CN103229614A - Automatic corn ear test method - Google Patents
Automatic corn ear test method Download PDFInfo
- Publication number
- CN103229614A CN103229614A CN201310139259XA CN201310139259A CN103229614A CN 103229614 A CN103229614 A CN 103229614A CN 201310139259X A CN201310139259X A CN 201310139259XA CN 201310139259 A CN201310139259 A CN 201310139259A CN 103229614 A CN103229614 A CN 103229614A
- Authority
- CN
- China
- Prior art keywords
- data
- seed
- fruit ear
- ear
- pixel
- 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
Images
Landscapes
- Investigating Or Analysing Materials By Optical Means (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses an automatic corn ear test method, comprising the following steps of: S1, acquiring data of weight of corn ear; S2, acquiring corn ear interpreting data; S3, separating kernels from corn cobs; S4, acquiring kernel interpreting data; S5, acquiring kernel weight data, average kernel weight data and data of weight of one hundred kernels; S6, acquiring data of volume weight and water content of the kernels; S7, sorting and collecting the kernels; S8, acquiring corn cob interpreting data; S9, acquiring corn cob weight data; S10, calculating data of the sum of the corn ear kernels according to the average kernel weight data in the S5; and S11, calculating kernel row data according to the data of the sum of the corn ear kernels in the S10 and the corn ear interpreting data in the S2. The automatic corn ear test method and assembly line type automatic corn ear test operation are realized, so that the corn ear test efficiency and the corn ear test data accuracy are greatly improved, labor investment is greatly reduced and the labor cost is effectively reduced.
Description
Technical field
The present invention relates to corn species test technical field, particularly the automatic species test method of a kind of corn ear.
Background technology
Corn accounts for critical role in China's grain-production, corn is the important component part that fields such as industry scientific research, production are planted by China, one of key problem of corn kind industry is the corn species test, traditional species test method depends on manual operations mostly, take a large amount of human resources, operating efficiency is low, is difficult to simultaneously the leading indicator of corn ear be measured, and becomes the technical bottleneck that restriction corn kind already develops.Mainly there is following technological deficiency in tradition species test method: 1) workload is big, manual operation is loaded down with trivial details, thereby it is more to take human resources; 2) operating efficiency is difficult to improve, and the species test process cycle is longer; 3) every data inaccuracy that the accuracy that the manual operation influence is passed the examination in the species test process makes species test.Above-mentioned these defectives are one of major issues of already developing of restriction corn kind.
Summary of the invention
(1) technical problem that will solve
The technical problem to be solved in the present invention is, at the deficiencies in the prior art, provides a kind of corn ear automatic species test method, realizes accurately, the operation of corn species test fast and efficiently.
(2) technical scheme
The invention provides the automatic species test method of a kind of corn ear, comprising:
S1: corn ear is sent to the fruit ear weighing device by conveyer, obtains the weight data of described corn ear and sends to display unit;
S2: corn ear is sent to the fruit ear image collecting device, obtains the fruit ear image and send in the fruit ear image analysis apparatus and analyze, draw the fruit ear resolution data and send to display unit;
S3: corn ear is sent in the sheller unit, seed is separated with cob;
S4: seed is sent to the seed image collecting device, obtains the seed image and send in the seed image analysis apparatus and analyze, draw the seed resolution data and send to display unit;
S5: all seeds are sent to the seed weighing device, obtain the weight data of described seed and draw seed average weight data and 100-grain weight amount data according to the seed resolution data among the S4, send weight data, seed average weight data and the 100-grain weight amount data of described all seeds to display unit;
S6: seed is sent to the molten heavy water of seed divides device, obtain unit weight and the water content data of seed and send to display unit;
S7: be that unit is collected respectively with the fruit ear by bar code with seed;
S8: cob is sent to the cob image collecting device, obtains the cob image and send in the cob image analysis apparatus and analyze, draw the cob resolution data and send to display unit;
S9: cob is sent to the cob weighing device, obtains the weight data of described cob and send to display unit;
S10: have a shower data and seed average weight data according to all seeds among the S5, calculate the total logarithmic data of fruit ear seed, send in the display unit;
S11: calculate fringe grain line number data according to the total logarithmic data of fruit ear seed among the S10 and the fruit ear resolution data among the S2 and send in the display unit.
Wherein, described fruit ear resolution data comprises: the length data of fruit ear, diameter data, row grain logarithmic data, bald sharp rate data, seed width data, seed thickness data and seed top color data.
Wherein, described seed resolution data comprises: seed number data, seed length data and seed side color data.
Wherein, described cob resolution data comprises: the cob diameter data.
Wherein, in S11, calculate fringe grain line number data according to the row grain logarithmic data among the total logarithmic data of fruit ear seed among the S10 and the S2 and send in the display unit.
Wherein, the length data of fruit ear, diameter data are obtained and are comprised the steps: among the S2
S21: to the corn ear image binaryzation, extract the fruit ear pixel in the described corn ear image;
S22: the fruit ear pixel is done refinement by 8 neighborhoods handle, be refined as single pixel connected curve;
S23: use method of least squares, the linear equation of the last point of the described single pixel connected curve of match, straight line and vertical direction angulation are exactly the angle of inclination of fruit ear;
S24: according to the angle of inclination among S23 rotation fruit ear image, calculate the outsourcing rectangle of fruit ear image, with the length data of described outsourcing rectangle and width data length data and the diameter data as fruit ear.
Wherein, a row grain logarithmic data, bald sharp rate data, seed width data, seed thickness data and the seed top color data of fruit ear obtained and comprised the steps: among the S2
S21 ': to the corn ear image binaryzation, extract the fruit ear pixel in the described corn ear image;
S22 ': statistics fruit ear pixel average, and be segmentation threshold with this average, described fruit ear pixel is cut apart;
S23 ': for step-length increases segmentation threshold, the fruit ear pixel is carried out dividing processing according to gray value 4, the fruit ear pixel after cutting apart among result and the S22 ' merges;
S24 ': return S22 ' till described fruit ear pixel segmentation threshold value is more than or equal to 255;
S25 ': the fruit ear pixel that draws according to S24 ' is calculated bald sharp rate data, seed width data, seed thickness data and the seed top color data of fruit ear;
S26 ': the centre position seed pixel of obtaining the fruit ear pixel that draws among the S24 ';
S27 ': according to the nearest neighbor search mode, the fruit ear pixel upper end seed nearest apart from the centre position seed that search S24 ' obtains;
S28 ': repeat S27 ' up to arriving corn ear top seed, draw fruit ear pixel upper end row grain number numerical value;
S29 ': according to the nearest neighbor search mode, the fruit ear pixel lower end seed nearest apart from the centre position seed that search S24 ' obtains;
S30 ': repeat S29 ' up to arriving corn ear bottom seed, draw fruit ear pixel lower end row grain and count numerical value;
S31 ': merge top row grain number numerical value and below seed line number value, obtain complete delegation's seed numerical value.
(3) beneficial effect
The present invention fully utilizes mechanical automation technology and computer graphic image technology, realize corn ear automation species test method, realize the automation species test operation of pipeline system, significantly improved the accuracy of corn ear species test efficient and species test data, significantly reduce artificial the input, effectively reduce cost of labor.
Description of drawings
Fig. 1 is the automatic species test method step of corn ear of the present invention flow chart;
Fig. 2 is 8 neighborhood schematic diagrames of the present invention.
Embodiment
Below in conjunction with drawings and Examples, the specific embodiment of the present invention is described in further detail.Following examples are used for explanation the present invention, but are not used for limiting the scope of the invention.
As described in Figure 1,
The invention provides the automatic species test method of a kind of corn ear, comprising:
S1: corn ear is sent to the fruit ear weighing device by conveyer, obtains the weight data of described corn ear and sends to display unit;
S2: corn ear is sent to the fruit ear image collecting device, obtains the fruit ear image and send in the fruit ear image analysis apparatus and analyze, draw the fruit ear resolution data and send to display unit;
S3: corn ear is sent in the sheller unit, seed is separated with cob;
S4: seed is sent to the seed image collecting device, obtains the seed image and send in the seed image analysis apparatus and analyze, draw the seed resolution data and send to display unit;
S5: all seeds are sent to the seed weighing device, obtain the weight data of described seed and draw seed average weight data and 100-grain weight amount data according to the seed resolution data among the S4, send weight data, seed average weight data and the 100-grain weight amount data of described all seeds to display unit;
S6: seed is sent to the molten heavy water of seed divides device, obtain unit weight and the water content data of seed and send to display unit;
S7: with the seed categorised collection, give each corn ear configuration a bar code when gathering, bar code comprises supplementarys such as the kind, cultivation, sequence number of this fruit ear, and this bar code is the sign of the corresponding fruit ear species test of index indication information;
S8: cob is sent to the cob image collecting device, obtains the cob image and send in the cob image analysis apparatus and analyze, draw the cob resolution data and send to display unit;
S9: cob is sent to the cob weighing device, obtains the weight data of described cob and send to display unit;
S10: have a shower data and seed average weight data according to all seeds among the S5, calculate the total logarithmic data of fruit ear seed, send in the display unit;
S11: calculate fringe grain line number data according to the total logarithmic data of fruit ear seed among the S10 and the fruit ear resolution data among the S2 and send in the display unit.
Wherein, described fruit ear resolution data comprises: the length data of fruit ear, diameter data, row grain logarithmic data, bald sharp rate data, seed width data, seed thickness data and seed top color data.
Wherein, described seed resolution data comprises: seed number data, seed length data and seed side color data.
Wherein, described cob resolution data comprises: the cob diameter data.
Wherein, in S11, calculate fringe grain line number data according to the row grain logarithmic data among the total logarithmic data of fruit ear seed among the S10 and the S2 and send in the display unit.
Wherein, the length data of fruit ear, diameter data are obtained and are comprised the steps: among the S2
S21: to the corn ear image binaryzation, extract the fruit ear pixel in the described corn ear image;
S22: the fruit ear pixel is done refinement by 8 neighborhoods handle, be refined as single pixel connected curve;
S23: use method of least squares, the linear equation of the last point of the described single pixel connected curve of match, straight line and vertical direction angulation are exactly the angle of inclination of fruit ear;
S24: according to the angle of inclination among S23 rotation fruit ear image, calculate the outsourcing rectangle of fruit ear image, with the length data of described outsourcing rectangle and width data length data and the diameter data as fruit ear.
Wherein, a row grain logarithmic data, bald sharp rate data, seed width data, seed thickness data and the seed top color data of fruit ear obtained and comprised the steps: among the S2
S21 ': to the corn ear image binaryzation, extract the fruit ear pixel in the described corn ear image;
S22 ': statistics fruit ear pixel average, and be segmentation threshold with this average, described fruit ear pixel is cut apart;
S23 ': for step-length increases segmentation threshold, the fruit ear pixel is carried out dividing processing according to gray value 4, the fruit ear pixel after cutting apart among result and the S22 ' merges;
S24 ': return S22 ' till described fruit ear pixel segmentation threshold value is more than or equal to 255;
S25 ': the fruit ear pixel that draws according to S24 ' is calculated bald sharp rate data, seed width data, seed thickness data and the seed top color data of fruit ear;
S26 ': the centre position seed pixel of obtaining the fruit ear pixel that draws among the S24 ';
S27 ': according to the nearest neighbor search mode, the fruit ear pixel upper end seed nearest apart from the centre position seed that search S24 ' obtains;
S28 ': repeat S27 ' up to arriving corn ear top seed, draw fruit ear pixel upper end row grain number numerical value;
S29 ': according to the nearest neighbor search mode, the fruit ear pixel lower end seed nearest apart from the centre position seed that search S24 ' obtains;
S30 ': repeat S29 ' up to arriving corn ear bottom seed, draw fruit ear pixel lower end row grain and count numerical value;
S31 ': merge top row grain number numerical value and below seed line number value, obtain complete delegation's seed numerical value.
In addition, image collecting device is installed in and gathers in the casing in the present invention, and the casing lower end is open, long 800mm, wide 500mm, high 400mm; Described industrial camera is installed in the casing top, towards the below, is taken by computer control, and industrial camera can be under complex working condition, and continuously shot images reaches per second 12 frames, effective long 750mm of image acquisition region in the casing.
Fruit ear weighing device, seed weighing device and cob weighing device are computer-controlled weight sensor.
The drive unit of species test system is stepper motor, realizes running in order of each link respectively under computer control.
Above embodiment only is used for explanation the present invention; and be not limitation of the present invention; the those of ordinary skill in relevant technologies field; under the situation that does not break away from the spirit and scope of the present invention; can also make a variety of changes and modification; therefore all technical schemes that are equal to also belong to category of the present invention, and scope of patent protection of the present invention should be defined by the claims.
Claims (7)
1. the automatic species test method of corn ear is characterized in that, comprising:
S1: corn ear is sent to the fruit ear weighing device by conveyer, obtains the weight data of described corn ear and sends to display unit;
S2: corn ear is sent to the fruit ear image collecting device, obtains the fruit ear image and send in the fruit ear image analysis apparatus and analyze, draw the fruit ear resolution data and send to display unit;
S3: corn ear is sent in the sheller unit, seed is separated with cob;
S4: seed is sent to the seed image collecting device, obtains the seed image and send in the seed image analysis apparatus and analyze, draw the seed resolution data and send to display unit;
S5: all seeds are sent to the seed weighing device, obtain the weight data of described seed and draw seed average weight data and 100-grain weight amount data according to the seed resolution data among the S4, send weight data, seed average weight data and the 100-grain weight amount data of described all seeds to display unit;
S6: seed is sent to the molten heavy water of seed divides device, obtain unit weight and the water content data of seed and send to display unit;
S7: be that unit is collected respectively with the fruit ear by bar code with seed;
S8: cob is sent to the cob image collecting device, obtains the cob image and send in the cob image analysis apparatus and analyze, draw the cob resolution data and send to display unit;
S9: cob is sent to the cob weighing device, obtains the weight data of described cob and send to display unit;
S10: have a shower data and seed average weight data according to all seeds among the S5, calculate the total logarithmic data of fruit ear seed, send in the display unit;
S11: calculate fringe grain line number data according to the total logarithmic data of fruit ear seed among the S10 and the fruit ear resolution data among the S2 and send in the display unit.
2. the method for claim 1 is characterized in that, described fruit ear resolution data comprises: the length data of fruit ear, diameter data, row grain logarithmic data, bald sharp rate data, seed width data, seed thickness data and seed top color data.
3. the method for claim 1 is characterized in that, described seed resolution data comprises: seed number data, seed length data and seed side color data.
4. the method for claim 1 is characterized in that, described cob resolution data comprises: the cob diameter data.
5. method as claimed in claim 2 is characterized in that, in S11, calculates fringe grain line number data according to the row grain logarithmic data among the total logarithmic data of fruit ear seed among the S10 and the S2 and sends in the display unit.
6. method as claimed in claim 2 is characterized in that, the length data of fruit ear, diameter data are obtained and comprised the steps: among the S2
S21: to the corn ear image binaryzation, extract the fruit ear pixel in the described corn ear image;
S22: the fruit ear pixel is done refinement by 8 neighborhoods handle, be refined as single pixel connected curve;
S23: use method of least squares, the linear equation of the last point of the described single pixel connected curve of match, straight line and vertical direction angulation are exactly the angle of inclination of fruit ear;
S24: according to the angle of inclination among S23 rotation fruit ear image, calculate the outsourcing rectangle of fruit ear image, with the length data of described outsourcing rectangle and width data length data and the diameter data as fruit ear.
7. method as claimed in claim 2 is characterized in that, row grain logarithmic data, bald sharp rate data, seed width data, seed thickness data and the seed top color data of fruit ear obtained and comprised the steps: among the S2
S21 ': to the corn ear image binaryzation, extract the fruit ear pixel in the described corn ear image;
S22 ': statistics fruit ear pixel average, and be segmentation threshold with this average, described fruit ear pixel is cut apart;
S23 ': for step-length increases segmentation threshold, the fruit ear pixel is carried out dividing processing according to gray value 4, the fruit ear pixel after cutting apart among result and the S22 ' merges;
S24 ': return S22 ' till described fruit ear pixel segmentation threshold value is more than or equal to 255;
S25 ': the fruit ear pixel that draws according to S24 ' is calculated bald sharp rate data, seed width data, seed thickness data and the seed top color data of fruit ear;
S26 ': the centre position seed pixel of obtaining the fruit ear pixel that draws among the S24 ';
S27 ': according to the nearest neighbor search mode, the fruit ear pixel upper end seed nearest apart from the centre position seed that search S24 ' obtains;
S28 ': repeat S27 ' up to arriving corn ear top seed, draw fruit ear pixel upper end row grain number numerical value;
S29 ': according to the nearest neighbor search mode, the fruit ear pixel lower end seed nearest apart from the centre position seed that search S24 ' obtains;
S30 ': repeat S29 ' up to arriving corn ear bottom seed, draw fruit ear pixel lower end row grain and count numerical value;
S31 ': merge top row grain number numerical value and below seed line number value, obtain complete delegation's seed numerical value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310139259.XA CN103229614B (en) | 2013-04-19 | 2013-04-19 | Automatic corn ear test method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310139259.XA CN103229614B (en) | 2013-04-19 | 2013-04-19 | Automatic corn ear test method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103229614A true CN103229614A (en) | 2013-08-07 |
| CN103229614B CN103229614B (en) | 2014-07-02 |
Family
ID=48877754
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310139259.XA Active CN103229614B (en) | 2013-04-19 | 2013-04-19 | Automatic corn ear test method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103229614B (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103983333A (en) * | 2014-05-18 | 2014-08-13 | 山西省农业科学院小麦研究所 | Method for measuring number of grains per wheat ear |
| CN104656516A (en) * | 2015-01-22 | 2015-05-27 | 中国农业大学 | Distributed corn test data acquisition system |
| CN104865259A (en) * | 2015-05-07 | 2015-08-26 | 中国农业大学 | Falling type corn ear holographic character rapid measuring system and method |
| CN105009731A (en) * | 2015-06-30 | 2015-11-04 | 华中农业大学 | Corn test method and system thereof |
| CN105027732A (en) * | 2015-07-09 | 2015-11-11 | 北京农业信息技术研究中心 | Method and system for corn ear seed test |
| CN105766128A (en) * | 2016-03-04 | 2016-07-20 | 北京农业智能装备技术研究中心 | High-flux full-automatic corn variety study production line device |
| CN108195724A (en) * | 2017-12-04 | 2018-06-22 | 北京农业信息技术研究中心 | The measuring method and measuring device of grain composition content |
| US10186029B2 (en) | 2014-09-26 | 2019-01-22 | Wisconsin Alumni Research Foundation | Object characterization |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102004063769A1 (en) * | 2004-12-30 | 2006-07-13 | Perner, Petra, Dr.-Ing. | Method for automatically and quantitatively determining the amount of seed or grain of required quality comprises recording the seed and grain using an imaging device and further processing |
| CN102960094A (en) * | 2012-11-13 | 2013-03-13 | 北京农业信息技术研究中心 | Corn test device and method |
| CN103026823A (en) * | 2012-12-25 | 2013-04-10 | 北京农业信息技术研究中心 | High-efficiency corn ear seed test method and device based on image |
| CN103039154A (en) * | 2012-12-25 | 2013-04-17 | 北京农业信息技术研究中心 | Method and device for performing high-precision determination of corn ear variety based on images |
-
2013
- 2013-04-19 CN CN201310139259.XA patent/CN103229614B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102004063769A1 (en) * | 2004-12-30 | 2006-07-13 | Perner, Petra, Dr.-Ing. | Method for automatically and quantitatively determining the amount of seed or grain of required quality comprises recording the seed and grain using an imaging device and further processing |
| CN102960094A (en) * | 2012-11-13 | 2013-03-13 | 北京农业信息技术研究中心 | Corn test device and method |
| CN103026823A (en) * | 2012-12-25 | 2013-04-10 | 北京农业信息技术研究中心 | High-efficiency corn ear seed test method and device based on image |
| CN103039154A (en) * | 2012-12-25 | 2013-04-17 | 北京农业信息技术研究中心 | Method and device for performing high-precision determination of corn ear variety based on images |
Non-Patent Citations (3)
| Title |
|---|
| 吕良: "玉米考种样取样有新法", 《四川农业科技》 * |
| 曹婧华等: "玉米考种系统的设计与实现", 《长春师范学院学报》 * |
| 郭艳艳等: "数字图像处理技术在玉米种植中的应用", 《中国种业》 * |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103983333B (en) * | 2014-05-18 | 2016-08-31 | 山西省农业科学院小麦研究所 | A kind of wheat grains per spike assay method |
| CN103983333A (en) * | 2014-05-18 | 2014-08-13 | 山西省农业科学院小麦研究所 | Method for measuring number of grains per wheat ear |
| US10186029B2 (en) | 2014-09-26 | 2019-01-22 | Wisconsin Alumni Research Foundation | Object characterization |
| CN104656516A (en) * | 2015-01-22 | 2015-05-27 | 中国农业大学 | Distributed corn test data acquisition system |
| CN104865259A (en) * | 2015-05-07 | 2015-08-26 | 中国农业大学 | Falling type corn ear holographic character rapid measuring system and method |
| CN105009731A (en) * | 2015-06-30 | 2015-11-04 | 华中农业大学 | Corn test method and system thereof |
| CN105009731B (en) * | 2015-06-30 | 2017-11-07 | 华中农业大学 | Corn seed investigating method and its system |
| CN105027732B (en) * | 2015-07-09 | 2017-06-20 | 北京农业信息技术研究中心 | The method and system of corn ear test |
| CN105027732A (en) * | 2015-07-09 | 2015-11-11 | 北京农业信息技术研究中心 | Method and system for corn ear seed test |
| CN105766128A (en) * | 2016-03-04 | 2016-07-20 | 北京农业智能装备技术研究中心 | High-flux full-automatic corn variety study production line device |
| CN105766128B (en) * | 2016-03-04 | 2018-09-11 | 北京农业智能装备技术研究中心 | High-throughput full-automatic corn species test flow-line equipment |
| CN108195724A (en) * | 2017-12-04 | 2018-06-22 | 北京农业信息技术研究中心 | The measuring method and measuring device of grain composition content |
| CN108195724B (en) * | 2017-12-04 | 2020-06-30 | 北京农业信息技术研究中心 | Measuring method and measuring device for grain component content |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103229614B (en) | 2014-07-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103229614B (en) | Automatic corn ear test method | |
| CN101905215B (en) | Digitalized paddy rice seed testing machine | |
| CN105203543B (en) | FCL red grape fruit size fractionation devices and methods therefor based on machine vision | |
| CN102384767B (en) | Nondestructive detection device and method for facility crop growth information | |
| CN103752531B (en) | A kind of Nicotiana tabacum L. sorter based on machine vision | |
| CN105181714B (en) | A kind of bottling liquid Impurity Detection System and its detection method based on image procossing | |
| CN202382768U (en) | Facility crop growth information nondestructive test apparatus | |
| CN203661799U (en) | Intelligent testing equipment for boar breeding | |
| CN104737707A (en) | Combine harvester cleaning impurity rate self-adaptive control device and self-adaptive control cleaning method | |
| CN105783990B (en) | A kind of panoramic picture measuring system for accurate species test in corn ear room | |
| CN203156758U (en) | Tail end actuator for automatic globular fruit picking machine | |
| CN106370667A (en) | Visual detection apparatus and method for quality of corn kernel | |
| CN108776978A (en) | A kind of threshed redried strips piece shape characterizing method | |
| CN105009731A (en) | Corn test method and system thereof | |
| CN105447860B (en) | A kind of wheat seeding method of counting | |
| CN103026823B (en) | High-efficiency corn ear seed test method and device based on image | |
| CN207408272U (en) | Rice grain shape parameter measuring apparatus based on linear array camera and X-ray Double-mode imaging | |
| CN202548896U (en) | System for measuring steel packaging parameters | |
| CN107886497A (en) | A kind of wheat flag leaf automatic nondestructive detection and measuring method based on image | |
| CN203664217U (en) | Tobacco leaf sorting machine based on machine vision | |
| CN114858984A (en) | Muskmelon germplasm resource commodity data acquisition device and acquisition and analysis method | |
| CN207570782U (en) | A kind of garden scissor fatigue tester | |
| CN103500458A (en) | Method for automatically detecting line number of corncobs | |
| CN217857431U (en) | Pseudo-ginseng section quality grading system based on machine vision | |
| CN208207889U (en) | Egg of oak silkworm counting device in a kind of agricultural breeding |
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 |