CN113534182A - Straw bundled crop row detection method and equipment and storage medium - Google Patents
Straw bundled crop row detection method and equipment and storage medium Download PDFInfo
- Publication number
- CN113534182A CN113534182A CN202110587401.1A CN202110587401A CN113534182A CN 113534182 A CN113534182 A CN 113534182A CN 202110587401 A CN202110587401 A CN 202110587401A CN 113534182 A CN113534182 A CN 113534182A
- Authority
- CN
- China
- Prior art keywords
- straw
- gradient
- laser
- point
- detection method
- 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.)
- Pending
Links
- 239000010902 straw Substances 0.000 title claims abstract description 102
- 238000001514 detection method Methods 0.000 title claims abstract description 38
- 238000003860 storage Methods 0.000 title claims abstract description 13
- 238000004422 calculation algorithm Methods 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 8
- 230000006870 function Effects 0.000 claims description 7
- 239000000126 substance Substances 0.000 claims description 3
- 238000012272 crop production Methods 0.000 abstract description 2
- 238000000034 method Methods 0.000 description 7
- 238000004364 calculation method Methods 0.000 description 5
- 238000004891 communication Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000001914 filtration Methods 0.000 description 4
- 239000003086 colorant Substances 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 244000025254 Cannabis sativa Species 0.000 description 1
- 238000012271 agricultural production Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
Abstract
The invention provides a straw bundled crop row detection method, equipment and a storage medium, and relates to the technical field of crop production. The straw bundled crop row detection method comprises the following steps: scanning the straw rows through a laser radar to obtain point cloud data formed by a plurality of returned laser points, wherein the point cloud data comprises angles and distances of the returned laser points, and identifying the straw rows according to the point cloud data. According to the straw bundled crop line detection method, the equipment and the storage medium, the point cloud data scanned by the laser radar is processed, and the straw lines are identified according to different returned angle and distance parameters for reference of path planning, so that support can be provided for intelligent straw bundling by applying a bundling machine.
Description
Technical Field
The invention relates to the technical field of crop production, in particular to a method and equipment for detecting crop rows bundled by straws and a storage medium.
Background
With the rapid development of agricultural machinery, the automation degree of the agricultural production is continuously improved at present. Before straw bundling operation is carried out, scattered straws need to be raked into a line by a rake after the crops are harvested so as to be picked up and bundled by the raker. Because the straw acts as a natural ridge in the grass raking process and is not completely straight, the crop line track can not be identified by applying the current agricultural machinery navigation method. Currently, popular crop row detection methods are mainly based on vision, but the application of vision to detect crop rows is mainly applicable to crops and crop colors with large color difference between the crop colors and the field colors. Generally, when the straws are bundled, the straws are harvested after the crops are harvested, the color of the straws is closer to the color of the field, the edge error is larger when the straw rows are identified by applying a visual scheme, and the errors are easily identified.
Disclosure of Invention
The invention provides a straw bundled crop row detection method, equipment and a storage medium, which are used for solving the defect that visual detection is influenced by ambient light, so that errors are large and are easy to identify in the prior art.
The invention provides a straw bundled crop row detection method, which comprises the following steps: scanning the straw rows through a laser radar to obtain point cloud data formed by a plurality of returned laser points, wherein the point cloud data comprises angles and distances of the returned laser points, and identifying the straw rows according to the point cloud data.
According to the straw bundled crop row detection method provided by the invention, the step of scanning the straw rows through the laser radar to obtain the point cloud data formed by a plurality of returned laser points specifically comprises the following steps: the laser radar is installed at the front end of the traveling equipment, the vertical field angle of the laser radar is-16 degrees to +15 degrees, and the horizontal field angle of the laser radar is 360 degrees.
According to the straw bundled crop row detection method provided by the invention, the laser radar is a 32-line three-dimensional laser radar.
According to the straw bundled crop row detection method provided by the invention, the step of identifying the straw rows according to the point cloud data specifically comprises the following steps:
extracting an interested area through a filter function, determining the gradient of each return laser point in the interested area through a gradient algorithm, and determining the boundary of the straw row based on the gradient of each return laser point.
According to the straw bundled crop row detection method provided by the invention, the filter function is as follows:
wherein rho is the distance between the laser radar and the straw, and theta is the azimuth angle of the laser radar during detection; gamma rayminAzimuth angle gamma corresponding to one side boundary of straw row when scanning straw row by laser radarmaxThe azimuth angle corresponding to the boundary at the other side of the straw row when the laser radar scans the straw row.
According to the straw-bundled crop row detection method provided by the invention, the gradient of each return laser point in the region of interest is determined through a gradient algorithm, and the determining of the boundaries of the straw rows based on the gradient of each return laser point specifically comprises the following steps:
determining the highest point of the straw row based on the point cloud data in the region of interest, determining the gradient of each returned laser point in the region of interest through a gradient algorithm, and determining the straw row boundaries on two sides of the highest point based on the gradient of each returned laser point.
According to the straw bundled crop row detection method provided by the invention, the gradient algorithm specifically comprises the following steps:
the gradient of any point N except the head and the tail of the N returned laser points is as follows:
the gradient of the head and the tail of the N returned laser points is as follows:
wherein the content of the first and second substances,
the gradient of the first returning laser spot,
the gradient of the returning laser spot at the end.
According to the straw bundled crop line detection method provided by the invention, the step of determining the straw line boundaries on two sides of the highest point based on the gradient of each returned laser point comprises the following steps:
setting a threshold value delta to satisfy the left side and the right side of the highest point
The points are respectively stored in an array a and an array b, and then the first point and the last point of the array a and the array b are the boundaries of the straw rows.
The invention also provides electronic equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, and is characterized in that the processor executes the program to realize the steps of the straw-bundled crop row detection method.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the straw baled crop row detection method as described above.
According to the straw bundled crop line detection method, the equipment and the storage medium, the point cloud data scanned by the laser radar is processed, and the straw lines are identified according to different returned angle and distance parameters for reference of path planning, so that support can be provided for intelligent straw bundling by applying a bundling machine.
Drawings
In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic view of the installation of a laser radar in the straw-bundled crop row detection method provided by the invention;
FIG. 2 is a flow chart of a straw-bound crop row detection method provided by the present invention;
FIG. 3 is a comparison of laser radar scan data before and after filtering;
FIG. 4 is a graph of the effect of the gradient calculation of each returned laser point in the region of interest;
FIG. 5 is a transformation diagram of a polar coordinate and a three-dimensional rectangular coordinate system of a laser radar;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Reference numerals:
1: a laser radar; 2: a tractor.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The steps of the straw-bound crop row detection method of the present invention will be described with reference to fig. 1 to 5.
The straw-bundled crop row detection method provided by the embodiment of the invention, as shown in fig. 2, comprises the following steps: scanning the straw rows through a laser radar to obtain point cloud data formed by a plurality of returned laser points, wherein the point cloud data comprises angles and distances of the returned laser points, and identifying the straw rows according to the point cloud data.
The straws which are raked into a line can be higher than the ground by a certain height, and the laser radar scans the angle and distance information of the returned laser point to form point cloud data. The straws with different heights can return to different angles and distance information, and the straw rows can be identified by analyzing the acquired point cloud data.
According to the straw bundled crop row detection method provided by the embodiment of the invention, the point cloud data obtained by scanning the laser radar is processed, and the straw rows are identified according to different returned angle and distance parameters, so that support is provided for intelligent straw bundling by using a bundling machine.
Wherein, as shown in fig. 1, the point cloud data of the laser radar scanning straw row specifically comprises: the laser radar 1 is arranged at the front end of the traveling equipment, the vertical field angle of the laser radar is-16 degrees to +15 degrees, the horizontal field angle of the laser radar is 360 degrees, and the laser beam rotates around the transmitting center of the laser radar for one circle to scan. For example, the laser radar 1 is installed at the front end of the tractor 2, and the returned angle and distance parameters can be determined after the scanned point cloud data is processed. Specifically, the laser radar can be installed at any position above the cab, above the front cover or above the front balancing weight, and the like, as long as the laser radar is not blocked, the straw rows in front of the travelling equipment can be scanned. In addition, the laser radar can be adjustably mounted on the traveling equipment through the holder or the mounting bracket, so that the angle can be adjusted by means of the holder or the mounting bracket, and the straw rows in the region of interest can obtain the best visual effect.
In an embodiment of the invention, the lidar is a 32-line three-dimensional lidar. Of course, two-dimensional lidar may also be used. The number and arrangement of the laser radars can be set according to needs, and are not particularly limited.
On the basis of any one of the above embodiments, identifying straw rows according to the point cloud data specifically comprises: extracting an interested area through a filter function, determining the gradient of each return laser point in the interested area through a gradient algorithm, and determining the boundary of the straw row based on the gradient of each return laser point.
The scanning range of the laser radar is larger than the width of the straw row, and in order to remove data outside the straw row, an interested region is extracted through a filter function before the boundary of the straw row is identified. And taking the region of interest as a data basis for subsequent analysis and calculation. In order to identify the straw row boundary, the gradient of each returned laser point in the region of interest is calculated, and the straw row boundary is determined according to the gradient.
Specifically, the filter function is:
wherein rho is the distance between the laser radar and the straw, and theta is the azimuth angle of the laser radar during detection; gamma rayminAzimuth angle gamma corresponding to one side boundary of straw row when scanning straw row by laser radarmaxThe azimuth angle corresponding to the boundary at the other side of the straw row when the laser radar scans the straw row. Gamma rayminAnd gammamaxAn angular range of the region of interest is defined, return laser points within the range being retained and return laser points outside the range being rejected. Taking data obtained in a certain experiment as an example, a comparison graph before and after filtering is shown in fig. 3, fig. 3(a) is data before filtering, and fig. 3(b) is data after filtering, and as can be seen by comparison, the filtered data is more concentrated, and unnecessary parts are removed.
Before the gradient of each returned laser point in the interested region is determined through a gradient algorithm, the highest point of the straw row is determined based on the point cloud data in the interested region, and the highest point is the point with the shortest distance measured in the interested region. Then, the gradient of each returned laser point in the region of interest is determined through a gradient algorithm, and the straw row boundaries on the two sides of the highest point are determined based on the gradients of the returned laser points.
On the basis of the above embodiment, the gradient algorithm specifically includes:
the gradient of any point N except the head and the tail of the N returned laser points is as follows:
the gradient of the head and the tail of the N returned laser points is as follows:
wherein the content of the first and second substances,
the gradient of the first returning laser spot,
the gradient of the returning laser spot at the end.
When the gradient of the returned laser point is determined, in order to avoid inaccurate gradient calculation caused by individual discrete values when one adjacent point is obtained, the front and back adjacent points are adopted for calculation when any point n except the head and tail points is obtained. The gradient of each returning laser spot in the region of interest after the gradient calculation is shown in fig. 4.
After the gradient of each returned laser point is calculated, a threshold value delta is set, and the left side and the right side of the highest point meet the requirements
The points are respectively stored in an array a and an array b, and then the first point and the last point of the array a and the array b are the boundaries of the straw rows.
Wherein the threshold value delta is used for distinguishing ground data from straw row data. Generally, the gradient of the ground data is small, the straw rows have a certain height and a certain gradient, and the ground data and the straw row data are distinguished by means of a threshold value. The part of the gradient with the absolute value larger than the threshold belongs to the straw row, and the data closest to the threshold in the data is the boundary of the straw row. And (4) orderly arranging the gradients corresponding to the returned laser points, wherein the first point and the last point of the array a and the array b are boundaries of the straw rows.
Therefore, the scanning data of the laser radar at each time is analyzed, multiple groups of point cloud data in front of the tractor can be obtained along with the advancing of the tractor, and the boundaries of the straw rows are determined according to the point cloud data, so that an analysis basis is provided for the advancing of the tractor, and the planning of a driving path is facilitated.
Because the laser radar adopts polar coordinates, and after the laser radar is converted into rectangular coordinates, the laser scanning plane still forms a certain angle with the vehicle body, so that the scanning data of the laser radar needs to be converted into three-dimensional rectangular coordinates from the polar coordinates. The three-dimensional rectangular coordinate takes the center of the laser radar as the origin of coordinates, one side of the vehicle body as the positive direction of an X axis, the driving direction in front of the vehicle body as the positive direction of a Y axis, and the upward direction perpendicular to the ground as the positive direction of a Z axis. Setting the inclination angle of the laser radar along the horizontal plane as alpha, the distance between the straw and the laser radar as rho, the included angle between the current laser beam and the YZ plane during detection as theta, and setting the coordinate point to be detected as (X, Y, Z), then
X＝ρsinθ
Y＝ρcosθsinα
Z＝ρcosθcosα
And calculating a driving path according to the converted three-dimensional rectangular coordinates so as to control the position of the vehicle body. The coordinate transformation diagram is shown in fig. 5.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 6, the electronic device may include: a processor (processor)610, a communication Interface (Communications Interface)620, a memory (memory)630 and a communication bus 640, wherein the processor 610, the communication Interface 620 and the memory 630 communicate with each other via the communication bus 640. The processor 610 may call logical commands in the memory 630 to perform the following method: scanning the straw rows through a laser radar to obtain point cloud data formed by a plurality of returned laser points, wherein the point cloud data comprises angles and distances of the returned laser points, and identifying the straw rows according to the point cloud data.
In addition, the logic commands in the memory 630 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic commands are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of commands for enabling a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Embodiments of the present invention further provide a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to perform the method provided in the foregoing embodiments when executed by a processor, and the method includes: scanning the straw rows through a laser radar to obtain point cloud data formed by a plurality of returned laser points, wherein the point cloud data comprises angles and distances of the returned laser points, and identifying the straw rows according to the point cloud data.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A straw-bound crop row detection method is characterized by comprising the following steps: scanning the straw rows through a laser radar to obtain point cloud data formed by a plurality of returned laser points, wherein the point cloud data comprises angles and distances of the returned laser points, and identifying the straw rows according to the point cloud data.
2. The straw-bundled crop row detection method according to claim 1, wherein the step of scanning the straw rows by a laser radar to obtain point cloud data formed by a plurality of returned laser points specifically comprises: the laser radar is installed at the front end of the traveling equipment, the vertical field angle of the laser radar is-16 degrees to +15 degrees, and the horizontal field angle of the laser radar is 360 degrees.
3. The straw-bundled crop row detection method according to claim 2, characterized in that the lidar is a 32-line three-dimensional lidar.
4. The straw-bundling crop row detection method of claim 1, wherein the identifying straw rows from the point cloud data specifically comprises:
extracting an interested area through a filter function, determining the gradient of each return laser point in the interested area through a gradient algorithm, and determining the boundary of the straw row based on the gradient of each return laser point.
5. The straw-bundling crop row detection method of claim 4, wherein the filter function is:
wherein rho is the distance between the laser radar and the straw, and theta is the azimuth angle of the laser radar during detection; gamma rayminAzimuth angle gamma corresponding to one side boundary of straw row when scanning straw row by laser radarmaxThe azimuth angle corresponding to the boundary at the other side of the straw row when the laser radar scans the straw row.
6. The straw-bundling crop row detection method of claim 4, wherein the determining the gradient of each return laser point in the region of interest by a gradient algorithm, the determining the boundaries of the straw rows based on the gradient of each return laser point specifically comprises:
determining the highest point of the straw row based on the point cloud data in the region of interest, determining the gradient of each returned laser point in the region of interest through a gradient algorithm, and determining the straw row boundaries on two sides of the highest point based on the gradient of each returned laser point.
7. The straw-bundled crop row detection method according to claim 6, wherein the gradient algorithm is specifically:
the gradient of any point N except the head and the tail of the N returned laser points is as follows:
the gradient of the head and the tail of the N returned laser points is as follows:
wherein the content of the first and second substances,
the gradient of the first returning laser spot,
the gradient of the returning laser spot at the end.
8. The straw-bundling crop row detection method of claim 6, wherein said determining straw row boundaries on both sides of the highest point based on a gradient of each returning laser spot comprises:
setting a threshold value delta to satisfy the left side and the right side of the highest point
Respectively store the numberAnd the group a and the group b are arranged, and the first point and the last point of the group a and the group b are the boundaries of the straw row.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the straw baled crop row detection method of any one of claims 1 to 8.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the straw baled crop row detection method according to any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110587401.1A CN113534182A (en) | 2021-05-27 | 2021-05-27 | Straw bundled crop row detection method and equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110587401.1A CN113534182A (en) | 2021-05-27 | 2021-05-27 | Straw bundled crop row detection method and equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113534182A true CN113534182A (en) | 2021-10-22 |
Family
ID=78094795
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110587401.1A Pending CN113534182A (en) | 2021-05-27 | 2021-05-27 | Straw bundled crop row detection method and equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113534182A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020189220A1 (en) * | 2001-06-16 | 2002-12-19 | Deere & Company, A Delaware Corporation | System for automatically steering a utility vehicle |
| US20180084718A1 (en) * | 2016-09-27 | 2018-03-29 | Claas Selbstfahrende Erntemaschinen Gmbh | Combine harvester having a driver assistance system |
| CN109017780A (en) * | 2018-04-12 | 2018-12-18 | 深圳市布谷鸟科技有限公司 | A kind of Vehicular intelligent driving control method |
| CN109287246A (en) * | 2018-08-24 | 2019-02-01 | 宁波市德霖机械有限公司 | Intelligent grass-removing based on laser radar map structuring |
| US20190236381A1 (en) * | 2018-01-30 | 2019-08-01 | Wipro Limited. | Method and system for detecting obstacles by autonomous vehicles in real-time |
-
2021
- 2021-05-27 CN CN202110587401.1A patent/CN113534182A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020189220A1 (en) * | 2001-06-16 | 2002-12-19 | Deere & Company, A Delaware Corporation | System for automatically steering a utility vehicle |
| US20180084718A1 (en) * | 2016-09-27 | 2018-03-29 | Claas Selbstfahrende Erntemaschinen Gmbh | Combine harvester having a driver assistance system |
| US20190236381A1 (en) * | 2018-01-30 | 2019-08-01 | Wipro Limited. | Method and system for detecting obstacles by autonomous vehicles in real-time |
| CN109017780A (en) * | 2018-04-12 | 2018-12-18 | 深圳市布谷鸟科技有限公司 | A kind of Vehicular intelligent driving control method |
| CN109287246A (en) * | 2018-08-24 | 2019-02-01 | 宁波市德霖机械有限公司 | Intelligent grass-removing based on laser radar map structuring |
Non-Patent Citations (2)
| Title |
|---|
| 孙朋朋;等: "基于3D激光雷达城市道路边界鲁棒检测算法", 浙江大学学报(工学版), no. 03, 31 December 2017 (2017-12-31) * |
| 赵腾: "基于激光扫描的联合收割机自动导航方法研究", 中国博士学位论文全文库信息科技辑, no. 12, 15 December 2017 (2017-12-15), pages 30 - 59 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109752701B (en) | Road edge detection method based on laser point cloud | |
| EP3400798B1 (en) | An agricultural system | |
| US10769845B2 (en) | System and method of virtual plant field modelling | |
| CN110363861B (en) | Laser radar point cloud-based field crop three-dimensional reconstruction method | |
| Bao et al. | Field‐based robotic phenotyping of sorghum plant architecture using stereo vision | |
| CN109215071B (en) | Intelligent rice and wheat harvester swath measuring method based on vision | |
| CN105989601B (en) | Agricultural AGV corn inter-row navigation datum line extraction method based on machine vision | |
| CN106774434B (en) | Automatic obstacle avoidance method and system applied to unmanned aerial vehicle plant protection | |
| CN111784597A (en) | Plant protection robot autonomous navigation path detection method, server and storage medium | |
| CN107220647A (en) | Crop location of the core method and system under a kind of blade crossing condition | |
| CN110969654A (en) | Corn high-throughput phenotype measurement method and device based on harvester and harvester | |
| Kang et al. | Sucker detection of grapevines for targeted spray using optical sensors | |
| CN113128576A (en) | Crop row detection method and device based on deep learning image segmentation | |
| CN113534182A (en) | Straw bundled crop row detection method and equipment and storage medium | |
| CN111815612A (en) | Red date disease and pest prediction system based on Internet of things | |
| CN116576863A (en) | Corn data acquisition robot crop inter-row navigation path identification method, computer equipment and medium | |
| CN108734054A (en) | Unobstructed citrusfruit image-recognizing method | |
| CN116797622A (en) | Corn tassel image double-threshold segmentation method based on multi-feature index | |
| CN116486130A (en) | Obstacle recognition method, device, self-mobile device and storage medium | |
| CN115984797A (en) | Lane line detection method and device and electronic equipment | |
| CN111179303B (en) | Grain harvesting robot visual navigation method based on particle filtering and application thereof | |
| CN113436336B (en) | Ground point cloud segmentation method and device and automatic driving vehicle | |
| CN115599119A (en) | Unmanned aerial vehicle keeps away barrier system | |
| Jiang et al. | Navigation path points extraction method based on color space and depth information for combine harvester | |
| Li et al. | Vision-based Navigation Line Extraction by Combining Crop Row Detection and RANSAC Algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |