CN114894092B - Agricultural implement operation breadth visual detection system and method - Google Patents
Agricultural implement operation breadth visual detection system and method Download PDFInfo
- Publication number
- CN114894092B CN114894092B CN202210545361.9A CN202210545361A CN114894092B CN 114894092 B CN114894092 B CN 114894092B CN 202210545361 A CN202210545361 A CN 202210545361A CN 114894092 B CN114894092 B CN 114894092B
- Authority
- CN
- China
- Prior art keywords
- agricultural implement
- track
- image
- width
- agricultural
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 23
- 238000001514 detection method Methods 0.000 title claims abstract description 21
- 230000000007 visual effect Effects 0.000 title claims abstract description 15
- 238000013461 design Methods 0.000 claims abstract description 19
- 238000012545 processing Methods 0.000 claims description 29
- 230000001629 suppression Effects 0.000 claims description 6
- 238000004891 communication Methods 0.000 claims description 5
- 238000012216 screening Methods 0.000 claims description 5
- 238000001914 filtration Methods 0.000 claims description 3
- 238000011179 visual inspection Methods 0.000 claims 4
- 238000004364 calculation method Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 238000003306 harvesting Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Length Measuring Devices By Optical Means (AREA)
- Image Processing (AREA)
Abstract
The invention discloses a visual detection system and a visual detection method for the operation breadth of agricultural machinery, and relates to the technical field of agriculture. The method of the invention comprises the following steps: collecting an agricultural implement operation image, and acquiring the agricultural implement design breadth according to the agricultural implement operation image; extracting an operating region track and an non-operating region track of the agricultural implement from the agricultural implement operating image; acquiring an operated edge track and a next agricultural implement operation edge track from an agricultural implement operation image; calculating the overlapping width according to the operated edge track and the next agricultural implement operation edge track; when the operated area track and the non-operated area track are overlapped, the effective operation width of the agricultural machine is the designed width of the agricultural machine minus the overlapped width; when there is no overlap between the trajectories of the worked and non-worked areas, the agricultural machine has an effective working width that is the agricultural machine design width.
Description
Technical Field
The invention relates to the field of agriculture, in particular to a visual detection system and method for the operation breadth of an agricultural implement.
Background
With the popularization of agricultural socialization services, agricultural equipment operation information monitoring becomes a new problem and a new demand. At present, the domestic standard does not have clear definition on the working width of the agricultural machinery and does not have a unified measuring method, and the design width of the agricultural machinery is commonly used for calculation; however, in the operation process of the agricultural machine, the actual operation route of the agricultural machine is complex and is not a single straight line due to the limitation of factors such as boundary curves, terrain changes, navigation errors and the like, and a missing region or an overlapping region cannot be avoided in the operation process, so that errors exist between the working breadth and the design breadth, and the real-time change of the working breadth cannot be reflected.
Aiming at the width detection requirement of the grain combine harvester, the prior art utilizes the cutting resistance of a cutter of the harvester to obtain, but the measurement accuracy is not high and the detection is easy to miss, and no effective detection device for the real-time width exists on other machines.
Disclosure of Invention
Aiming at the problems of the prior art, the invention provides a visual detection system and a visual detection method for the operation breadth of an agricultural implement, wherein:
the invention provides a visual detection method for the operation breadth of agricultural machinery, which comprises the following steps:
and acquiring an agricultural implement operation image, and acquiring the agricultural implement design breadth according to the agricultural implement operation image.
And extracting the track of the operated area and the track of the non-operated area of the agricultural implement from the agricultural implement operation image.
Acquiring an operated edge track and a next agricultural implement operation edge track from an agricultural implement operation image; and calculating the overlapping breadth according to the operated edge track and the next agricultural implement operation edge track.
When the operated area track and the non-operated area track are overlapped, the effective operation width of the agricultural machine is the designed width of the agricultural machine minus the overlapped width; when there is no overlap between the trajectories of the worked and non-worked areas, the agricultural machine has an effective working width that is the agricultural machine design width.
Further, the collecting the farm implement operation image specifically includes: and acquiring an agricultural implement operation image by using the area array camera.
Further, the extracting the track of the operated area and the track of the non-operated area of the farm tool specifically includes: and using OpenCV software to eliminate high-frequency noise on the farm tool operation image by using Gaussian blur, and using an Otsu binarization algorithm to carry out noise filtering.
Further, the acquiring the edge track of the agricultural implement operation specifically includes: and obtaining the edge track of the agricultural implement operation by calculating the gradient and gradient direction of the image, non-maximum suppression and double-threshold screening of the image subjected to Gaussian blur and binarization processing.
Further, the computing agricultural machine has an effective job width, and specifically includes:
when delta H is less than 0, the agricultural implement operation has an overlapping area, and the effective operation width is H-delta H.
When Δh=0, the farm implement operation is in a normal state at this time, and the effective operation width is H.
When delta H >0, the agricultural implement operation has a missing area, and the effective operation breadth is H.
Wherein H is the design breadth of the farm implement.
Δh is the overlap width of the agricultural implement work, Δh=l1-L2.
L1 is the operated edge track of the farm tool.
L2 is the next operation edge track of the farm tool.
The invention provides a visual detection system for the operation breadth of farm machinery, which specifically comprises the following components:
an agricultural implement body.
And the area array camera is arranged on the agricultural implement body and is used for collecting an agricultural implement operation image.
The vehicle-mounted terminal is provided with a control unit,
the method is used for acquiring the design breadth of the agricultural implement according to the agricultural implement operation image.
The method is used for extracting the track of the operated area and the track of the non-operated area of the agricultural implement from the agricultural implement operation image.
The method comprises the steps of acquiring a operated edge track and a next agricultural implement operation edge track from an agricultural implement operation image; and calculating the overlapping breadth according to the operated edge track and the next agricultural implement operation edge track.
When the operated area track and the non-operated area track are overlapped, the agricultural machine has an effective operation width which is the designed width of the agricultural machine minus the overlapped width; when there is no overlap between the trajectories of the worked and non-worked areas, the agricultural machine has an effective working width that is the agricultural machine design width.
Further, the invention provides a visual detection system for the operation breadth of farm tools, which further comprises:
the marking ruler is arranged on the agricultural implement body and used for marking the size of the agricultural implement operation image.
Further, the vehicle-mounted terminal includes:
the central processing unit is electrically connected with the area array camera and is used for receiving and processing the farm tool operation image and calculating the effective operation breadth of the farm tool;
the communication module is electrically connected with the area array camera and the central processing unit and is used for receiving and acquiring the farm tool operation image and sending the effective operation breadth to the central processing unit;
the power supply module is electrically connected with the central processing unit and is used for supplying electric energy to the central processing unit;
the storage module is electrically connected with the central processing unit and used for storing programs and instructions on the vehicle-mounted terminal;
and the display module is electrically connected with the central processing unit and used for displaying the effective operation breadth in real time by the vehicle-mounted terminal.
Compared with the prior art, the invention provides a visual detection system and a visual detection method for the operation breadth of farm machinery, which have the beneficial effects that:
according to the agricultural machinery width visual detection system and method provided by the invention, the agricultural machinery operation image is collected, the operation image is processed, the agricultural machinery operation edge track is obtained, the effective operation width of the agricultural machinery is finally calculated, and the influence of overlapping and missing areas on the calculation accuracy of the harvesting area is effectively avoided.
Drawings
FIG. 1 is a schematic flow chart of a method for measuring the width of a web;
FIG. 2 is a schematic top view of an image of an agricultural implement;
FIG. 3 is a schematic diagram of a hardware circuit connection provided by the present invention;
fig. 4 is a schematic structural view of a harvester body according to the present invention;
fig. 5 is a schematic diagram of a photographing width of a camera according to the present invention.
In the figure: 1. the system comprises an area array camera, a marking ruler, a vehicle-mounted terminal and a camera shooting angle.
Detailed Description
Embodiments of the present invention will be further described with reference to fig. 1 to 5. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
Example 1: as shown in FIG. 1, the method for visually detecting the operation breadth of the agricultural implement provided by the invention comprises the following steps: collecting an agricultural implement operation image, and acquiring the agricultural implement design breadth according to the agricultural implement operation image; extracting an operating region track and an non-operating region track of the agricultural implement from the agricultural implement operating image; acquiring an operated edge track and a next agricultural implement operation edge track from an agricultural implement operation image; calculating the overlapping width according to the operated edge track and the next agricultural implement operation edge track; when the operated area track and the non-operated area track are overlapped, the effective operation width of the agricultural machine is the designed width of the agricultural machine minus the overlapped width; when there is no overlap between the trajectories of the worked and non-worked areas, the agricultural machine has an effective working width that is the agricultural machine design width.
The method for collecting the farm tool operation image specifically comprises the following steps: and acquiring an agricultural implement operation image by using the area array camera.
Extracting the track of the operated area and the track of the non-operated area of the farm tool, which comprises the following steps: and using OpenCV software to eliminate high-frequency noise on the farm tool operation image by using Gaussian blur, and using an Otsu binarization algorithm to carry out noise filtering.
Gaussian blur uses a Gaussian filter kernel to calculate the Gaussian weighted average of all pixels under the 3*3 kernel region and replaces the center element to smooth the image, the Gaussian filter kernel passes the Gaussian formula The Otsu binarization, which is generated by dividing the image into foreground and background portions using a threshold, maximizes the variance between foreground and background, avoids the need to manually determine a threshold and determines the best global threshold from the image histogram.
The method for acquiring the operation edge track of the agricultural implement specifically comprises the following steps: and obtaining the edge track of the agricultural implement operation by calculating the gradient and gradient direction of the image, non-maximum suppression and double-threshold screening of the image subjected to Gaussian blur and binarization processing.
Calculating gradients and gradient directions of the image in horizontal and vertical directions by utilizing a Sobel operator so as to realize enhancement of the image edge; after obtaining the gradient magnitude and direction, non-maximum suppression will be performed on the image full scan, checking if the pixel is its local maximum near the gradient direction, if so, taking into account for the next stage, otherwise suppressing it (set to zero); two thresholds minVal and maxVal are determined for dual threshold screening, any edges with intensity gradients greater than maxVal must be edges, while those less than minVal must be non-edges, so they are discarded. Objects between these two thresholds are classified as edges or non-edges according to their connectivity, if they are connected to "edge" pixels, they are considered as part of the edge, otherwise they will also be discarded.
The method for calculating the effective operation breadth of the agricultural machine specifically comprises the following steps: when delta H is less than 0, the agricultural implement operation has an overlapping area, and the effective operation breadth is H-delta H; when Δh=0, the farm implement operation is in a normal state, and the effective operation width is H; when delta H is more than 0, the agricultural implement operation has a missing area, and the effective operation breadth is H; wherein H is the design breadth of the farm machinery; Δh is the overlapping width of the agricultural implement operation, Δh=l1-L2; l1 is the operated edge track of the farm machinery; l2 is the next operation edge track of the farm tool.
In this embodiment, an area array camera is used to collect an image of an agricultural implement, and the type of the relevant equipment is selected according to the following conditions: an image acquisition system: selecting a corresponding lens of the camera, and selecting a focal length: field of view range/CCD size = working distance/focal length; a pixel: breadth/detection accuracy; resolution ratio: field length/detection accuracy field width/detection accuracy; calibrating a camera: selecting the most suitable calibration calculation method; selecting a high-precision calibration plate, and shooting from a plurality of angles to obtain images of the calibration plate; and then, according to the selected calibration calculation method, the internal parameters and the external parameters of the camera are obtained, and the calibration of the camera is completed.
Fig. 2 is a schematic top view of image acquisition provided by the present invention, as shown in fig. 2, the area camera 1 acquires an image of a working track through the marking ruler 2, so as to clearly obtain tracks of a working area and a next working area.
Fig. 5 is a schematic view of the shooting width of the camera according to the present invention, and as shown in fig. 5, the agricultural implement working image can be obtained in a specific size by the marking ruler 2, thereby obtaining an effective working width.
Example 2: as shown in FIG. 1, the invention provides a visual detection system for the operation breadth of an agricultural implement, which specifically comprises: an agricultural implement body; the area array camera 1 is arranged on the agricultural implement body and is used for collecting an agricultural implement operation image; the vehicle-mounted terminal 3 is used for acquiring the design breadth of the farm tool according to the farm tool operation image; the method comprises the steps of extracting an operating region track and an unworked region track of an agricultural implement from an agricultural implement operating image; the method comprises the steps of acquiring a operated edge track and a next agricultural implement operation edge track from an agricultural implement operation image; calculating the overlapping width according to the operated edge track and the next agricultural implement operation edge track; when the operated area track and the non-operated area track are overlapped, the agricultural machine has an effective operation width which is the designed width of the agricultural machine minus the overlapped width; when there is no overlap between the trajectories of the worked and non-worked areas, the agricultural machine has an effective working width that is the agricultural machine design width. And the marking ruler 2 is arranged on the agricultural implement body and is used for marking the size of the agricultural implement operation image.
The in-vehicle terminal 3 further includes: the central processing unit is electrically connected with the area array camera 1 and is used for receiving and processing the farm tool operation image and calculating the effective operation breadth of the farm tool; the communication module is electrically connected with the area array camera 1 and the central processing unit and is used for receiving and acquiring an agricultural implement operation image and transmitting the effective operation breadth to the central processing unit; the power supply module is electrically connected with the central processing unit and is used for supplying electric energy to the central processing unit; the storage module is electrically connected with the central processing unit and is used for storing programs and instructions on the vehicle-mounted terminal 3; and the display module is electrically connected with the central processing unit and is used for displaying the effective operation breadth of the vehicle-mounted terminal 3 in real time.
Fig. 3 is a schematic diagram of connection of a hardware circuit according to the present invention, where, as shown in fig. 3, the hardware circuit includes: the device comprises an area array camera, a power supply module, a communication module, a display module and a central processing unit;
the area array camera provides power supply, I/O and serial port functions for the camera through a 6-pin Hirose interface, and the camera is set through OPTO_ IN, GPIO, OPTO _OUT and OPTO_GND; the power supply module is connected with the DC_PWR and the GND interface to supply power for the area array camera; the communication module performs data transmission through the gigabit network port; the display module uses a positive atom TFTLCD screen, and the LCD adopts 16-bit 8080 parallel ports; the central processing unit adopts STM32F103.
Fig. 4 is a schematic structural diagram of the agricultural implement body provided by the invention, as shown in fig. 4, a vehicle-mounted terminal 3 is placed in a cab, an area-array camera 1 is installed above a working part of the agricultural implement, and a marking ruler 2 is installed in parallel with the area-array camera so as to ensure that the size of an operation image of the agricultural implement can be accurately reflected. The camera shooting angle 4 is required to ensure that both the operated area and the non-operated area can be shot and are clear and distortion-free.
The above embodiments are merely preferred embodiments of the present invention, the protection scope of the present invention is not limited thereto, and any simple changes or equivalent substitutions of technical solutions that can be obviously obtained by those skilled in the art within the technical scope of the present invention disclosed in the present invention belong to the protection scope of the present invention.
Claims (5)
1. The visual detection method for the operation breadth of the farm machinery is characterized by comprising the following steps of:
collecting an agricultural implement operation image, and acquiring the agricultural implement design breadth according to the agricultural implement operation image;
extracting an operating region track and an non-operating region track of the agricultural implement from the agricultural implement operating image;
acquiring an operated edge track and a next agricultural implement operation edge track from an agricultural implement operation image; calculating the overlapping width according to the operated edge track and the next agricultural implement operation edge track;
when the operated area track and the non-operated area track are overlapped, the effective operation width of the agricultural machine is the designed width of the agricultural machine minus the overlapped width; when the tracks of the operated area and the non-operated area are not overlapped, the agricultural machine has an effective operation width which is the design width of the agricultural machine;
the method for calculating the effective operation width of the agricultural machine specifically comprises the following steps:
when delta H is less than 0, an overlapping area exists in the operation of the agricultural machinery, and the effective operation breadth is H-delta H;
when Δh=0, the farm implement operation is in a normal state, and the effective operation width is H;
when delta H is more than 0, the agricultural implement operation has a missing area, and the effective operation breadth is H;
wherein H is the design breadth of the farm machinery;
Δh is the overlapping width of the agricultural implement operation, Δh=l1-L2;
l1 is the operated edge track of the farm machinery;
l2 is the next operation edge track of the agricultural implement;
the extracting the track of the operated area and the track of the non-operated area of the farm tool from the farm tool operation image specifically comprises the following steps:
using OpenCV software to eliminate high-frequency noise on an agricultural implement operation image by using Gaussian blur, and using an Otsu binarization algorithm to carry out noise filtering;
the Gaussian blur uses Gaussian filter kernels to calculate Gaussian weighted average values of all pixels under 3*3 kernel area and replaces center elements to smooth the image, and the Gaussian filter kernels are generated through Gaussian formulas;
dividing an image into a foreground part and a background part by using an Otsu binarization threshold value, maximizing the variance between the foreground and the background, and determining an optimal global threshold value from an image histogram;
the step of acquiring the operated edge track and the next agricultural implement operation edge track specifically comprises the following steps:
obtaining an edge track of agricultural implement operation on the image subjected to Gaussian blur and binarization processing by calculating the gradient and gradient direction of the image, non-maximum suppression and double-threshold screening;
calculating gradients and gradient directions of the image in horizontal and vertical directions by utilizing a Sobel operator so as to realize enhancement of the image edge;
after the gradient size and direction are obtained, performing non-maximum suppression on the image overall scanning, checking whether the pixel is a local maximum near the pixel in the gradient direction, if so, using the pixel in the next stage, otherwise, performing suppression and setting the pixel to zero;
determining two thresholds minVal and maxVal for double-threshold screening, wherein any edges with gradient greater than maxVal are necessarily edges, and those edges less than minVal are necessarily non-edges, so that the edges are discarded; objects between these two thresholds are classified as edges or non-edges according to their connectivity, if they are connected to "edge" pixels, they are considered as part of the edge, otherwise they will also be discarded.
2. The method for visually inspecting the working width of an agricultural implement according to claim 1, wherein the step of collecting the working image of the agricultural implement comprises:
and acquiring an agricultural implement operation image by using the area array camera.
3. A visual inspection system for the working width of an agricultural implement using the visual inspection method for the working width of an agricultural implement according to any one of claims 1 to 2, comprising:
an agricultural implement body;
the area array camera (1) is arranged on the agricultural implement body and is used for collecting an agricultural implement operation image;
the vehicle-mounted terminal (3) is used for acquiring the design breadth of the farm tool according to the farm tool operation image;
the method comprises the steps of extracting an operating region track and an unworked region track of an agricultural implement from an agricultural implement operating image;
the method comprises the steps of acquiring a operated edge track and a next agricultural implement operation edge track from an agricultural implement operation image; calculating the overlapping width according to the operated edge track and the next agricultural implement operation edge track;
when the operated area track and the non-operated area track are overlapped, the agricultural machine has an effective operation width which is the designed width of the agricultural machine minus the overlapped width; when there is no overlap between the trajectories of the worked and non-worked areas, the agricultural machine has an effective working width that is the agricultural machine design width.
4. A visual inspection system for the working width of an agricultural implement as recited in claim 3, further comprising:
and the marking ruler (2) is arranged on the agricultural implement body and is used for marking the size of the agricultural implement operation image.
5. A visual inspection system for the working width of agricultural implements as claimed in claim 3, characterized in that said vehicle-mounted terminal (3) comprises:
the central processing unit is electrically connected with the area array camera (1) and is used for receiving and processing the farm tool operation image and calculating the effective operation breadth of the farm tool;
the communication module is electrically connected with the area array camera (1) and the central processing unit and is used for receiving and acquiring the farm tool operation image and sending the effective operation breadth to the central processing unit;
the power supply module is electrically connected with the central processing unit and is used for supplying electric energy to the central processing unit;
the storage module is electrically connected with the central processing unit and is used for storing programs and instructions on the vehicle-mounted terminal (3);
and the display module is electrically connected with the central processing unit and is used for displaying the effective operation breadth in real time by the vehicle-mounted terminal (3).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210545361.9A CN114894092B (en) | 2022-05-19 | 2022-05-19 | Agricultural implement operation breadth visual detection system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210545361.9A CN114894092B (en) | 2022-05-19 | 2022-05-19 | Agricultural implement operation breadth visual detection system and method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114894092A CN114894092A (en) | 2022-08-12 |
| CN114894092B true CN114894092B (en) | 2024-02-27 |
Family
ID=82723415
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210545361.9A Active CN114894092B (en) | 2022-05-19 | 2022-05-19 | Agricultural implement operation breadth visual detection system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114894092B (en) |
Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH01173111A (en) * | 1987-12-26 | 1989-07-07 | Kubota Ltd | Image pickup type boundary detecting device for self-traveling working vehicle |
| JPH09271016A (en) * | 1996-03-29 | 1997-10-14 | Seibutsukei Tokutei Sangyo Gijutsu Kenkyu Suishin Kiko | Remote operation device for working vehicle |
| JPH10307627A (en) * | 1997-05-07 | 1998-11-17 | Fuji Heavy Ind Ltd | Working border detecting device and copy travel controller for autonomous traveling work vehicle |
| CN1664497A (en) * | 2005-03-08 | 2005-09-07 | 河北农业大学 | Method and device for automatic on-line measurement of real-time actual grain cutting width |
| EP2221702A2 (en) * | 2009-02-20 | 2010-08-25 | CLAAS Selbstfahrende Erntemaschinen GmbH | Method for creating reference lanes for agricultural vehicles |
| JP2015164001A (en) * | 2014-02-28 | 2015-09-10 | 株式会社丸山製作所 | guide device |
| CN105651311A (en) * | 2016-04-01 | 2016-06-08 | 黑龙江八农垦大学 | Method for measuring satellite navigation autopilot accuracy of agricultural machinery operation |
| CN106202931A (en) * | 2016-07-08 | 2016-12-07 | 浙江联辉智能科技有限公司 | A kind of agricultural machinery working area algorithm |
| JP2017117256A (en) * | 2015-12-25 | 2017-06-29 | 井関農機株式会社 | Working vehicles fuel management system |
| JP2018099113A (en) * | 2016-12-19 | 2018-06-28 | 株式会社クボタ | Travel route determination device |
| JP2019088209A (en) * | 2017-11-13 | 2019-06-13 | 小橋工業株式会社 | Work route providing method |
| CN110132215A (en) * | 2019-04-29 | 2019-08-16 | 丰疆智能科技研究院(常州)有限公司 | Agricultural machinery working breadth automatic obtaining method and agricultural machinery working area acquisition methods |
| CN110597288A (en) * | 2019-09-29 | 2019-12-20 | 陈�峰 | Algorithm based on agricultural machinery field unmanned operation path planning |
| CN110703777A (en) * | 2019-09-25 | 2020-01-17 | 江苏大学 | Combined navigation method and system of combine harvester based on Beidou and vision |
| CN210551582U (en) * | 2019-07-02 | 2020-05-19 | 南通路捷机械有限公司 | Width real-time detection display device for film cutting machine |
| JP2020176460A (en) * | 2019-04-19 | 2020-10-29 | コベルコ建機株式会社 | Surroundings monitoring device for work machine |
| CN114009207A (en) * | 2021-10-29 | 2022-02-08 | 北京市农林科学院智能装备技术研究中心 | Harvesting area measuring method and harvester |
| CN216482826U (en) * | 2021-10-28 | 2022-05-10 | 深圳市博盛新材料有限公司 | Online film width detection device |
-
2022
- 2022-05-19 CN CN202210545361.9A patent/CN114894092B/en active Active
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH01173111A (en) * | 1987-12-26 | 1989-07-07 | Kubota Ltd | Image pickup type boundary detecting device for self-traveling working vehicle |
| JPH09271016A (en) * | 1996-03-29 | 1997-10-14 | Seibutsukei Tokutei Sangyo Gijutsu Kenkyu Suishin Kiko | Remote operation device for working vehicle |
| JPH10307627A (en) * | 1997-05-07 | 1998-11-17 | Fuji Heavy Ind Ltd | Working border detecting device and copy travel controller for autonomous traveling work vehicle |
| CN1664497A (en) * | 2005-03-08 | 2005-09-07 | 河北农业大学 | Method and device for automatic on-line measurement of real-time actual grain cutting width |
| EP2221702A2 (en) * | 2009-02-20 | 2010-08-25 | CLAAS Selbstfahrende Erntemaschinen GmbH | Method for creating reference lanes for agricultural vehicles |
| JP2015164001A (en) * | 2014-02-28 | 2015-09-10 | 株式会社丸山製作所 | guide device |
| JP2017117256A (en) * | 2015-12-25 | 2017-06-29 | 井関農機株式会社 | Working vehicles fuel management system |
| CN105651311A (en) * | 2016-04-01 | 2016-06-08 | 黑龙江八农垦大学 | Method for measuring satellite navigation autopilot accuracy of agricultural machinery operation |
| CN106202931A (en) * | 2016-07-08 | 2016-12-07 | 浙江联辉智能科技有限公司 | A kind of agricultural machinery working area algorithm |
| JP2018099113A (en) * | 2016-12-19 | 2018-06-28 | 株式会社クボタ | Travel route determination device |
| JP2019088209A (en) * | 2017-11-13 | 2019-06-13 | 小橋工業株式会社 | Work route providing method |
| JP2020176460A (en) * | 2019-04-19 | 2020-10-29 | コベルコ建機株式会社 | Surroundings monitoring device for work machine |
| CN110132215A (en) * | 2019-04-29 | 2019-08-16 | 丰疆智能科技研究院(常州)有限公司 | Agricultural machinery working breadth automatic obtaining method and agricultural machinery working area acquisition methods |
| CN210551582U (en) * | 2019-07-02 | 2020-05-19 | 南通路捷机械有限公司 | Width real-time detection display device for film cutting machine |
| CN110703777A (en) * | 2019-09-25 | 2020-01-17 | 江苏大学 | Combined navigation method and system of combine harvester based on Beidou and vision |
| CN110597288A (en) * | 2019-09-29 | 2019-12-20 | 陈�峰 | Algorithm based on agricultural machinery field unmanned operation path planning |
| CN216482826U (en) * | 2021-10-28 | 2022-05-10 | 深圳市博盛新材料有限公司 | Online film width detection device |
| CN114009207A (en) * | 2021-10-29 | 2022-02-08 | 北京市农林科学院智能装备技术研究中心 | Harvesting area measuring method and harvester |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114894092A (en) | 2022-08-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102175700B (en) | Method for detecting welding seam segmentation and defects of digital X-ray images | |
| CN109447945B (en) | Quick counting method for basic wheat seedlings based on machine vision and graphic processing | |
| CN106824806B (en) | The detection method of low module plastic gear based on machine vision | |
| CN111353993B (en) | Thread angle measuring method based on machine vision | |
| CN108537772B (en) | Visual detection method for printing defects of chip resistor positive conductor | |
| CN103617611B (en) | A kind of automatic threshold segmentation spot center and size detecting method | |
| CN112686920A (en) | Visual measurement method and system for geometric dimension parameters of circular part | |
| CN111257329A (en) | Smartphone camera defect detection method and detection system | |
| CN113689401A (en) | Method and device for detecting diameter of crystal bar of czochralski silicon single crystal furnace | |
| CN116468687A (en) | Scratch defect detection method and device, storage medium and electronic equipment | |
| CN108871185B (en) | Method, device and equipment for detecting parts and computer readable storage medium | |
| CN115880296B (en) | Machine vision-based prefabricated part quality detection method and device | |
| CN109636793A (en) | The detection system and its detection method of display | |
| CN110455201B (en) | Stalk crop height measuring method based on machine vision | |
| CN114894092B (en) | Agricultural implement operation breadth visual detection system and method | |
| CN111489389A (en) | Light spot center detection method | |
| CN113484867B (en) | Method for detecting density of fish shoal in closed space based on imaging sonar | |
| CN110956624A (en) | Image definition evaluation method for three-dimensional object | |
| CN106767467A (en) | A kind of indexable insert tip, throw away tip blunt circle detecting system based on machine vision | |
| CN116152158A (en) | Weld joint detection method and device, electronic equipment and storage medium | |
| CN115731388A (en) | Integrated rapid detection method and system for road health state | |
| CN112330667B (en) | Morphology-based laser stripe center line extraction method | |
| CN113223034A (en) | Road edge detection and tracking method | |
| CN102081792A (en) | Multi-scale red jujube image crack edge detection method based on wavelet transformation | |
| CN117329970B (en) | Unmanned rotary cultivator broken soil quality detection method |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |