CN102360428B - Automatic detection methods for trefoil stage and seven-leaf stage of corn - Google Patents
Automatic detection methods for trefoil stage and seven-leaf stage of corn Download PDFInfo
- Publication number
- CN102360428B CN102360428B CN 201110162626 CN201110162626A CN102360428B CN 102360428 B CN102360428 B CN 102360428B CN 201110162626 CN201110162626 CN 201110162626 CN 201110162626 A CN201110162626 A CN 201110162626A CN 102360428 B CN102360428 B CN 102360428B
- Authority
- CN
- China
- Prior art keywords
- corn
- image
- leaf
- tri
- observation area
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 235000002017 Zea mays subsp mays Nutrition 0.000 title claims abstract description 109
- 240000008042 Zea mays Species 0.000 title claims abstract description 106
- 235000005824 Zea mays ssp. parviglumis Nutrition 0.000 title claims abstract description 90
- 235000005822 corn Nutrition 0.000 title claims abstract description 90
- 238000001514 detection method Methods 0.000 title abstract description 7
- 235000004035 Cryptotaenia japonica Nutrition 0.000 title abstract 4
- 102000007641 Trefoil Factors Human genes 0.000 title abstract 4
- 235000015724 Trifolium pratense Nutrition 0.000 title abstract 4
- 238000000034 method Methods 0.000 claims abstract description 57
- 238000009331 sowing Methods 0.000 claims abstract description 6
- 238000004458 analytical method Methods 0.000 claims abstract description 3
- 235000016383 Zea mays subsp huehuetenangensis Nutrition 0.000 claims description 15
- 235000009973 maize Nutrition 0.000 claims description 15
- 238000012360 testing method Methods 0.000 claims description 13
- 238000010899 nucleation Methods 0.000 claims description 8
- 241001057636 Dracaena deremensis Species 0.000 claims description 7
- 239000000284 extract Substances 0.000 claims description 6
- 238000013507 mapping Methods 0.000 claims description 6
- 230000012010 growth Effects 0.000 abstract description 6
- 230000011218 segmentation Effects 0.000 abstract description 5
- 230000000694 effects Effects 0.000 abstract description 4
- 238000009313 farming Methods 0.000 abstract description 3
- 208000003643 Callosities Diseases 0.000 abstract description 2
- 206010020649 Hyperkeratosis Diseases 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 10
- 238000012549 training Methods 0.000 description 6
- 238000000605 extraction Methods 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- 241000482268 Zea mays subsp. mays Species 0.000 description 4
- 239000011159 matrix material Substances 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 241000196324 Embryophyta Species 0.000 description 2
- 241000209140 Triticum Species 0.000 description 2
- 235000021307 Triticum Nutrition 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 2
- 230000015572 biosynthetic process Effects 0.000 description 2
- 230000034303 cell budding Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 239000004744 fabric Substances 0.000 description 2
- 238000003909 pattern recognition Methods 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000003698 anagen phase Effects 0.000 description 1
- 235000013339 cereals Nutrition 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000005553 drilling Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 244000037666 field crops Species 0.000 description 1
- 238000003709 image segmentation Methods 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 230000008635 plant growth Effects 0.000 description 1
- 238000004064 recycling Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
Images
Landscapes
- Image Analysis (AREA)
Abstract
The invention provides automatic detection methods for a trefoil stage and a seven-leaf stage of a corn. According to the methods, segmentation is automatically carried out on a collected downward viewing image of a corn in the field and an image feature is extracted; and it is determined whether the corn in the image area enters a trefoil stage by utilizing the extracted image feature; and furthermore, on the basis of utilization of a feature of an initial image at the trefoil stage, it is automatically determined whether the corn enters a seven-leaf stage with regard to different sowing modes. According to the invention, an image feature parameter that characterizes the number of leaves of a corn is utilized as a determination basis; real-time determination is carried out on a growth period of the corn; and the detection result has high accuracy; therefore, the methods provided in the invention have an important guiding significance for an analysis of a relationship between a corn developmental phase and a meteorological condition, identification on an agricultural meteorological condition for corn growth as well as for farming activities on corns.
Description
Technical field
The invention belongs to Digital Image Processing and agrometeorological observation crossing domain, be specifically related to the automatic testing method of a kind of corn tri-leaf period and seven leaf phases, be object with view under the field corn of taking as sequence namely, utilize characteristics of image to detect the method whether corn arrives tri-leaf period and seven leaf phases.
Background technology
Corn is one of main cereal crops of China, and cultivated area is very extensive.In order to improve maize yield and quality, need understand its rate of development and process, and analyze relation between its each puberty and the meteorological condition, thereby identify the agrometeorological conditions of corn growth.Yet each budding observation mainly is the mode by artificial observation for corn, is observed the influence of personnel's subjective factor bigger for a long time; Because the corn planting region is wide, observation cycle is long, utilize manpower to observe also economical inadequately simultaneously.Therefore, by looking sequence image under the corn of taking, the means of handling by image are observed its puberty just seeming very necessary automatically.Corn tri-leaf period and seven leaf phases are two important puberty links of corn nourishment growth phase, effectively and accurate recognition these two periods, be two important contents of agrometeorological observation, the present invention utilizes to look sequence image under the corn two puberties are identified automatically.
Liu Hong in 2007 sees etc. and to utilize image processing techniques that the extractive technique of corn plant type skeleton is studied in the paper " image processing techniques is being extracted the application on the corn image framework " that " agriculture network information " is delivered, but the method for this paper only is fit to the individual plant corn under the single background, and also inapplicable to the field condition of complexity; The Li Rong spring in 2010 etc. are estimated population leaf area index (LAI) and dry-matter accumulation (DMA) value of summer corn by the method for extracting the ground coverage in the paper " based on the summer corn colony growing way study on monitoring of image processing techniques " that " corn science " delivered, set up the regression relation model of coverage and leaf area index (LAI) and dry-matter accumulation (DMA), thereby finish the estimation to summer corn colony growing way, but only utilize this characteristics of image of coverage not observe exactly the puberty of corn; Ma Yanping proposed the measuring method based on the field corn growth parameter(s) of binocular stereo vision in Master's thesis " based on winter wheat, the technical research of summer corn growing way remote dynamic monitoring of digital picture " in 2010, some growth parameter(s)s to winter wheat and summer corn are measured, but the hardware cost of this method is higher, and because the actual serious shielding of field crop, environmental impact factor is more, the resolution of image is also limited, therefore be difficult in actual applications that field corn is carried out plant type and carry out three-dimensional reconstruction, so this method is infeasible in budding observation.In sum, although at present aspect the plant growth monitoring existing many correlation unit technology occur, all because of certain limitation, the crop puberty that is difficult to apply it to actual land for growing field crops environment automatically observation come up.
Summary of the invention
The object of the invention is to provide the automatic testing method of a kind of corn tri-leaf period and seven leaf phases, and looking like with view under the corn of actual farmland collection is object, utilizes the maize seedling image characteristic point that obtains, and detects the time that corn arrives tri-leaf period and seven leaf phases exactly.
The automatic testing method in a kind of corn tri-leaf period is specially:
(1) corn map to be measured after emerging is looked like to cut apart extracts n maize seedling connected region, the end of adding up each connected domain EndNum that counts
j, j=1...n;
(2) count StemNum according to corn plants number and the corn plants that terminal mapping relations of counting obtain each connected domain
j, described corn plants number and terminal mapping relations of counting are to utilize the historical sample graphical analysis statistics in corn tri-leaf period to obtain;
(3) the average end that calculates each connected domain is counted
(4) the average end that calculates corn map picture to be measured is counted
(5) if MeanEndNum 〉=tri-leaf period judgment threshold, show that then corn has arrived tri-leaf period.
Further, described tri-leaf period judgment threshold span be [2,3].
The automatic testing method of a kind of seven leaf phases of corn, automatic testing method according to aforesaid corn tri-leaf period determines that corn enters tri-leaf period, utilize corn image in the tri-leaf period seven leaf phases of carrying out in the following manner to detect: seeding method is selected option A during for bunch planting for use, and seeding method is selected option b sowing time for use for bar;
Option A:
(A1) determine unique point sum and the seven leaf phase judgment thresholds of emphasis observation area, tri-leaf period image;
Described emphasis observation area is the barycenter M with n maize seedling connected region of corn image in tri-leaf period
1..., M
nCentered by, R is n square area of the length of side;
The unique point of described image adds up in the image feature of each connected region sum of counting, and the feature of described connected region is counted and counted and the trident sum of counting for the end of connected region;
Described seven leaf phase judgment thresholds comprise seven leaf phases arrival final singling threshold value T afterwards
1Arrive final singling threshold value T before with seven leaf phases
2, wherein
EndJunctionNum
jThe feature that is j connected region is counted, k ∈ [2,3]; T
2=k*M*N, M are the cave number in the image in tri-leaf period, N be tri-leaf period the average characteristics of image count
(A2) the unique point sum B of statistics image to be detected, and judge and corn final singling whether in the image to be detected if there is not final singling, then enter step (A3), otherwise, step (A4) entered;
(A3) if B＞T
1, show that then the corn in the image to be detected arrived for seven leaf phases, finish;
(A4) if B＞T
2, show that then the corn in the image to be detected arrived for seven leaf phases, finish;
Described final singling is carried out in the following manner: the note corn is respectively P at i-1 and i image constantly
I-1And P
i, calculate
J=1...n,
Be P
I-1In j emphasis observation area in the maize leaves elemental area,
Be P
iIn j emphasis observation area in the maize leaves elemental area, statistics Δ H
j＞T
3Emphasis observation area number J, if
Then show P
iIn corn final singling, T
3∈ [0.3,0.6], T
4∈ [0.2,0.4];
Option b:
(B1) according to the mode of step (A1) determine the emphasis observation area and tri-leaf period image the unique point sum
(B2) the sum D that counts of the feature in each emphasis observation area in the statistics image to be detected, distal point in the observation area and the trident sum of counting of attaching most importance to of counting of the feature in the described emphasis observation area;
(B3) judge corn final singling whether in the image to be detected, if there is not final singling, then enter step (B4), otherwise, step (B5) entered;
(B4) if D＞k*C shows that then the corn in the image to be detected arrived for seven leaf phases, finish;
(B5) Δ H is satisfied in rejecting
j＞T
5The emphasis observation area, the feature of emphasis observation area connected domain of correspondence in corn image in tri-leaf period of sum E and the reservation sum F that counts that counts of the feature in the emphasis observation area that statistics keeps;
(B6) if E＞k*F shows that then the corn in the image to be detected arrived for seven leaf phases, finish.
Further, described cave number is determined in the following manner: the barycenter M that obtains each connected domain of image
1..., M
n, calculate centroid distance dis
I, j, i, if j=1...n is dis
I, jLess than threshold alpha, then reject barycenter M
iWith M
j, and with M
iWith M
jBetween mid point be designated as barycenter, recomputate centroid distance, again with itself and threshold alpha relatively, so circulation, till all centroid distances were all greater than α, the final barycenter number that keeps was the cave number.
Technique effect of the present invention is embodied in: the present invention looks like to cut apart to view under the field corn of gathering automatically, and characteristics of image extracted automatically, utilize the characteristics of image that extracts, judge whether the corn in this image-region enters tri-leaf period, and then utilize the feature of initial pictures in tri-leaf period, at different seeding methods whether corn was entered for seven leaf phases and judge automatically.This method with the characteristics of image parameter that characterizes the maize leaf number as basis for estimation, in real time corn growing season is judged, testing result accuracy rate height, to analyzing the relation between corn puberty and the meteorological condition, identify the agrometeorological conditions of corn growth and the farming activities of corn is all had important directive significance.
Description of drawings
Fig. 1 is overall flow figure of the present invention;
Fig. 2 is the corn automatic training stage process flow diagram that detects in tri-leaf period;
Fig. 3 is corn history image in tri-leaf period exemplary plot;
Fig. 4 is cut apart figure as a result after the extraction to Fig. 3;
Fig. 5 is the skeleton diagram after Fig. 4 handles through morphology;
Fig. 6 carries out figure as a result after the skeleton distal point extracts to Fig. 5;
Fig. 7 is corn strain in tri-leaf period number and the end function curve fitted figure of counting;
Fig. 8 is the corn automatic detection-phase process flow diagram that detects in tri-leaf period;
Automatic testing process figure of seven leaf phases during Fig. 9 bunch planting;
Process flow diagram is determined in the emphasis observation area during Figure 10 bunch planting;
Emphasis observation area synoptic diagram during Figure 11 bunch planting;
During Figure 12 bunch planting tri-leaf period initial pictures unique point mark synoptic diagram;
Figure 13 cave number is determined process flow diagram;
During Figure 14 bunch planting tri-leaf period image average characteristics definite process flow diagram of counting;
Final singling decision flow chart during Figure 15 bunch planting;
Automatic testing process figure of Figure 16 bar seven leaf phases of sowing time;
Final singling sowing time of Figure 17 bar is judged and is upgraded emphasis area of observation coverage process flow diagram.
Embodiment
The invention provides the automatic testing method of a kind of corn tri-leaf period and seven leaf phases, looking like with view under the corn of actual farmland collection is object, utilizes the image characteristic point that extracts, and detects the time that corn arrives tri-leaf period and seven leaf phases exactly.Describe the specific embodiment of the present invention and implementation step in detail below in conjunction with accompanying drawing.
Fig. 1 is overall flow figure of the present invention, be divided into two parts, first part is by carrying out feature extraction to the image after emerging, counting with the average end of image is basis for estimation, automatically identify the time of taking place tri-leaf period, and then enter second part, namely by the characteristics of image that will begin tri-leaf period as important indicator, successively the image after tri-leaf period is carried out feature extraction, identify seven leaf phase time of origins.Therefore, the beginning image in tri-leaf period is the necessary condition of successfully identifying for seven leaf phases among the present invention.
Introduce the detection method of tri-leaf period and seven leaf phases below respectively:
1. the detection in tri-leaf period is divided into training and detects two stages:
(1) training stage: the fundamental purpose in this stage is by the corn history image data in tri-leaf period, sets up corn strain in tri-leaf period number and terminal funtcional relationship of counting, its flow process as shown in Figure 2, concrete steps are as follows:
A. at first obtain several corns history image in tri-leaf period, as shown in Figure 3, then image is cut apart extraction maize seedling wherein, only contained the bianry image of seedling, as shown in Figure 4; Dividing method can adopt environment self-adaption dividing method (Lei F.Tian.Environmentally adaptive segmentation algorithm for outdoor image segmentation.Computers and electronics in agriculture, 1998,21:153～168), super green operator dividing method (D.M.Woebbecke, G.E.Meyer, K.Von Bargen, D.A.Mortensen.Color Indices for weed identification under various soil, residue, and lighting conditions.Transactions of the ASAE, 1995,38 (1): 259～269), crop image partition method (Zheng L based on Mean Shift, Zhang J, Wang Q.Mean-shift-based color segmentation of images containing green vegetation.Computers and Electronics in Agriculture, 2009,65:93-98) etc.
B. the bianry image that step a is obtained carries out the morphology processing, and the connected domain in the image is carried out refinement, obtains its skeleton, as shown in Figure 5.Again by Cecilia Di Ruberto, Recognition of shapes by attributed skeletal graphs, Pattern Recognition 37,21-31, method in 2004, distal point (end points) quantity of extracting and adding up each connected domain skeleton, as shown in Figure 6, blockage represents distal point among the figure.Suppose that certain width of cloth image has m connected domain, the connected domain end that statistics wherein is numbered i is counted and is EndNum
i, i=1...m;
C. StemNum is counted in its corresponding strain of each connected domain complicate statistics among the step b
i, i=1...m, (x, y), namely when StemNum is x (x＞0), the end EndNum that counts is y={EndNum thereby can obtain strain number and terminal mapping of counting
i| StemNum
i=x, i=1...m}.
A. last, utilize conic fitting to go out that x is counted in strain and terminal Function Mapping of counting between the y concerns y=F (x), present embodiment fitting function relational expression is: y=1.5891x
2-3.773x+5.0172; As shown in Figure 7.
(2) detection-phase: the funtcional relationship by training obtains, the corn map after emerging is looked like to detect automatically, its flow process as shown in Figure 8, concrete steps are as follows:
A. at first, the corn map to be detected after emerging is looked like to carry out self-adaptation cut apart the extraction maize seedling, and the end of adding up each connected domain is counted the same training stage of method.Suppose that image has n connected domain, through statistics, the end that can obtain all connected domains EndNum that counts
j, j=1...n;
B. the end of again step a the being added up EndNum that counts
j, j=1...n is updated to functional relation y=F (x)=ax that the training stage match obtains respectively
2Among+the bx+c, pass through formula:
C. follow the end of each connected domain that step a the is obtained EndNum that counts
j, StemNum is counted in each connected domain strain that j=1...n and step b calculate
j, the j=1...n correspondence is divided by, and can obtain the average end of each connected domain and count, namely
j＝1...n；
D. the average end of step c being obtained of all connected domains summation of counting, again divided by the connected domain number, calculating formula is:
The average end that can the obtain entire image MeanEndNum that counts;
E. last, the MeanEndNum if the average end of entire image obtained of steps d is counted＞tri-leaf period judgment threshold represents that then this image arrives tri-leaf period, and the shooting time of this image is designated as time of arrival in tri-leaf period, judges tri-leaf period and finishes; Otherwise expression is no show tri-leaf period also, continues to handle next time chart picture.Tri-leaf period, judgment threshold needed to be determined by experiment according to camera parameter and camera heights, and the value of present embodiment is 2.4, and image resolution ratio is 1824 * 1368 pixels, and camera heights is 5 meters.
The automatic detection of seven leaf phases of corn will be because the difference of seeding method will be divided into two kinds of situations of bunch planting and drilling.Simultaneously, because have the farming activities of final singling before and after seven leaf phases arrived, the seedling number of corn will change this moment, and in order to adapt to this situation, the present invention will identify automatically to the carrying out of final singling, makes the automatic detection algorithm of seven leaf phases have better robustness.To divide two class situations below, introduce automatic concrete technical scheme and the realization flow that detects of seven leaf phases of the present invention respectively:
(1) when the seeding method of corn is bunch planting, detect automatically according to following steps, its flow process as shown in Figure 9:
A. at first determine the emphasis observation area of image, the unique point sum A that obtains the image in tri-leaf period and the judgment threshold of determining for seven leaf phases.
Further, the flow process of described definite emphasis observation area as shown in figure 10, detailed process is: earlier with tri-leaf period image cut apart, and its connected domain is carried out mark, obtain the barycenter M of each connected domain
1..., M
n, more respectively with this barycenter M
i, be that the length of side is made square centered by the i=1...n, with threshold value R, this square region observation area of namely attaching most importance to is used for the leaf elemental area of this zone seedling is observed, and obtains n emphasis observation area the most at last, is designated as Ω
1..., Ω
nWherein, the value of threshold value R can cover seedling length with this square region and be as the criterion to seven leaf phases, considers that simultaneously this value is also relevant with camera heights and picture size, therefore needs to set by experiment.Each parameter of present embodiment is: camera heights is that 5 meters, image size are that 309 * 609 pixels, R get 80 pixels, be area of observation coverage synoptic diagram as shown in figure 11, certain strain maize seedling with the intercepting regional area is example, and square-shaped frame is area of observation coverage scope, and round dot is represented the barycenter in this connection district;
Further, described tri-leaf period image the obtaining of unique point sum A, detailed process is: the segmentation result bianry image that will obtain in the time of will determining the emphasis observation area carries out morphology to be handled, and the connected domain in the image is carried out refinement, obtains its skeleton.Again by Cecilia Di Ruberto, Recognition of shapes by attributed skeletal graphs, Pattern Recognition 37,21-31, method in 2004, extract and add up the distal point (end points) and triradius (junction points) sum of each connected domain skeleton, as shown in figure 12, blockage represents all unique points among the figure.Suppose that certain width of cloth image has n connected domain, the connected domain feature that statistics wherein is numbered i is counted and is EndJunctionNum
i, i=1...n, then the unique point of this image adds up to
Further, determining of described seven leaf phase judgment thresholds is specially: the difference according to the final singling time is divided into seven leaf phases arrival final singling threshold value T afterwards
1Arrive final singling threshold value T before with seven leaf phases
2, wherein seven leaf phases arrive after the final singling threshold value be tri-leaf period image unique point sum A k doubly, namely
And seven leaf phases arrive before the final singling threshold value be k*M*N, wherein M is the cave number in the image in tri-leaf period, N be tri-leaf period the average characteristics of image count k ∈ [2,3].
Determining of M counted in the cave in the described tri-leaf period image, flow process as shown in figure 13, detailed process is: utilize the segmentation result binary map that obtains when determining the emphasis observation area, carry out connected component labeling, suppose to have n connected domain; Obtain the barycenter M of each connected domain then
1..., M
n, pass through range formula:
Calculate barycenter M
i(x
i, y
i) and M
j(x
j, y
j), i, the distance between the j=1...n obtains one apart from symmetry square matrix DIS, wherein the element dis in the square formation
I, jNamely represent barycenter M
i(x
i, y
i) and M
j(x
j, y
j) between distance; Judge the element value dis in the square formation
I, jAnd if the relation between the threshold alpha is less than α, then with two barycenter M of its element correspondence
iWith M
jMerge into a barycenter M
Ij, the coordinate of this barycenter is
Again this barycenter is joined in the barycenter sequence again, recomputate matrix D IS, so circulation, till the matrix element value was all greater than α, Sheng Xia barycenter number was the cave number at last.Cave and the cave distance in image was set when wherein the value of α was sowed according to reality, and present embodiment α value is 50 pixels;
Described tri-leaf period image the determining of average characteristics points N, flow process as shown in figure 14, detailed process is: at first to tri-leaf period image cut apart and the end of adding up each connected domain EndNum that counts
j, j=1...n, the distal point with each connected domain is updated among the fitting function relational expression y=F in tri-leaf period (x) again, obtains the strain of each connected region and counts StemNum
j, j=1...n, above method is with tri-leaf period detection-phase step a and step b; The unique point sum A of recycling image in tri-leaf period counts sum divided by all connected domain strains, and expression formula is
Can obtain the average characteristics points N of image in tri-leaf period.
B. obtain next treating constantly again and observe the crop map picture, and add up the unique point sum B of this image, the total A of unique point that obtains the image in tri-leaf period among statistical method and the step a is identical;
C. judge whether image has carried out final singling; If there is not final singling, then enter steps d; If final singling has taken place, then enter step e;
The judgement of described final singling, flow process as shown in figure 15, detailed process is: at first, to tri-leaf period later i-1 image constantly cut apart, and the leaf elemental area of the crop seedling in the statistics emphasis observation area is designated as
Take off the image of a moment i again, the leaf elemental area of the crop seedling in the same counterweight point observation zone is added up, and is designated as
The leaf elemental area of above two corresponding observation areas of the moment is subtracted each other, again except the area area of carving i-1 above a period of time, namely
J=1...n; Add up the Δ H that satisfies condition
j＞T
3Connected domain number J, T
3∈ [0.3,0.6]; At last, when
The time, T
4∈ [0.2,0.4] then judges i final singling constantly, otherwise i also not final singling constantly.
D. if the unique point of present image sum B arrives final singling threshold value afterwards greater than seven leaf phases, namely
Represent that then this image arrived for seven leaf phases, the shooting time of this image was designated as for seven time of arrival leaf phase, seven leaf phases were judged and finish; Otherwise enter step b;
E. if the unique point of present image sum B arrive greater than seven leaf phases before the final singling threshold value, namely k*M*N represents that then this image arrived for seven leaf phases, and the shooting time of this image was designated as for seven time of arrival leaf phase, seven leaf phases were judged and finish; Otherwise enter step b;
(2) seeding method when corn is bar sowing time, detect automatically according to following steps, its flow process as shown in figure 16:
A. the emphasis observation area in definite image, the unique point sum C that obtains the image in tri-leaf period and the feature of each connected domain are counted;
Further, described definite emphasis observation area Ω
1..., Ω
n, obtain the unique point sum of image in tri-leaf period
And the feature of each connected domain EndJunctionNum that counts
i, i=1...n is identical with method among the bunch planting way flow process a;
B. obtain next treating constantly and observe the crop map picture, and the sum D that counts of the feature in the statistical picture emphasis observation area;
Further, the sum of counting of the feature in the described image emphasis observation area refers to obtain the unique point EndJunction of each connected domain in the current entire image
i(x
i, y
i), after the i=1...k, statistics belongs to image emphasis observation area Ω again
1..., Ω
nIn the feature sum of counting, namely statistical nature point is numbered the number of i, wherein the i EndJunction that satisfies condition
i(x
i, y
i) ∈ Ω
j, i=1...k, j=1...n.
C. judge whether present image has carried out final singling; If there is not final singling, then enter steps d; If final singling has taken place, then enter step e;
Further, the judgement flow process of described final singling is identical with final singling flow process among the bunch planting step c, as shown in figure 17;
D. if the total characteristic in the present image emphasis observation area is counted D greater than k*C, represent that then this image arrived for seven leaf phases, the shooting time of this image was designated as for seven time of arrival leaf phase, seven leaf phases were judged end; Otherwise enter step b;
E. redefine the emphasis observation area, flow process as shown in figure 17, final singling takes place after, emphasis observation area Ω
1..., Ω
nIn the leaf elemental area of some regional maize seedling die-off, namely satisfy Δ H
j＞T
5, wherein
J=1...n, T
5∈ [0.3,0.6] therefore will satisfy the zone of above condition and reject, and remaining observation area is designated as Ω ' as new emphasis observation area.
F. obtain in the present image the new interior feature of the emphasis observation area Ω ' feature of the connected domain identical with the emphasis area of observation coverage numbering sum F that counts that counts in sum E and the tri-leaf period image;
Further, the statistics of described unique point sum E is to carry out in the emphasis observation area Ω ' after renewal, and concrete steps are identical with step b.
Further, the statistics of unique point sum F, be by to tri-leaf period the image connectivity territory the unique point sum and
Calculate, wherein k ∈ { 1...n} and satisfied { k|k=i, Ω
i∈ Ω ' }.
G. if the unique point of present image sum E greater than k*F, represents that then this image arrived for seven leaf phases, the shooting time of this image was designated as for seven time of arrival leaf phase, seven leaf phases were judged and finish; Otherwise enter step b.
Claims (3)
1. the automatic testing method of seven leaf phases of corn is specially:
Determine that at first in the following manner corn enters tri-leaf period:
(1) corn map to be measured after emerging is looked like to cut apart extracts n maize seedling connected region, the end of adding up each connected domain EndNum that counts
j, j=1...n;
(2) count StemNum according to corn plants number and the corn plants that terminal mapping relations of counting obtain each connected domain
j, j=1...n, described corn plants number and terminal mapping relations of counting are to utilize the historical sample graphical analysis statistics in corn tri-leaf period to obtain;
(3) the average end that calculates each connected domain is counted
(4) the average end that calculates corn map picture to be measured is counted
(5) if MeanEndNum 〉=tri-leaf period judgment threshold, show that then corn has arrived tri-leaf period;
Utilize corn image in the tri-leaf period seven leaf phases of carrying out in the following manner to detect then: seeding method is selected option A during for bunch planting for use, and seeding method is selected option b sowing time for use for bar;
Option A:
(A1) determine unique point sum and the seven leaf phase judgment thresholds of emphasis observation area, tri-leaf period image;
Described emphasis observation area is the barycenter M with n maize seedling connected region of corn image in tri-leaf period
1..., M
nCentered by, R is n square area of the length of side;
The unique point of described image adds up in the image feature of each connected region sum of counting, and the feature of described connected region is counted and counted and the trident sum of counting for the end of connected region;
Described seven leaf phase judgment thresholds comprise seven leaf phases arrival final singling threshold value T afterwards
1Arrive final singling threshold value T before with seven leaf phases
2, wherein
EndJunctionNum
jThe feature that is j connected region is counted, k ∈ [2,3]; T
2=k*M*N, M are the cave number in the image in tri-leaf period, N be tri-leaf period the average characteristics of image count
(A2) the unique point sum B of statistics image to be detected, and judge and corn final singling whether in the image to be detected if there is not final singling, then enter step (A3), otherwise, step (A4) entered;
(A3) if B＞T
1, show that then the corn in the image to be detected arrived for seven leaf phases, finish;
(A4) if B＞T
2, show that then the corn in the image to be detected arrived for seven leaf phases, finish;
Described final singling is carried out in the following manner: the note corn is respectively P at i-1 and i image constantly
I-1And P
i, calculate
Be P
I-1In j emphasis observation area in the maize leaves elemental area,
Be P
iIn j emphasis observation area in the maize leaves elemental area, statistics Δ H
j＞T
3Emphasis observation area number J, if
Then show P
iIn corn final singling, T
3∈ [0.3,0.6], T
4∈ [0.2,0.4];
Option b:
(B1) according to the mode of step (A1) determine the emphasis observation area and tri-leaf period image the unique point sum
(B2) the sum D that counts of the feature in each emphasis observation area in the statistics image to be detected, distal point in the observation area and the trident sum of counting of attaching most importance to of counting of the feature in the described emphasis observation area;
(B3) judge corn final singling whether in the image to be detected, if there is not final singling, then enter step (B4), otherwise, step (B5) entered;
(B4) if D＞k*C shows that then the corn in the image to be detected arrived for seven leaf phases, finish;
(B5) Δ H is satisfied in rejecting
j＞T
5The emphasis observation area, the feature of emphasis observation area connected domain of correspondence in corn image in tri-leaf period of sum E and the reservation sum F that counts that counts of the feature in the emphasis observation area that statistics keeps, T
5∈ [0.3,0.6];
(B6) if E＞k*F shows that then the corn in the image to be detected arrived for seven leaf phases, finish.
2. automatic testing method according to claim 1 is characterized in that, described cave number is determined in the following manner: the barycenter M that obtains each connected domain of image
1..., M
n, calculate centroid distance dis
I, j, i, if j=1...n is dis
I, jLess than threshold alpha, then reject barycenter M
iWith M
j, and with M
iWith M
jBetween mid point be designated as barycenter, recomputate centroid distance, again with itself and threshold alpha relatively, so circulation, till all centroid distances were all greater than α, the final barycenter number that keeps was the cave number.
3. automatic testing method according to claim 1 is characterized in that, described tri-leaf period, the span of judgment threshold was [2,3].
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201110162626 CN102360428B (en) | 2011-06-16 | 2011-06-16 | Automatic detection methods for trefoil stage and seven-leaf stage of corn |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201110162626 CN102360428B (en) | 2011-06-16 | 2011-06-16 | Automatic detection methods for trefoil stage and seven-leaf stage of corn |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102360428A CN102360428A (en) | 2012-02-22 |
| CN102360428B true CN102360428B (en) | 2013-09-18 |
Family
ID=45585755
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN 201110162626 Expired - Fee Related CN102360428B (en) | 2011-06-16 | 2011-06-16 | Automatic detection methods for trefoil stage and seven-leaf stage of corn |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102360428B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10186029B2 (en) | 2014-09-26 | 2019-01-22 | Wisconsin Alumni Research Foundation | Object characterization |
| CN108647652B (en) * | 2018-05-14 | 2022-07-01 | 北京工业大学 | Cotton development period automatic identification method based on image classification and target detection |
| CN109345539B (en) * | 2018-10-08 | 2022-03-01 | 浙江农林大学 | Self-adaptive Mean-Shift standing tree image segmentation method based on image abstraction |
| CN111860038B (en) * | 2019-04-25 | 2023-10-20 | 河南中原光电测控技术有限公司 | Crop front end recognition device and method |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101980249A (en) * | 2010-11-12 | 2011-02-23 | 中国气象局气象探测中心 | Automatic observation method and device for crop development and growth |
| CN102022984A (en) * | 2009-09-15 | 2011-04-20 | 南开大学 | Image-technique-based artificial wetland plant growth information extracting method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006320240A (en) * | 2005-05-18 | 2006-11-30 | Satake Corp | Method for measuring crop information by remote sensing |
-
2011
- 2011-06-16 CN CN 201110162626 patent/CN102360428B/en not_active Expired - Fee Related
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102022984A (en) * | 2009-09-15 | 2011-04-20 | 南开大学 | Image-technique-based artificial wetland plant growth information extracting method |
| CN101980249A (en) * | 2010-11-12 | 2011-02-23 | 中国气象局气象探测中心 | Automatic observation method and device for crop development and growth |
Non-Patent Citations (1)
| Title |
|---|
| JP特开2006-320240A 2006.11.30 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102360428A (en) | 2012-02-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Yu et al. | Automatic image-based detection technology for two critical growth stages of maize: Emergence and three-leaf stage | |
| CN106971167B (en) | Crop growth analysis method and system based on unmanned aerial vehicle platform | |
| CN105740759B (en) | Semilate rice information decision tree classification approach based on feature extraction in multi-temporal data | |
| CN106355143A (en) | Seed maize field identification method and system based on multi-source and multi-temporal high resolution remote sensing data | |
| Zhong et al. | Phenology-based crop classification algorithm and its implications on agricultural water use assessments in California’s Central Valley | |
| CN102636486B (en) | Method for automatically detecting three-true-leaf period of cotton | |
| CN109948596A (en) | A method of rice identification and crop coverage measurement are carried out based on vegetation index model | |
| CN101858971A (en) | Rice yield remote sensing estimation method based on MODIS data | |
| CN106951836A (en) | Crop cover degree extracting method based on priori threshold optimization convolutional neural networks | |
| CN113221806B (en) | Cloud platform fusion multi-source satellite image and tea tree phenological period based automatic tea garden identification method | |
| CN110110595A (en) | A kind of farmland portrait and medicine hypertrophy data analysing method based on satellite remote-sensing image | |
| CN102592118B (en) | Automatic detection method for time emergence of seedling of corns | |
| CN104949981A (en) | Automatic detection method and system for cotton five-euphylla period | |
| CN102855485B (en) | The automatic testing method of one grow wheat heading | |
| CN102360428B (en) | Automatic detection methods for trefoil stage and seven-leaf stage of corn | |
| CN114419431A (en) | Locust plague potential high risk area identification method, device, equipment and storage medium | |
| Kotsuki et al. | SACRA–a method for the estimation of global high-resolution crop calendars from a satellite-sensed NDVI | |
| CN104951754A (en) | Sophisticated crop classifying method based on combination of object oriented technology and NDVI (normalized difference vegetation index) time series | |
| Han et al. | Rice yield estimation using a CNN-based image-driven data assimilation framework | |
| CN102663396A (en) | Method for automatically detecting rice milky ripe stage | |
| CN113377062B (en) | Multifunctional early warning system with disease and pest damage and drought monitoring functions | |
| CN108537164B (en) | Method and device for monitoring germination rate of dibbling and sowing based on unmanned aerial vehicle remote sensing | |
| CN102622345B (en) | High-precision land-utilization remote sensing updating technology with synergistic multisource spatio-temporal data | |
| CN116843164A (en) | Agricultural machinery intelligent control system based on image analysis | |
| CN105550707A (en) | Classified extraction method of tobacco field remote sensing data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20130918 |