if find and the hole error of canopy reality are larger, can proofread and correct by artificial means, again in " input hole detection threshold " input frame, input Second Threshold, then carry out porosity extraction, until draw desirable hole.
In result display interface, comprise " porosity ", " LAI value " display box, porosity and the LAI value of the present embodiment step S7 and step S8 calculating are shown respectively in these two display boxes.Therefore the present embodiment can pass through Matlab software Real-time Obtaining leaf area index testing result.
Above-described embodiment is preferably embodiment of the present invention; but embodiments of the present invention are not restricted to the described embodiments; other any do not deviate from change, the modification done under Spirit Essence of the present invention and principle, substitutes, combination, simplify; all should be equivalent substitute mode, within being included in protection scope of the present invention.
Claims (10)
1. the discrete type canopy leaf area index detection system based on wide-angle imaging, comprise computing machine, it is characterized in that, also comprise the wide-angle camera and the elevating mechanism that are connected with computing machine, described elevating mechanism comprises VTOL (vertical take off and landing) bar and horizontal slide rod, described horizontal slide rod is arranged on VTOL (vertical take off and landing) bar, and wide-angle camera is arranged in horizontal slide rod, can in horizontal slide rod, move.
2. the discrete type canopy leaf area index detection system based on wide-angle imaging according to claim 1, it is characterized in that, VTOL (vertical take off and landing) bar is connected by steering angle with horizontal slide rod, wherein horizontal slide rod is fixed on steering angle, steering angle is fixed on VTOL (vertical take off and landing) bar by inner-angle nut, adjusts the position of horizontal slide rod on VTOL (vertical take off and landing) bar by inner-angle nut.
3. the detection method based on the discrete type canopy leaf area index detection system based on wide-angle imaging claimed in claim 1, is characterized in that, step is as follows:
S1, colorful digital canopy Image Acquisition: wide-angle camera is placed in to canopy top, starts computing machine, detected and caught image and take by wide-angle camera, wide-angle camera will photograph colorful digital canopy image and be sent to computing machine;
S2, greyscale transformation: the colorful digital canopy image that wide-angle camera is photographed converts gray level image to;
S3, figure image intensifying: adopt various image enhancement processing modes to carry out respectively image enhancement processing for the gray level image obtaining in step S2, finally judge figure image intensifying effect according to the image grey level histogram after figure image intensifying, and the best image of selective enhancement effect;
S4, filtering processing: the image best to the enhancing effect of obtaining in step S3 carries out filtering processing;
S5, image binaryzation: by step S4 after filtering image after treatment carry out binary conversion treatment, wherein selected first threshold, judge through the gray-scale value of each pixel in step S4 filtering image after treatment whether be less than first threshold, comprise blade and two kinds of pixels of background so that image is divided into;
If so, this pixel is incorporated into as backdrop pels;
If not, this pixel is incorporated into into blade pixel;
S6, analysis of porosity: select Second Threshold, the image according to this Second Threshold after to step S5 binaryzation carries out porosity to be cut apart, by the canopy hole image element extraction in image out; Wherein gray-scale value is greater than to the division hole pixel of Second Threshold;
S7, porosity calculate: obtain the quantity of image Leaf and canopy hole pixel according to step S5 and S6, calculate the canopy porosity under wide-angle photography image list angle zenith angle θ=57.5 ° according to Beer-Lambert theorem;
Wherein T (θ) is the factor of porosity of single angle zenith angle θ lower canopy, P
0(θ) be single angle zenith angle θ lower canopy hole pixel quantity, P
1(θ) be single angle zenith angle θ lower blade pixel quantity;
S8, calculating leaf area index: according to the leaf area index that is related to canopy in computed image of image canopy porosity and leaf area index in Beer-Lambert theorem.
4. the discrete type canopy leaf area index detection method based on wide-angle imaging according to claim 3, is characterized in that: in described step S2, select maximum value process, the method for average or the method for weighting to carry out gradation conversion to colorful digital canopy image.
5. the discrete type canopy leaf area index detection method based on wide-angle imaging according to claim 3, is characterized in that: the each image enchancing method adopting in described step S3 is Gray Histogram linear transformation method, histogram equalization method and limited contrast adaptive histogram equalization method.
6. the discrete type canopy leaf area index detection method based on wide-angle imaging according to claim 3, it is characterized in that: in described step S4, adopt the image after low-pass filter strengthens the canopy obtaining in step S3 to carry out medium filtering processing: the pixel in low-pass filter window, by gray level sequence, is got to the gray-scale value of its intermediate value as parent window center pixel.
7. the discrete type canopy leaf area index detection method based on wide-angle imaging according to claim 3, it is characterized in that, in described step S5, by selected first threshold, image is divided into and comprises blade and two kinds of pixels of background, its Leaf pixel is shown as white, and background pixel unit is shown as black.
8. the discrete type canopy leaf area index detection method based on wide-angle imaging according to claim 3, is characterized in that, in described step S5, determines first threshold when image binaryzation by maximum between-cluster variance adaptive approach and/or manual method.
9. the discrete type canopy leaf area index detection method based on wide-angle imaging according to claim 3, is characterized in that, adopts maximum between-cluster variance adaptive approach and/or manual method to determine Second Threshold in described step S6; In the time adopting maximum between-cluster variance adaptive approach and manual method to determine Second Threshold simultaneously, if the definite Second Threshold of maximum between-cluster variance adaptive approach carries out after porosity cuts apart image, in the time that the error of the porosity of extraction and the porosity of canopy reality exceedes certain value, adopt manual method to reselect Second Threshold porosity is extracted; The canopy hole pixel wherein extracting in step S6 is white, and other pixels are black.
10. the discrete type canopy leaf area index detection method based on wide-angle imaging according to claim 3, is characterized in that, in described step S8, leaf area index LAI is:
Wherein G (θ) is averaging projection's function, i.e. the projected area of unit blade in θ direction, and it is relevant with the Leaf angle inclination distribution of canopy intra vane, and wherein, in the time of θ=57.5 °, G (θ) is 0.5.
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| CN108460798A (en) * | 2017-12-29 | 2018-08-28 | 江苏金恒信息科技股份有限公司 | A kind of sample notch localization method and device |
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