CN106813576A - A kind of leaf area, girth, leaf measuring method long and leaf width - Google Patents
A kind of leaf area, girth, leaf measuring method long and leaf width Download PDFInfo
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- CN106813576A CN106813576A CN201710059592.8A CN201710059592A CN106813576A CN 106813576 A CN106813576 A CN 106813576A CN 201710059592 A CN201710059592 A CN 201710059592A CN 106813576 A CN106813576 A CN 106813576A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
- H04N1/04—Scanning arrangements, i.e. arrangements for the displacement of active reading or reproducing elements relative to the original or reproducing medium, or vice versa
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- 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
- G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
Abstract
The present invention relates to a kind of leaf area, girth, leaf measuring method long and leaf width, the leaf of collection is preserved into picture format after scan module is scanned first；Secondly using the model constructor tectonic model of ArcGIS softwares, the model of construction includes ISO cluster unsupervised classifications module, field computing module, input/output module, and the ISO cluster unsupervised classifications module, field computing module and input/output module form the instrument with friendly interface；Again, leaf image and outgoing route are input into using the instrument of generation, in generation file per the girth of leaf, area, leaf be long and leaf width data；It is intended to by ArcGIS softwares, the fresh approach of utilization space analysis and graph image, by model constructor Construct Tool, the accurate leaf area of automatic batch acquisition, girth, leaf be long and the leaf parameter such as leaf width.
Description
Technical field
The present invention relates to a kind of leaf area, girth, leaf measuring method long and leaf width, belong to digital measurement field.
Background technology
Blade carries out the major organs of respiration, photosynthesis and transpiration as plant, its leaf area, girth,
Leaf is long and the information such as leaf width is important parameter in plant growth state diagnosis, the growth rhythm of plant leaf blade is grasped, for referring to
Production is led, formulating High yield cultivation technique measure has positive meaning.Leaf area determination method common at present has
Gridding method, duplicating weight method, leaf leaf width predication method long, leaf area instrument method, scanner method, AutoCAD etc..Gridding method and duplicating claim
Weight method, it is simple to operate, but time and effort consuming；Leaf leaf width predication method long needs to obtain the blade face of certain amount sample using other method
Product, according to statistical analysis prediction equation, could calculate using formula and measurement leaf are long with leaf width；Leaf area instrument method, using special
Leaf area can be quickly obtained with leaf area instrument, but instrument price is expensive；Scanning method is with software statistics such as Photoshop, R2V
The method of pixel determines blade area.The above method can only typically obtain blade area, it is difficult to while the girth of acquisition blade,
Leaf is long and leaf width, is especially difficult to the disposable measurement task for completing multiple blades.Digital image method is exactly by counting blade figure
The pixel count of picture is converted into blade area, and the premise of this method is to need to know the reality in image representated by each pixel
Area.According to scanning resolution DPI, it may be determined that the relation of leaf image pixel and physical length.《Forest-science》Volume 50 the 5th
Phase《Mangrove blade information measurement based on automated taxonomy》Disclose automatic using the spatial analysis functions and image of GIS
Principle of classification, the method that blade information measurement device is built in software ARC GISIO；Because the scanning of multi-disc leaf is in an image
In, and there are multiple scan images in file.Accordingly, it is considered to need solution many to ask using computer automatic batch measurement
Topic：
1) every leaf profile how is accurately extracted；
2) how the area of figure, girth are converted to real area, girth；
3) leaf is long, how leaf width is calculated；
4) how measure batch is realized；
5) how record sort measurement data.
The content of the invention
In order to solve the above-mentioned technical problem, the present invention provides a kind of leaf area, girth, leaf measurement side long and leaf width
Method, it is intended to by ArcGIS softwares, the fresh approach of utilization space analysis and graph image constructs work by model constructor
Tool, automatic batch obtains that accurate leaf area, girth, leaf be long and the leaf parameter such as leaf width.
Technical scheme is as follows：
A kind of leaf area, girth, leaf measuring method long and leaf width, are first swept the leaf of collection by scan module
Picture format is preserved into after retouching；Secondly using the model constructor tectonic model of ArcGIS softwares, the model of construction includes input
Output module and the ISO cluster unsupervised classification module and the field computing module that carry out successively, the ISO cluster unsupervised classification
Module, field computing module and input/output module form the instrument with friendly interface；Again, it is input into using the instrument of generation
Leaf image and outgoing route, in generation file per the girth of leaf, area, leaf be long and leaf width data；The mould of construction
Type also includes file picture processing module, edge pretreatment module, grid and vector module, maximum boundary rectangle module and figure
Layer turns tables of data module；Scan module scans leaf and imports ISO by file picture processing module and input/output module
Cluster unsupervised classification module is parsed, and parsing data are processed through edge pretreatment module and obtain leaf border, then by grid
Lattice vector quantization modules carry out data boundary, and the girth and area data of leaf are then obtained by field computing module, pass through
The leaf that maximum external world's rectangular module obtains leaf is long and leaf width data, and the girth and area data and leaf of final leaf be long and leaf
Data wide turn tables of data module by figure layer to be derived.
Wherein, the scan module includes scanner, preserves into JPG or PNG format；The file picture processing module
Including iterator；It is Excel that the figure layer turns the tables of data that tables of data module changes.
Wherein, a kind of leaf area, girth, leaf measuring method long and leaf width, concretely comprise the following steps：
(1) leaf of collection is laid on scanner, space is left between leaf and avoids overlapped, scanning point
Resolution is 100-300dpi；
(2) ISO clusters unsupervised classification, class number is set to 2, and infima species are sized to 200, and the sampling interval is set to 10,
The precondition of attribute is the value that resolution path module is produced；
(3) edge pretreatment module includes mode filtering and border cleaning modul；Mode is filtered, the adjacent key element to be used
Number is set to EIGHT, replaces threshold value and is set to HALF, and performs 2 times；Border cleaning modul, choose " operation twice extension with
Shrink "；
(4) area of the vector polygon that blade profile is surrounded is directly obtained by the Shape.Area functions of module；Blade
The girth that profile is surrounded is directly obtained by Shape.Length functions；According to pixel and the conversion formula of physical length：2.54cm/
DPI, blade real area S=Area* (2.54cm/DPI) ^2；Blade perimeter C=Cir*2.54cm/DPI；Blade profile
The area and girth of the maximum boundary rectangle of vector polygon are directly obtained by function, leaf L=0.25* long (Cir_max+ (Cir_
max^2-16*Area_max)^0.5)*2.54cm/DPI；Leaf width W=0.25* (Cir_max- (Cir_max^2-16*Area_
max)^0.5)*2.54cm/DPI；
(5) using field computing module by the area of blade, girth, leaf is long and leaf width numerical value is added to blade vector graphics
The attribute list of object；Turn tables of data module using figure layer conversion figure layer, the attribute list of blade figure is switched into Excel tables, it is each
Bar record correspondence is per the area of leaf, girth, leaf are long and leaf width.
The present invention has the advantages that：
1st, the measuring method utilization space analysis long and leaf width of a kind of leaf area of the invention, girth, leaf and graph image
Fresh approach, by model constructor Construct Tool, automatic batch obtains that accurate leaf area, girth, leaf be long and leaf
The leaf parameter such as width.
2nd, grating image is converted into vector graphics by the present invention, according to graphics principle reference area, girth, by generation
The leaf of blade is long and the method for leaf width to calculate for the maximum boundary rectangle of leaf image.Quickly accurately obtain each with batch
The blade information such as the area of piece leaf, girth, leaf are long, leaf width；Friendly interface, it is simple to operate；Excel tables are directly generated, favorably
The features such as collect statistics analysis, saving data inputting time.
3rd, the present invention has the blade letters such as quick accurate area, girth, the long, leaf width of leaf obtained per leaf of batch
Breath；Friendly interface, it is simple to operate；Excel tables are directly generated, is conducive to collect statistics to analyze, save the spies such as data inputting time
Point.
Brief description of the drawings
Fig. 1 is the model construction flow signal of a kind of leaf area of the invention, girth, leaf measuring method long and leaf width
Figure；
Fig. 2 is a kind of leaf area of the invention, girth, the module diagram of leaf measuring method long and leaf width；
Fig. 3 is blade of the present invention maximum boundary rectangle schematic diagram.
Specific embodiment
It is next with specific embodiment below in conjunction with the accompanying drawings that the present invention will be described in detail.
As shown in Figure 1, 2, a kind of leaf area, girth, leaf measuring method long and leaf width, will first with scan module
The leaf of collection is laid on the scanner of scan module, preserves into JPG or PNG format；Secondly using the mould of ArcGIS softwares
Type composer tectonic model, the model of construction includes input/output module and the ISO cluster unsupervised classification modules for sequentially carrying out
With field computing module, the instrument with friendly interface is formed；Again, the instrument input leaf image using generation and output road
Footpath, in generation file per the girth of leaf, area, leaf be long and leaf width data；The model of construction also includes thering is iteration
The file picture processing module of device, edge pretreatment module, grid and vector module, maximum boundary rectangle module and figure layer turn
Tables of data module；The effect of iterator is to realize that the scanned picture in file is processed one by one, reaches the effect of batch processing
Really；The tables of data that figure layer turns tables of data module uses Excel；Scan module scans leaf and passes through file picture processing module
ISO cluster unsupervised classification modules are imported with input/output module to be parsed, parsing data are processed through edge pretreatment module
Leaf border is obtained, then data boundary is carried out by grid and vector module, leaf is then obtained by field computing module
Girth and area data, the leaf for obtaining leaf by maximum external world's rectangular module is long and leaf width data, the girth of final leaf
And leaf width data long with area data and leaf turn tables of data module by figure layer to be derived.
Further, it is described based on scan image broad-leaved leaf area, girth, leaf measuring method long and leaf width：
(1) leaf of collection is laid on scanner, space is left between leaf and avoids overlapped, scanning point
Resolution is 100-300dpi；
(2) ISO clusters unsupervised classification, class number is set to 2, and infima species are sized to 200, and the sampling interval is set to 10,
The precondition of attribute is the value that resolution path module is produced；ISO clusters unsupervised classification need not artificially specify group indication, class
Number is set to 2, is only divided into blade-section and the part of background 2, and infima species are dimensioned to 200 pixels, and the sampling interval is set to 10,
That the spot for preventing leaf surfaces generates polygon in small, broken bits, the precondition of attribute for value that resolution path module is produced be in order to
Obtaining the filename of scanning blade is used for the filename of corresponding output vector file；
(3) edge pretreatment module includes mode filtering and border cleaning modul；Mode is filtered, the adjacent key element to be used
Number is set to EIGHT, replaces threshold value and is set to HALF, and performs 2 times；Border cleaning modul, choose " operation twice extension with
Shrink "；Edge pretreatment includes mode filtering and border cleaning, and wherein mode filtering is replaced according to the mode of adjacent picture elements data value
The pixel changed in grid, it is therefore an objective to which preventing individual spot to be mixed into blade-section causes generation cavity, the mistake used in the present embodiment
Filter kernel will be eight adjacent picture elements (3 × 3 window) nearest away from current pixel, and it is indivedual that execution can be good at removal twice
Spot, border cleaning is that operation can make edge smoothing and accord with twice by extending and shrinking come the border between smooth region
Close former blade profile；The conversion formula of scanning resolution and physical length:2.54cm/DPI, realizes elemental area, the length of blade
Degree is converted to actual area and length；
(4) the maximum boundary rectangle of the vector graphics of blade profile, it grows and leaf width long with the leaf of fitting blade wide, rectangle
Area and girth can be directly obtained by function, wherein, the area of the vector polygon that blade profile is surrounded is by module
Shape.Area functions are directly obtained；The girth that blade profile is surrounded is directly obtained by Shape.Length functions；According to pixel
With the conversion formula of physical length：2.54cm/DPI, blade real area S=Area* (2.54cm/DPI) ^2；Blade actual week
C=Cir*2.54cm/DPI long；The area and girth of the maximum boundary rectangle of blade profile vector polygon are directly obtained by function
, leaf L=0.25* long (Cir_max+ (Cir_max^2-16*Area_max) ^0.5) * 2.54cm/DPI；Leaf width W=0.25*
(Cir_max-(Cir_max^2-16*Area_max)^0.5)*2.54cm/DPI；
(5) using field computing module by the area of blade, girth, leaf is long and leaf width numerical value is added to blade vector graphics
The attribute list of object；Turn tables of data module using figure layer conversion figure layer, the attribute list of blade figure is switched into Excel tables, it is each
Bar record correspondence is per the area of leaf, girth, leaf are long and leaf width.
As shown in Figure 1,3, the specific implementation of the above method comprises the following steps：
1. the leaf of collection is laid on scanner, space is left between leaf, scanning resolution is used
300dpi, preserves into png forms.
2. model is built in Arcgis softwares (10.0) model constructor：
1) establishment file folder variable " input scanning blade " is set to model parameter, and file variable " export folders " sets
Model parameter is set to, double-precision variable " scanning resolution " is set to model parameter；
2) iterator (grid), working space or grid catalogue are set to " input scanning blade "；
3) resolution path, parsing type is set to NAME；
4) ISO clusters unsupervised classification, class number is set to 2, and infima species are sized to 200, and the sampling interval is set to 10, category
Property precondition for resolution path module produce value；
5) mode filtering, to be used it is adjacent want prime number to be set to EIGHT, replacement threshold value is set to HALF；
6) mode filtering again, with 4), is equally set；
7) border cleaning, ordering techniques are set to NO_SORT, choose operation extension twice and shrink；
8) grid turns face, and field is set to VALUE, chooses simplified face；
9) screen, output factor kind is set to " % export folders % % values %.shp ", expression formula setting：”
GRIDCODE "=1AND " Shape_Area ">1000；
10) field is deleted, field is deleted and is chosen ID, GRIDCODE；
11) field is added, field name is respectively set to " girth ", " area ", " leaf is long ", " leaf width ", and field type is
DOUBLE；
12) calculated field, field name is set to " girth ", expression formula：round(！Shape.length！* 2.54/% sweeps
Retouch resolution ratio %, 1), type expression PYTHON_9.3；
13) calculated field, field name is set to " area ", expression formula：round(！Shape.Area！* 2.54*2.54/%
Scanning resolution %/% scanning resolutions %, 2), type expression PYTHON_9.3；
14) minimum boundary geometrical, output factor kind is set to " % export folders % LCK% values %.shp ", geometry class
Type is set to RECTANGLE_BY_WIDTH, chooses during geometric properties are exported as attribute addition；
15) calculated field, field name is set to " leaf is long ", expression formula：round(0.25*(！Shape.length！+(！
Shape.length！^2-16*！Shape.Area！) ^0.5*2.54/% scanning resolutions %, 1), type expression PYTHON_
9.3；
16) calculated field, field name is set to " leaf width ", expression formula：round(0.25*(！Shape.length！-(！
Shape.length！^2-16*！Shape.Area！) ^0.5*2.54/% scanning resolutions %, 1), type expression PYTHON_
9.3；
17) table turns Excel, and output Excel file is set to " % export folders %/% values %.xls "
3. the instrument for building is double-clicked, and |input paramete clicks on operation.The leaf that the veneer of the present embodiment 150 lookes at, scans 31 figures
Piece file, 12 points of measuring and calculating in 52 seconds are completed.
The inventive method accuracy is checked：
1. the reference plane using different type known circumferential length, area is (square, rectangle, triangle, circle, trapezoidal
Deng), the area and girth for calculating these reference planes with the method for the present invention are compareed with actual value：
Area extraction value is 0~0.028 with the relative error (absolute value) of actual value, and average area relative error is
0.O13；Between 0~0.018, average perimeter relative error is the relative error (absolute value) of girth extraction of values and actual value
0.007；Between 0~0.032, average length relative error is the relative error (absolute value) of length extraction of values and actual value
0.002；Between 0~0.029, mean breadth relative error is the relative error (absolute value) of width extraction of values and actual value
0.004。
2. compared with photoshop, gridding method
Area that the method for the present invention and Photoshop methods are determined, girth, leaf is long and leaf width is all closest, its sample
Average value and standard deviation are not significantly different from.The coefficient of variation of automated taxonomy, gridding method and Photoshop methods does not have significance difference
It is different.
Embodiments of the invention are the foregoing is only, the scope of the claims of the invention is not thereby limited, it is every to utilize this hair
Equivalent structure or equivalent flow conversion that bright specification and accompanying drawing content are made, or directly or indirectly it is used in other related skills
Art field, is included within the scope of the present invention.
Claims (3)
1. a kind of leaf area, girth, leaf measuring method long and leaf width, the leaf that will be gathered first is scanned by scan module
After preserve into picture format；Secondly using the model constructor tectonic model of ArcGIS softwares, the model of construction includes that input is defeated
Go out module, ISO cluster unsupervised classification module and field computing module, the ISO cluster unsupervised classifications module, field computing
Module and input/output module form the instrument with friendly interface；Again, using the instrument input leaf image and defeated of generation
Outbound path, in generation file per the girth of leaf, area, leaf be long and leaf width data；It is characterized in that：The model of construction
Also include file picture processing module, edge pretreatment module, grid and vector module, maximum boundary rectangle module and figure layer
Turn tables of data module；Scan module scans leaf and imports ISO by file picture processing module and input/output module and gathers
Class unsupervised classification module is parsed, and parsing data are processed through edge pretreatment module and obtain leaf border, then by grid
Vector quantization module carries out data boundary, the girth and area data of leaf is then obtained by field computing module, by most
The leaf that big external world's rectangular module obtains leaf is long and leaf width data, and the girth and area data and leaf of final leaf be long and leaf width
Data turn tables of data module by figure layer to be derived.
2. as claimed in claim 1 a kind of leaf area, girth, leaf measuring method long and leaf width, it is characterised in that：It is described
Scan module includes scanner, preserves into JPG or PNG format；The file picture processing module includes iterator；The figure
It is Excel that layer turns the tables of data that tables of data module changes.
3. a kind of leaf area according to claim 2, girth, leaf measuring method long and leaf width, it is characterised in that：
（1）The leaf of collection is laid on scanner, space is left between leaf and is avoided overlapped, scanning resolution
It is 100-300dpi；
（2）ISO clusters unsupervised classification, and class number is set to 2, and infima species are sized to 200, and the sampling interval is set to 10, attribute
Precondition for resolution path module produce value；
（3）Edge pretreatment module includes mode filtering and border cleaning modul；Mode is filtered, and what is used adjacent wants prime number to set
EIGHT is set to, threshold value is replaced and is set to HALF, and perform 2 times；Border cleaning modul, chooses " operation extension twice and receipts
Contracting "；
（4）The area of the vector polygon that blade profile is surrounded is directly obtained by the Shape.Area functions of module；Blade profile
The girth for surrounding is directly obtained by Shape.Length functions；According to pixel and the conversion formula of physical length：2.54cm/DPI,
Blade real area S=Area* (2.54cm/DPI) ^2；Blade perimeter C=Cir*2.54cm/DPI；Blade profile vector is more
The area and girth of the maximum boundary rectangle of side shape are directly obtained by function, leaf L=0.25* long (Cir_max+ (Cir_max^2-
16*Area_max)^0.5)*2.54cm/DPI；Leaf width W=0.25* (Cir_max- (Cir_max^2-16*Area_max) ^
0.5)*2.54cm/DPI；
（5）Using field computing module by the area of blade, girth, leaf is long and leaf width numerical value is added to blade vector graphic object
Attribute list；Turn tables of data module using figure layer conversion figure layer, the attribute list of blade figure is switched into Excel tables, each note
Record correspondence is per the area of leaf, girth, leaf are long and leaf width.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108318098A (en) * | 2018-02-01 | 2018-07-24 | 山西省农业科学院小麦研究所 | A kind of assay method of wheat flag leaf volume |
| CN108613639A (en) * | 2018-05-11 | 2018-10-02 | 中国建材检验认证集团浙江有限公司 | Ornamental engraving decorated gypsum board hollow out rate measurement method |
| CN109059808A (en) * | 2018-08-15 | 2018-12-21 | 华南农业大学 | Method for measuring leaf area, system, storage medium and mobile terminal |
| CN109145414A (en) * | 2018-08-06 | 2019-01-04 | 上海华虹宏力半导体制造有限公司 | A kind of method and system improving post-layout simulation results exhibit accuracy |
| CN111043939A (en) * | 2019-12-31 | 2020-04-21 | 云南省红河热带农业科学研究所 | Leaf area correction coefficient acquisition method for swertia undulata |
| CN112461075A (en) * | 2020-11-05 | 2021-03-09 | 内蒙古工业大学 | Grassland vegetation leaf area measuring tool and method |
| CN113099065A (en) * | 2021-04-02 | 2021-07-09 | 上海海事大学 | Handheld portable plant leaf rolling adsorption scanning device and scanning method thereof |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105930805A (en) * | 2016-04-25 | 2016-09-07 | 云南瀚哲科技有限公司 | ArcGIS-based basic grassland type judging method |
-
2017
- 2017-01-24 CN CN201710059592.8A patent/CN106813576A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105930805A (en) * | 2016-04-25 | 2016-09-07 | 云南瀚哲科技有限公司 | ArcGIS-based basic grassland type judging method |
Non-Patent Citations (2)
| Title |
|---|
| 乐通潮 等: "基于自动分类法的红树植物叶片信息测定", 《林业科学》 * |
| 赖日文: "《3S技术实践教程》", 30 September 2014, 浙江大学出版社 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108318098A (en) * | 2018-02-01 | 2018-07-24 | 山西省农业科学院小麦研究所 | A kind of assay method of wheat flag leaf volume |
| CN108613639A (en) * | 2018-05-11 | 2018-10-02 | 中国建材检验认证集团浙江有限公司 | Ornamental engraving decorated gypsum board hollow out rate measurement method |
| CN109145414A (en) * | 2018-08-06 | 2019-01-04 | 上海华虹宏力半导体制造有限公司 | A kind of method and system improving post-layout simulation results exhibit accuracy |
| CN109059808A (en) * | 2018-08-15 | 2018-12-21 | 华南农业大学 | Method for measuring leaf area, system, storage medium and mobile terminal |
| CN111043939A (en) * | 2019-12-31 | 2020-04-21 | 云南省红河热带农业科学研究所 | Leaf area correction coefficient acquisition method for swertia undulata |
| CN112461075A (en) * | 2020-11-05 | 2021-03-09 | 内蒙古工业大学 | Grassland vegetation leaf area measuring tool and method |
| CN113099065A (en) * | 2021-04-02 | 2021-07-09 | 上海海事大学 | Handheld portable plant leaf rolling adsorption scanning device and scanning method thereof |
| CN113099065B (en) * | 2021-04-02 | 2022-10-28 | 上海海事大学 | Handheld portable rolling adsorption scanning device for plant leaves and scanning method thereof |
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