CN105371762A - Image analysis-based fruit tree crown volume measurement method - Google Patents
Image analysis-based fruit tree crown volume measurement method Download PDFInfo
- Publication number
- CN105371762A CN105371762A CN201510812159.8A CN201510812159A CN105371762A CN 105371762 A CN105371762 A CN 105371762A CN 201510812159 A CN201510812159 A CN 201510812159A CN 105371762 A CN105371762 A CN 105371762A
- Authority
- CN
- China
- Prior art keywords
- tree crown
- fruit
- volume
- tree
- image
- 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.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/28—Measuring arrangements characterised by the use of optical techniques for measuring areas
- G01B11/285—Measuring arrangements characterised by the use of optical techniques for measuring areas using photoelectric detection means
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention discloses an image analysis-based fruit tree crown volume measurement method. The method includes the following steps that: the images of five fruit trees are acquired, and tree crown volume is measured manually; tree crown area is obtained by using an image processing technology; based on the least squares method, a relationship model between the logarithm LnV of the tree crown volume and area S can be obtained through fitting; and when measurement is carried out, only the fruit tree images are required to be acquired, and the tree crown area can be obtained through image processing, and the tree crown volume can be obtained according to the relationship model. With the image analysis-based fruit tree crown volume measurement method of the invention adopted, the defects of low efficiency and high strength of manual measurement and the defects of complex operation and large data processing amount of an existing electronic measurement method can be eliminated; the relationship model can be calibrated at any time with the changes of seasons and environments, so that measurement accuracy can be differentiated. The image analysis-based fruit tree crown volume measurement method is simple in operation steps and is suitable for being used by ordinary farmers and has a bright market prospect.
Description
Technical field
The invention belongs to planting fruit trees field, particularly a kind of top fruit sprayer volume measuring method based on graphical analysis.
Background technology
Tree crown is the key factor affecting production of fruit trees ability, its upgrowth situation directly affects the seed output and quality of fruit, therefore the volume of tree crown evaluates tree and surveys the important indicator of producing, at fruit breeding, survey and produce and in tree crown precision management, there is extremely important reference value, as the yield by estimation of: fruit tree, agricultural chemicals variable spray, the calculating of precision fertilizing, fruit tree biomass, leaf density calculation etc., can say, measure the prerequisite that top fruit sprayer volume is fruit tree healthy growth, reasonable management.
At present, the common method of fruit tree cubing comprises traditional hand dipping method and the method for automatic measurement of sensor.Wherein, traditional-handwork measuring method is simple, be easy to operate, require low to the know-how of grower, but wastes time and energy, and measuring accuracy is subject to the impact of gauger's subjective factor, and in sensor is measured automatically, main with ultrasonic sensor (" based on hyperacoustic fruit tree canopy three-dimensionalreconstruction and cubing [J] " Yu Long etc., Transactions of the Chinese Society of Agricultural Engineering, 2010, 26 (11): 204-208), three-dimensional laser passes scanner (" the Tree Crown Volume computing method [J] based on 3 D laser scanning point cloud " Wei Xuehua etc., agricultural mechanical journal, 2013, 44 (7), 235-240.) and reflectorless total station (" the Tree Crown Volume measuring method [J] based on cube grid method " He Cheng etc., agricultural mechanical journal 2014, 45 (12), 308-313) the most extensive, current ultrasonic sensor is widely used in grape, the top fruit sprayer cubings such as oranges and tangerines, but there is diffraction phenomena in the air in ultrasound wave, the angle of divergence is larger, and ultrasonic intensity can weaken with the ratio that is changing into of measuring distance, cause measuring accuracy lower, three-dimensional laser technology then can reach higher measuring accuracy, but its cost is higher, and the cloud data amount obtained is huge, and treatment effeciency is lower, reflectorless total station is by obtaining tree crown cloud data, construct tree crown three-dimensional information, then gridding method is utilized to obtain Tree Crown Volume, it is lower that the method measures cost, but need in tree crown east, south, west, north, direction is repeatedly adopted a little every phase co-altitude the southeast, northwest, southwest, 8, northwest, workload is large, not easy to operate.Therefore these measuring methods are made to be difficult to widely use in orchard management.
Image processing techniques is that one utilizes computing machine to analyze image, to reach the technology of results needed, Wang Yongjiao (based on the Measurement Approach of Leaf Area [J] of image procossing. computer engineering, 2006, 32 (8), 210-212.), Zhang Yuanyuan (based on the research and apply [D] of the irregular body projected area measuring method of Digital Image Processing. Changchun, Changchun University of Science and Technology, 2010) etc. propose to utilize the image procossing modes such as OpenCV or Matlab, the method of plant two-dimensional areas is obtained by image, but two-dimensional areas data are being transferred in the process of three-dimensional volume data, although Liao Caixia etc. (research [J] of Pinus Silvestris Var. Mongolica Plantation Crown surface area and volume prediction model. plant research, 2007, 27 (4), 478-483.) multinomial model is proposed, Wang Shuan etc. (research [D] of larch Tree Crown Volume and surface area growth model. Harbin, Northeast Forestry University, 2014.) multiple transformation models such as power function model are proposed, but these models all need to process mass data, length consuming time and can not according to season, the transition of environment carry out real time correction to existing model, the precision that extreme influence is measured.
Therefore, how under the prerequisite ensureing general agricultural operation precision, provide a kind of easy, efficient Tree Crown Volume measuring method, be planting fruit trees field technical barrier urgently to be resolved hurrily always.
Summary of the invention
For the problems referred to above, the present invention is based on the top fruit sprayer cubing new method of graphical analysis and parameter calibration, the method is easy to operation, be suitable for and common orchard management, and can along with the change of the external factors such as fruit variety, environment, weather, make model and revising quickly, ensure the precision measured, the present invention is achieved in that
Based on a top fruit sprayer volume measuring method for graphical analysis, concrete steps are as follows:
(A) 5 top fruit sprayer images in same orchard are gathered, during collection, camera lens primary optical axis in the horizontal plane, the image obtained should comprise complete tree crown, tree crown occupy whole collection image 1/2 and more than, image size is that 3456 pixel × 2304 pixels are advisable; Measure the vertical range between camera measurement point and trunk with ultrasonic distance-measuring sensor simultaneously, and adopt the Tree Crown Volume V that spheroid shape method manual measurement fruit tree is corresponding;
Be reduce sunlight to the impact of picture quality when gathering image, preferably select the sunlight more weak time period to carry out operation, select point-9 point in the morning 7 at fine day, afternoon 4, point-6 was Best Times section, and illuminance is at 2-5 ten thousand Lux; Illuminance, at 20,000 below Lux, is equivalent to cloudy environment, now under camera flash-light pattern, completes sampling.
(B) image processing software (as OpenCV2.4.10 and QT5.3.2 etc.) is utilized respectively, filtering, Iamge Segmentation, gray processing, binaryzation, the process of morphology opening operation are carried out to figure, obtains the tree crown vertical plane area S that 5 fruit trees are corresponding;
Concrete steps are: (1) carries out gaussian filtering process to the image that step (A) collection obtains, and choosing Gaussian kernel size is Size (5,5), remove picture noise, extract tree crown feature;
(2) Resize () process rear (after equal proportion convergent-divergent) is carried out to image, with watershed algorithm, Iamge Segmentation is become super-pixel, with the node structure graph model of the super-pixel obtained as figure; With GrabCut algorithm, prospect and background segment are carried out to gained gradient image again, extract prospect tree crown;
(3) gray processing process is carried out to institute's segmenting foreground image, then image binaryzation, adopt large law, automatic acquisition optimal segmenting threshold completes the segmentation of tree crown image-region and background area;
(4) morphological image process, the morphology opening operation of the post-etching that first expands, kernel structure is chosen as oval MORPH_ELLIPSE, and structural element is chosen as size (2,3), is removed by the reasonable fraction being less than structural element;
(5) now gained image intensity value only has 0 and 1, and target image gray-scale value is 0 is black, and non-targeted thing gray-scale value is 1 is white; Be the number of pixels addition of 0 by gray-scale value, finally obtain the pixel total amount that target tree crown comprises;
(6) select 200 × 150 black cardboards as object, demarcate, carry out first time at distance cardboard 600mm place to take pictures, then retreating 100mm successively takes pictures once, serial sampling 37, all amount of pixels of cardboard under the process of OpenCV image software obtains each sampled distance, thus real area when calculating this distance representated by pixel, data are imported in form, based on least square method, complete model construction; Vertical range between the camera measurement point obtain step (A) and trunk substitutes into this model, namely obtains tree crown real area;
(7) based on least square method, the tree crown real area that the Tree Crown Volume obtained by step (A) and step (6) obtain, sets up the Discussion of Linear Model of Tree Crown Volume logarithm LnV and area S.
(C) gather the top fruit sprayer image to be measured of same breed in this orchard, utilize image image processing software to obtain this top fruit sprayer area S to be measured
1, then the linear relationship model obtained by step (B) obtains the Tree Crown Volume V of this fruit tree
1.
Further, 5 top fruit sprayer images in the same orchard of step of the present invention (A) described collection, refer to: using top fruit sprayer centre hat width diameter D as choice criteria, first fruit tree that in this orchard, D value is minimum is chosen as sample point 1, then increase 20-30% sampling successively with D value, choose the fruit tree sample point of 5 same breed in same orchard.
Further, the Tree Crown Volume V that step of the present invention (A) described manual measurement fruit tree is corresponding, refers to: adopt the artificial volume measuring method of spheroid shape, calculates Tree Crown Volume V according to formula (1) and (2):
In formula (1) and formula (2), E
tfor tree crown hat top terrain clearance, E
ffor tree crown hat end terrain clearance, E
bfor distance tree crown hat top E
aplace's canopy North and South direction vector length, E
cfor distance tree crown hat top E
aplace's canopy east-west direction vector length, V is target top fruit sprayer volume.
In the present invention, area S refers to the planimetric area of tree crown.
The present invention is by utilizing OPenCV2.4.10 and QT5.3.2 as image processing software, process gathering the top fruit sprayer image obtained, analyze the correlationship between Tree Crown Volume V and vertical plane area S, Modling model, effectively transfer three-dimensional tree crown cubing the computing of to two tree crown areas, thus greatly reduce the treatment capacity of data, improve measurement efficiency.Relative to similar modeling method of the prior art, its model is only done Tree Crown Volume and area to determine quantitative analysis, establish some nonlinear models such as polynomial expression or power exponent, the correlationship just simply reflected between Tree Crown Volume and area, and do not consider that in the system of orchard, some external factors are on the impact of model, and these Model Parameters are numerous, cause it can not along with fruit variety, environment, the change of the external factors such as weather, model is made and revises quickly, thus make measuring accuracy poor stability, model universality is low.Applicant, by the experiment measuring to multiple fruit trees such as pear tree, apple tree, peach, persimmon trees, finds to there is obvious linear relationship, coefficient R between top fruit sprayer area S and the logarithm LnV of top fruit sprayer volume
2all more than 0.9, at this based on five point calibration methods, namely five equally distributed fruit tree samples are selected, just can the parameter of Confirming model fast, namely obtain intercept and the slope of model fast, but also can adjust timely the parameter (slope and intercept) in model according to the change of different extraneous factors, thus make model have more universality, measuring accuracy is higher more stable, and operate more simple, practicality is stronger.The present invention is that the exploitation of later stage top fruit sprayer cubing instrument provides theoretical foundation, the automatization level of domestic measurement Tree Crown Volume can be improved further, to the bottleneck problem solving the measurement of domestic Tree Crown Volume, improve the quality of tree crown precision management in orchard, the quality safety level improving fruit has and is important meaning.
Accompanying drawing explanation
Fig. 1 is elliposoidal manual measurement volumetric method schematic diagram;
Fig. 2 is top fruit sprayer image characteristics extraction schematic diagram;
Fig. 3 is top fruit sprayer image processing algorithm process flow diagram;
Fig. 4 is that unit pixel represents correlationship schematic diagram between area and measuring distance;
Fig. 5 is that fruit tree sample tree crown amasss size distribution histogram;
Fig. 6 is correlationship schematic diagram between Tree Crown Volume logarithm and area;
Fig. 7 is 5 peg model schematic diagram;
Fig. 8 is that two kinds of model errors divide spreading point schematic diagram;
Fig. 9 is different model accuracy stability analysis result schematic diagrams.
Embodiment
Below in conjunction with accompanying drawing, technical scheme of the present invention is described further.
Multinomial model described in embodiment refers to " research [D] of larch Tree Crown Volume and surface area growth model. " (Wang Shuan etc., Northeast Forestry University, 2014) model disclosed in a literary composition;
Power function model refers to " research [J] of Pinus Silvestris Var. Mongolica Plantation Crown surface area and volume prediction model " (Liao Caixia etc., plant research, 2007,27 (4), 478-483) model disclosed in a literary composition.
Embodiment 1 gathers top fruit sprayer image, obtains top fruit sprayer area S
Experimental period: 2015.9.207:30-11:40AM (cloudy day, illuminance 20,000 below lux)
Experiment place: pear tree garden, farm, Jiangpu, Nanjing
The first step, to choose in this garden 30 pear trees as sample collection image, its crown projection comprises all forms in this pear tree garden as far as possible, and by these 30 samples, choose 5 pear trees, as the basis of 5 calibration methods, the system of selection of these 5 pear trees is: using top fruit sprayer centre hat width diameter D as choice criteria, first choose fruit tree that in this orchard, D value is minimum as sample point 1, then increase 20-30% successively with D value and sample.
Adopt Canon EOS350D (REBELXT) camera in the present embodiment, under artificial intelligence autofocus mode, carry out image acquisition, its horizontal and vertical resolution is respectively 72dpi, and image size is 3456 pixel × 2304 pixels; From trunk about 2m, offside of taking pictures arranges that (5m*5m) no-reflection white bars flannelette does background (eliminating non-targeted thing); When taking pictures, camera is arranged in cloud and rises on VCT-880 tripod, level meter is utilized to regulate camera, make camera lens primary optical axis in the horizontal plane, gather image and comprise complete tree crown, tree crown with occupy whole collection image 1/2 and above for standard, measure the vertical range (the present embodiment selects LM-040-050-DAC ultrasonic distance-measuring sensor, and measurement range is 0.3-5m, for 12-24V DC voltage) between camera measurement point and trunk with ultrasonic distance-measuring sensor simultaneously.And adopt the artificial volume measuring method of spheroid shape to obtain 30 sample Tree Crown Volume V, be specially:
Tree Crown Volume V is calculated according to formula (1) and (2):
In formula (1) and formula (2), as shown in Figure 1, E
tfor tree crown hat top terrain clearance (unit m), E
ffor tree crown hat end terrain clearance (unit m), E
bfor distance tree crown hat top E
aplace's canopy North and South direction vector length (unit m), E
cfor distance tree crown hat top E
aplace's canopy east-west direction vector length, V is target top fruit sprayer volume (unit) m
3.
Second step, utilize computing machine to process gathering the tree crown image obtained, as shown in Figure 3, concrete operation step is as follows for its process flow diagram:
A. according to method described in the first step, top fruit sprayer image acquisition is completed, as shown in Figure 2 a;
B. carry out gaussian filtering process to image, choosing Gaussian kernel size is Size (5,5), removes picture noise, and extract tree crown feature, result as shown in Figure 2 b;
C. because view data is comparatively large, the operational efficiency of image processing algorithm can be had a strong impact on, therefore C
1. first image is carried out
Than convergent-divergent Resize () process, to reduce processes pixel amount;
C
2. with watershed algorithm, Iamge Segmentation is become the zonule of color similarity in region, i.e. super-pixel, with the node structure graph model of the super-pixel obtained as figure;
C
3. carry out prospect and background segment with GrabCut algorithm to gained gradient image, extract prospect tree crown, as shown in Figure 2 c, for having split rear image, visual target tree crown has been split extraction to result preferably; Applicant utilizes this algorithm to pass through the 30 width pear tree tree crown process gathered on farm, Jiangpu, Nanjing, and compares rear discovery to result, and when iterations is 5, extraction effect is best;
D. pre-service is carried out to institute's segmenting foreground image:
D
1. image gray processing, result is as shown in Figure 2 d;
D
2. image binaryzation, adopt large law (Otsu), automatic acquisition optimal segmenting threshold completes the segmentation of tree crown image-region and background area, and result is as shown in Figure 2 e;
E. morphological image process, the morphology opening operation of the post-etching that first expands, according to the shape of fruit tree leaf, kernel structure is chosen as oval MORPH_ELLIPSE, and structural element size is chosen as size (2,3); By this computing, as shown in figure 2f, the reasonable fraction being less than structural element is removed;
F. now gained image intensity value only has 0 and 1, and target image gray-scale value is 0 is black, and non-targeted thing gray-scale value is 1 is white; Be the number of pixels addition of 0 by gray-scale value, finally obtain the pixel total amount that target tree crown comprises;
G. the real area representated by pixel each under different sampled distance is multiplied by total amount of pixels of object under this sampling condition, can obtain tree crown real area, be specially:
G
1. select 200 × 150 black cardboards as object, demarcate, carry out first time at distance cardboard 600mm place to take pictures, then retreat 100mm successively and take pictures once, serial sampling 37, the all amount of pixels of cardboard under the process of OpenCV image software obtains each sampled distance, thus real area when calculating this distance representated by pixel, data are imported in form, based on least square method, complete model construction, result as shown in Figure 4;
G
2. the vertical range between the camera measurement point obtained during sample collection and trunk is substituted into Fig. 4 model that G1 step obtains, namely obtain tree crown real area;
30 pear tree samples selected by the present embodiment, as shown in Figure 5, it becomes normal distribution to its tree crown area size distribution histogram substantially, illustrates that institute's collecting sample has statistical significance.
Embodiment 2 sets up Tree Crown Volume and area universality Correlation model based on parameter calibration method
The tree crown real area S of 30 samples embodiment 1 obtained and the Tree Crown Volume V of manual measurement imports in form, the relational model between the logarithm LnV of Tree Crown Volume and tree crown area S is built based on least square method, acquired results as shown in Figure 6, obtain relationship equation: LnV=0.6654S-0.3538, R
2=0.9444; Can find that related coefficient is more than 0.94, in obvious positive correlation.
5 samples simultaneously embodiment chosen, set up the relational model between the logarithm LnV of Tree Crown Volume and tree crown area S, acquired results as shown in Figure 7, obtains relationship equation: LnV=0.6349S-0.3826, R
2=0.9866; Comparison diagram 6 and Fig. 7 can intuitively find, in two kinds of method institute established models, parameter value is similar, and stronger based on 5 standardization institute established model correlativitys.
Because different fruit varieties, Different climate, different growth periods all can have an impact to model accuracy and practicality, select 5 equally distributed fruit tree samples, just can the parameter of Confirming model fast, namely intercept and the slope of model is obtained fast, but also rapid adjustment can be carried out according to extraneous factors such as different seeds, growth period, weathers to the parameter in model, thus model is made to have more universality.
Embodiment 3 five parameter calibration method model volume forecasting accuracy assessments
In formula (3), E is measuring error, V
1for Tree Crown Volume model predication value (unit m
3), V
2for Tree Crown Volume manual measurement value (unit m
3);
According to formula (3), with selected 30 pear tree samples for research object, analysis and assessment are done to two kinds of methods (30 samples and 5 samples) institute's established model volume predictions accuracy.In order to verify the validity of 5 parameter calibration methods, Fig. 8 is the application condition of two kinds of forecast models, and in Fig. 8, classical model method refers to 30 strain pear tree institute established models, and 5 peg models refer to 5 sample institute established models.Table 1 is the probability statistics situation that the sample size of two kinds of volume predictions models in different prediction error intervals accounts for total sample:
Table 1 two kinds of model error probability statistics
As can be seen from Fig. 8 and table 1, two kinds of model true errors are substantially within 30%, and the sample size error burst more than 70% is within 20%, can meet agriculture measurement accuracy requirement.Be 13.73% build 14.59% of model a little less than classical approach based on the average error of model constructed by 5 parameter calibration methods, and build more fast succinct, the time of 80% is saved than model constructed by classical approach, show that the introducing of 5 parameter calibration methods makes the universality of model stronger, there is higher using value, while guarantee measuring accuracy, also save a large amount of time and manpower.
With 30 pear tree samples in orchard, Jiangpu, Summer in Nanjing for target sample, with 5 parameter calibration methods, multinomial model, power function model, target sample is affected to the precision stability analysis of drag respectively at different external factor, the precision comparison of other extraneous factors with summer measuring accuracy for benchmark, (spring: 2015.04.11; Summer: 2015.08.20; Autumn: 2015.10.17; Winter: 2014.1.24; After rain: 2015.06.2816:27PM; After wind: 2015.09.148:20AM), result is as shown in table 2 and Fig. 9, and in table 2, each accurate values is the mean value of measurement 30 pear tree sample precision.
The different external factor of table 2 affects the precision stability analysis of drag
From table 2 and Fig. 9, with model constructed by 5 standardizations, along with the change of the ectocine factor, its precision rises and falls little, and amplitude peak is 3%, and precision maintains about 85% substantially.But other two kinds of models, can not adjust model in time according to the impact of extraneous factor, cause precision to rise and fall very large, amplitude peak reaches about 15%, is difficult to the accuracy requirement meeting the farm works such as agricultural plant protection.
These are only the embodiment of a part in technical solution of the present invention; instead of the content of whole technical scheme; based on technical scheme disclosed by the invention; those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.
Claims (3)
1., based on a top fruit sprayer volume measuring method for graphical analysis, it is characterized in that, concrete steps are as follows:
A) gather 5 top fruit sprayer images in same orchard, measure the vertical range between collection point and trunk, and the Tree Crown Volume V that manual measurement fruit tree is corresponding;
B) image processing software is utilized to obtain tree crown area S corresponding to 5 fruit trees respectively; Obtain the linear relationship model between tree crown area S and volume logarithm LnV;
C) gathering top fruit sprayer image to be measured in this orchard, utilize image image processing software to obtain top fruit sprayer area to be measured, then by step B) the linear relationship model that obtains obtains the Tree Crown Volume of this fruit tree.
2. a kind of top fruit sprayer volume measuring method based on graphical analysis according to claim 1, it is characterized in that, steps A) 5 top fruit sprayer images in the same orchard of described collection, refer to: using top fruit sprayer centre hat width diameter D as choice criteria, first fruit tree that in this orchard, D value is minimum is chosen as sample point 1, then increase 20-30% sampling successively with D value, choose the fruit tree sample point of 5 same breed in same orchard.
3. a kind of top fruit sprayer volume measuring method based on graphical analysis according to claim 2, it is characterized in that, steps A) Tree Crown Volume V that described manual measurement fruit tree is corresponding, refer to: adopt the artificial volume measuring method of spheroid shape, calculate Tree Crown Volume V according to formula (1) and (2):
In formula (1) and formula (2),
E
tfor tree crown hat top terrain clearance, E
ffor tree crown hat end terrain clearance, E
bfor distance tree crown hat top E
aplace's canopy North and South direction vector length, E
cfor distance tree crown hat top E
aplace's canopy east-west direction vector length, V is target top fruit sprayer volume.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510812159.8A CN105371762B (en) | 2015-11-20 | 2015-11-20 | A kind of top fruit sprayer volume measuring method based on graphical analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510812159.8A CN105371762B (en) | 2015-11-20 | 2015-11-20 | A kind of top fruit sprayer volume measuring method based on graphical analysis |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105371762A true CN105371762A (en) | 2016-03-02 |
| CN105371762B CN105371762B (en) | 2017-11-07 |
Family
ID=55374174
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510812159.8A Expired - Fee Related CN105371762B (en) | 2015-11-20 | 2015-11-20 | A kind of top fruit sprayer volume measuring method based on graphical analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105371762B (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106969730A (en) * | 2017-05-19 | 2017-07-21 | 中国农业大学 | A kind of top fruit sprayer volume measuring method based on unmanned plane Detection Techniques |
| CN107255446A (en) * | 2017-08-01 | 2017-10-17 | 南京农业大学 | A kind of Cold region apple fruit tree canopy three-dimensional map constructing system and method |
| CN107462151A (en) * | 2016-06-03 | 2017-12-12 | 北京林业大学 | A kind of method based on the digital camera measurement higher volume of timber |
| CN107622228A (en) * | 2017-08-28 | 2018-01-23 | 辽宁远天城市规划有限公司 | A kind of tridimensional green method based on unmanned aerial vehicle remote sensing images |
| CN108680109A (en) * | 2018-05-23 | 2018-10-19 | 山东农业大学 | A kind of wheat root measurement method based on image procossing |
| CN109991911A (en) * | 2019-05-05 | 2019-07-09 | 西安邮电大学 | A kind of orchard comprehensive monitoring system based on Internet of Things |
| CN110579420A (en) * | 2019-09-17 | 2019-12-17 | 北京大学深圳研究生院 | unmanned aerial vehicle-based whole arbor dust retention amount calculation method |
| CN115496796A (en) * | 2022-09-20 | 2022-12-20 | 北京数字绿土科技股份有限公司 | Method and system for measuring and calculating trunk volume through laser point cloud |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101520307A (en) * | 2008-02-26 | 2009-09-02 | 中国计量科学研究院 | Method for measuring tree-crown volume fractal dimension by applying three-dimensional laser image-scanning system |
| US20100041981A1 (en) * | 2007-01-23 | 2010-02-18 | Kassab Ghassan S | Applications of scaling laws of tree structures |
| CN102252612A (en) * | 2011-06-14 | 2011-11-23 | 北京林业大学 | Method for measuring crown volume based on elevation isoline method |
| UA98416C2 (en) * | 2011-04-08 | 2012-05-10 | Александр Петрович Рябоконь | Method for accelerated growing of mine woods or pulp bolts in pine cultures of a fresh pine underbrush |
| CN103206944A (en) * | 2013-02-21 | 2013-07-17 | 北京林业大学 | Method for calculating and measuring volume and area of crown based on three-dimensional laser-point cloud data |
-
2015
- 2015-11-20 CN CN201510812159.8A patent/CN105371762B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100041981A1 (en) * | 2007-01-23 | 2010-02-18 | Kassab Ghassan S | Applications of scaling laws of tree structures |
| CN101520307A (en) * | 2008-02-26 | 2009-09-02 | 中国计量科学研究院 | Method for measuring tree-crown volume fractal dimension by applying three-dimensional laser image-scanning system |
| UA98416C2 (en) * | 2011-04-08 | 2012-05-10 | Александр Петрович Рябоконь | Method for accelerated growing of mine woods or pulp bolts in pine cultures of a fresh pine underbrush |
| CN102252612A (en) * | 2011-06-14 | 2011-11-23 | 北京林业大学 | Method for measuring crown volume based on elevation isoline method |
| CN103206944A (en) * | 2013-02-21 | 2013-07-17 | 北京林业大学 | Method for calculating and measuring volume and area of crown based on three-dimensional laser-point cloud data |
Non-Patent Citations (3)
| Title |
|---|
| D. P. SHARMA ET AL.: "Allometric equations to predict volume of chir pine (Pinus roxburghii Sargent) stands based on crown attributes", 《JOURNAL OF TREE SCIENCES》 * |
| SANQIN ZHAO ET AL.: "A method for estimating spikelet number per panicle: Integrating image analysis and a 5-point calibration model", 《SCIENTIFIC REPORTS》 * |
| 吴明钦等: "长白落叶松树冠体积和表面积模型", 《东北林业大学学报》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107462151A (en) * | 2016-06-03 | 2017-12-12 | 北京林业大学 | A kind of method based on the digital camera measurement higher volume of timber |
| CN106969730A (en) * | 2017-05-19 | 2017-07-21 | 中国农业大学 | A kind of top fruit sprayer volume measuring method based on unmanned plane Detection Techniques |
| CN107255446A (en) * | 2017-08-01 | 2017-10-17 | 南京农业大学 | A kind of Cold region apple fruit tree canopy three-dimensional map constructing system and method |
| CN107255446B (en) * | 2017-08-01 | 2020-01-07 | 南京农业大学 | Dwarfing close-planting fruit tree canopy three-dimensional map construction system and method |
| CN107622228A (en) * | 2017-08-28 | 2018-01-23 | 辽宁远天城市规划有限公司 | A kind of tridimensional green method based on unmanned aerial vehicle remote sensing images |
| CN108680109A (en) * | 2018-05-23 | 2018-10-19 | 山东农业大学 | A kind of wheat root measurement method based on image procossing |
| CN109991911A (en) * | 2019-05-05 | 2019-07-09 | 西安邮电大学 | A kind of orchard comprehensive monitoring system based on Internet of Things |
| CN110579420A (en) * | 2019-09-17 | 2019-12-17 | 北京大学深圳研究生院 | unmanned aerial vehicle-based whole arbor dust retention amount calculation method |
| CN110579420B (en) * | 2019-09-17 | 2022-06-17 | 北京大学深圳研究生院 | Unmanned aerial vehicle-based whole arbor dust retention amount calculation method |
| CN115496796A (en) * | 2022-09-20 | 2022-12-20 | 北京数字绿土科技股份有限公司 | Method and system for measuring and calculating trunk volume through laser point cloud |
| CN115496796B (en) * | 2022-09-20 | 2024-01-16 | 北京数字绿土科技股份有限公司 | Method and system for measuring trunk volume through laser point cloud |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105371762B (en) | 2017-11-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105371762B (en) | A kind of top fruit sprayer volume measuring method based on graphical analysis | |
| CN106908415B (en) | A kind of big region crops time of infertility Soil Moisture Monitoring method based on amendment NDVI time series | |
| CN105004320B (en) | A kind of high score satellite data land table vegetation coverage inversion method and system | |
| Chen et al. | Spatio-temporal prediction of leaf area index of rubber plantation using HJ-1A/1B CCD images and recurrent neural network | |
| CN111091079B (en) | TLS-based method for measuring vegetation advantage single plant structural parameters in friable region | |
| CN109213964A (en) | A kind of satellite AOD product bearing calibration for merging multi-source feature geographic factor | |
| CN103424405B (en) | Drought monitoring method based on HJ-1A/1B CCD data | |
| CN110988909B (en) | TLS-based vegetation coverage measuring method for sand vegetation in severe cold fragile area | |
| CN106056591A (en) | Method for estimating urban density through fusion of optical spectrum image and laser radar data | |
| CN108509836A (en) | Crop yield estimation method based on double-polarized synthetic aperture radar and crop model data assimilation | |
| CN102314546A (en) | Method for estimating plant growth biomass liveweight variation based on virtual plants | |
| CN106126920A (en) | Crops disaster caused by hail disaster area remote sensing evaluation method | |
| CN115950838A (en) | Summer corn drought unmanned aerial vehicle rapid monitoring and distinguishing method based on chlorophyll content | |
| CN107330898A (en) | Altitudinal vegetation zone quantitatively delineates computational methods and system | |
| CN107479065A (en) | A kind of three-dimensional structure of forest gap method for measurement based on laser radar | |
| CN112380497A (en) | Straw coefficient estimation method and system for regional corn | |
| CN111160799A (en) | Natural resource database construction method | |
| LI et al. | Estimating rice yield by HJ-1A satellite images | |
| Song et al. | Estimating effective leaf area index of winter wheat using simulated observation on unmanned aerial vehicle-based point cloud data | |
| Shi et al. | Estimation of sugarcane yield by assimilating UAV and ground measurements via ensemble Kalman filter | |
| CN111289997A (en) | Method for detecting field crop canopy thickness based on laser radar sensor | |
| CN114663814A (en) | Fruit detection and yield estimation method and system based on machine vision | |
| WO2024061160A1 (en) | Method for rapidly monitoring and determining drought condition of winter wheat by using unmanned aerial vehicle and on basis of leaf area index | |
| CN113534083A (en) | Corn stubble mode identification method, device and medium based on SAR | |
| Memduhoğlu | Identifying impervious surfaces for rainwater harvesting feasibility using unmanned aerial vehicle imagery and machine learning classification |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20171107 Termination date: 20211120 |