CN112487936A - Corn field management robot based on machine vision technology - Google Patents
Corn field management robot based on machine vision technology Download PDFInfo
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- CN112487936A CN112487936A CN202011348977.4A CN202011348977A CN112487936A CN 112487936 A CN112487936 A CN 112487936A CN 202011348977 A CN202011348977 A CN 202011348977A CN 112487936 A CN112487936 A CN 112487936A
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- 235000002017 Zea mays subsp mays Nutrition 0.000 title claims abstract description 31
- 240000008042 Zea mays Species 0.000 title claims abstract description 30
- 235000005824 Zea mays ssp. parviglumis Nutrition 0.000 title claims abstract description 30
- 235000005822 corn Nutrition 0.000 title claims abstract description 30
- 238000005516 engineering process Methods 0.000 title claims abstract description 17
- 241000196324 Embryophyta Species 0.000 claims abstract description 21
- 230000035784 germination Effects 0.000 claims abstract description 16
- 238000000034 method Methods 0.000 claims abstract description 15
- 208000003643 Callosities Diseases 0.000 claims abstract description 6
- 206010020649 Hyperkeratosis Diseases 0.000 claims abstract description 6
- 238000010586 diagram Methods 0.000 claims abstract description 6
- 230000002159 abnormal effect Effects 0.000 claims abstract description 4
- 244000038559 crop plants Species 0.000 claims abstract description 4
- 238000005457 optimization Methods 0.000 claims abstract description 4
- 238000007781 pre-processing Methods 0.000 claims abstract description 4
- 238000009331 sowing Methods 0.000 claims abstract description 4
- 238000011161 development Methods 0.000 claims description 13
- 230000018109 developmental process Effects 0.000 claims description 13
- 238000013473 artificial intelligence Methods 0.000 claims description 12
- 238000012545 processing Methods 0.000 claims description 6
- 238000012935 Averaging Methods 0.000 claims description 3
- 238000007726 management method Methods 0.000 description 7
- 238000004519 manufacturing process Methods 0.000 description 4
- 238000001514 detection method Methods 0.000 description 3
- 238000012271 agricultural production Methods 0.000 description 2
- 241000282887 Suidae Species 0.000 description 1
- 241000482268 Zea mays subsp. mays Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000002950 deficient Effects 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 238000003306 harvesting Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J11/00—Manipulators not otherwise provided for
- B25J11/008—Manipulators for service tasks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
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Abstract
The invention relates to a corn field management robot based on a machine vision technology, which comprises a camera and a processor, wherein the camera is used for automatically performing vertical counting of corns and determining plant spacing, the processor is used for determining abnormal germination rate and performing space optimization, the robot identifies the germination state of each sowing position and reports a germination diagram, and when a non-germination position is determined, the robot places a seed; after each row of corns is scanned, the robot calculates and reports the plant population density, and displays a result report through a display, and the working process of the robot comprises the steps of obtaining clear images of crop plants, preprocessing the images of plant stalks and accurately identifying and capturing the images of the plant stalks. The invention provides a corn field management robot based on a machine vision technology, which realizes accurate identification of stalks and leaves.
Description
Technical Field
The invention relates to the technical field of corn field management equipment, in particular to a corn field management robot based on a machine vision technology.
Background
The development of information technology greatly changes the traditional corn agricultural operation mode, so that the corn production gradually develops from a rough type to a precise type, the whole process of the corn production can be controlled like industrial production, and the quality of the corn production is greatly improved. The current situation in rural areas of China is that the labor force capable of being dried is seriously deficient, the dependence on the labor force can be reduced to the maximum extent by agricultural production based on information technology, and the informatization of agricultural equipment is a main means for solving the shortage of agricultural labor force in China.
During the production of corn operations, no matter planting, field management and harvesting, a basic problem exists, namely the growth condition of stalk leaves of corn needs to be captured. The stalks and plants are observed by naked eyes during manual operation, the positioning of the stalks is most accurate, but the efficiency is low; the positions of the stalks and the leaves are determined by the standard operation range and the standardized planting during the mechanized operation, the efficiency is high, but the identification of the stalks and the leaves is almost zero; the identification of the stalk leaves based on the image processing technology integrates the above dual advantages of human eye identification and mechanized operation. Therefore, the identification of corn stalk leaves is an urgent problem to be solved in modern agricultural production.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a corn field management robot based on a machine vision technology, which can realize accurate identification of stalks and leaves.
The invention is realized by the following technical scheme:
a corn field management robot based on a machine vision technology comprises a camera and a processor, wherein the camera is used for automatically performing corn upright counting and determining plant spacing, and the processor is used for determining abnormal germination rate and performing space optimization;
the robot identifies the germination state of each sowing position and reports a germination diagram, and when a non-germination position is determined, the robot places a seed; after each row of corns is scanned, the robot calculates and reports the plant population density, and a result report is displayed through a display;
the robot has the working procedures of acquiring clear images of crop plants, preprocessing the images of the plant stalks and accurately identifying and capturing the images of the plant stalks.
In order to further implement the present invention, the following technical solutions may be preferably selected:
preferably, the processor comprises a JETSON NANO AI artificial intelligence development board.
Preferably, the JETSON NANO AI artificial intelligence development board comprises a selection module and an adjustment module.
Preferably, the workflow of the selection module of the JETSON NANO AI artificial intelligence development board comprises the following steps:
a. selecting a frame of image for reading;
b. judging whether the definition of the image meets the requirement or not, and repeating the step a and the step b when the definition of the image does not meet the requirement;
c. and d, selecting the image with the definition meeting the requirement in the step b, and finishing the image selection.
Preferably, the workflow of the adjusting module of the JETSON NANO AI artificial intelligence development board includes the following steps:
A. acquiring an image meeting the requirements;
B. b, graying the image collected in the step A by using a weighting method;
C. b, carrying out noise reduction on the grayed image in the step B by using an averaging method;
D. judging whether the definition of the denoised image in the step C meets the processing requirement or not, and repeating the step C and the step D on the image which does not meet the requirement;
E. and D, performing gray level enhancement on the image with the definition meeting the requirement in the step D by using a self-adaptive method to finish image adjustment.
Through the technical scheme, the invention has the beneficial effects that:
the system saves manpower, material resources and financial resources for scientific research workers to weigh the weight of the pigs, and can complete field corn detection without human participation. Increasing the frequency of detection also reduces the cost of the measurement.
The system greatly avoids data confusion and errors caused by factors such as similar characteristics of corns and the like due to a digital image processing technology, and also avoids inevitable forgetting and mistaking caused by accidental errors of workers and artificial interference on experimental results.
The system has the advantages that the data storage, processing and forwarding are all carried out at the cloud end, the experimental data can be monitored at any time and any place, and the precious time is not wasted on the running distance between the field and the laboratory.
Drawings
FIG. 1 is a system flow diagram of a culling module of the present invention.
FIG. 2 is a system flow diagram of the adjustment module of the present invention.
Detailed Description
In the description of the present invention, it should also be noted that, unless otherwise explicitly specified or limited, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly and may, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
Example 1:
as shown in fig. 1 and 2, a corn field management robot based on a machine vision technology comprises a camera and a processor, wherein the camera is used for automatically performing corn upright counting and determining plant spacing, and the processor is used for determining abnormal germination rate and performing space optimization;
the robot identifies the germination state of each sowing position and reports a germination diagram, and when a non-germination position is determined, the robot places a seed; after each row of corns is scanned, the robot calculates and reports the plant population density, and a result report is displayed through a display;
the robot has the working procedures of obtaining clear images of crop plants, preprocessing the images of the plant stalks and accurately identifying and capturing the images of the plant stalks.
The processor includes a JETSON NANO AI artificial intelligence development board.
The JETSON NANO AI artificial intelligence development board comprises a selection module and an adjustment module.
The workflow of the selection module of the JETSON NANO AI artificial intelligence development board comprises the following steps:
a. selecting a frame of image for reading;
b. judging whether the definition of the image meets the requirement or not, and repeating the step a and the step b when the definition of the image does not meet the requirement;
c. and d, selecting the image with the definition meeting the requirement in the step b, and finishing the image selection.
The workflow of the adjusting module of the JETSON NANO AI artificial intelligence development board comprises the following steps:
A. acquiring an image meeting the requirements;
B. b, graying the image collected in the step A by using a weighting method;
C. b, carrying out noise reduction on the grayed image in the step B by using an averaging method;
D. judging whether the definition of the denoised image in the step C meets the processing requirement or not, and repeating the step C and the step D on the image which does not meet the requirement;
E. and D, performing gray level enhancement on the image with the definition meeting the requirement in the step D by using a self-adaptive method to finish image adjustment.
The straight line characteristics of the plant stems are greatly different from other parts, the straight line characteristics of the plant stems are very reliable, the straight line characteristics of the plant stems have two characteristics of good image characteristics, and JETSON NANO detection has incomparable advantages compared with other methods, so that the JETSON NANO is selected to extract the plant stem characteristics.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments or portions thereof without departing from the spirit and scope of the invention.
Claims (5)
1. A corn field management robot based on a machine vision technology comprises a camera and a processor, and is characterized in that the camera is used for automatically performing corn upright counting and determining plant spacing, and the processor is used for determining abnormal germination rate and performing space optimization;
the robot identifies the germination state of each sowing position and reports a germination diagram, and when a non-germination position is determined, the robot places a seed; after each row of corns is scanned, the robot calculates and reports the plant population density, and a result report is displayed through a display;
the robot has the working procedures of acquiring clear images of crop plants, preprocessing the images of the plant stalks and accurately identifying and capturing the images of the plant stalks.
2. A corn field management robot based on machine vision technology as claimed in claim 1, wherein said processor comprises a JETSON NANO AI artificial intelligence development board.
3. The corn field management robot based on machine vision technology as claimed in claim 1, wherein the JETSON NANO AI artificial intelligence development board comprises a selection module and an adjustment module.
4. The corn field management robot based on the machine vision technology as claimed in claim 3, wherein the workflow of the selection module of the JETSON NANO AI artificial intelligence development board comprises the following steps:
a. selecting a frame of image for reading;
b. judging whether the definition of the image meets the requirement or not, and repeating the step a and the step b when the definition of the image does not meet the requirement;
c. and d, selecting the image with the definition meeting the requirement in the step b, and finishing the image selection.
5. A corn field management robot based on machine vision technology as claimed in claim 3, characterized in that the workflow of the adjusting module of the JETSON NANO AI artificial intelligence development board comprises the following steps:
A. acquiring an image meeting the requirements;
B. b, graying the image collected in the step A by using a weighting method;
C. b, carrying out noise reduction on the grayed image in the step B by using an averaging method;
D. judging whether the definition of the denoised image in the step C meets the processing requirement or not, and repeating the step C and the step D on the image which does not meet the requirement;
E. and D, performing gray level enhancement on the image with the definition meeting the requirement in the step D by using a self-adaptive method to finish image adjustment.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202011348977.4A CN112487936A (en) | 2020-11-26 | 2020-11-26 | Corn field management robot based on machine vision technology |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202011348977.4A CN112487936A (en) | 2020-11-26 | 2020-11-26 | Corn field management robot based on machine vision technology |
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| CN112487936A true CN112487936A (en) | 2021-03-12 |
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101750051A (en) * | 2010-01-04 | 2010-06-23 | 中国农业大学 | Visual navigation based multi-crop row detection method |
| CN105631884A (en) * | 2016-01-06 | 2016-06-01 | 上海交通大学 | Crops spike number field active measurement device and method |
| CN106231890A (en) * | 2014-04-14 | 2016-12-14 | 精密种植有限责任公司 | Crop group of hill optimizes system, method and apparatus |
| CN108465649A (en) * | 2018-07-26 | 2018-08-31 | 长沙荣业软件有限公司 | Artificial intelligence corn quality inspection robot and quality detecting method |
| CN108476676A (en) * | 2018-04-18 | 2018-09-04 | 华南农业大学 | Field intelligent seeder device people and type of seeding |
| CN108848713A (en) * | 2018-06-19 | 2018-11-23 | 安阳师范学院 | A kind of automatic thinning for milpa is filled the gaps with seedlings machine |
| CN110309933A (en) * | 2018-03-23 | 2019-10-08 | 广州极飞科技有限公司 | Plant plants data measuring method, work route method and device for planning, system |
| CN111578837A (en) * | 2020-04-30 | 2020-08-25 | 北京农业智能装备技术研究中心 | Plant shape visual tracking measurement method for agricultural robot operation |
-
2020
- 2020-11-26 CN CN202011348977.4A patent/CN112487936A/en active Pending
Patent Citations (8)
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|---|---|---|---|---|
| CN101750051A (en) * | 2010-01-04 | 2010-06-23 | 中国农业大学 | Visual navigation based multi-crop row detection method |
| CN106231890A (en) * | 2014-04-14 | 2016-12-14 | 精密种植有限责任公司 | Crop group of hill optimizes system, method and apparatus |
| CN105631884A (en) * | 2016-01-06 | 2016-06-01 | 上海交通大学 | Crops spike number field active measurement device and method |
| CN110309933A (en) * | 2018-03-23 | 2019-10-08 | 广州极飞科技有限公司 | Plant plants data measuring method, work route method and device for planning, system |
| CN108476676A (en) * | 2018-04-18 | 2018-09-04 | 华南农业大学 | Field intelligent seeder device people and type of seeding |
| CN108848713A (en) * | 2018-06-19 | 2018-11-23 | 安阳师范学院 | A kind of automatic thinning for milpa is filled the gaps with seedlings machine |
| CN108465649A (en) * | 2018-07-26 | 2018-08-31 | 长沙荣业软件有限公司 | Artificial intelligence corn quality inspection robot and quality detecting method |
| CN111578837A (en) * | 2020-04-30 | 2020-08-25 | 北京农业智能装备技术研究中心 | Plant shape visual tracking measurement method for agricultural robot operation |
Non-Patent Citations (1)
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Application publication date: 20210312 |