CN109886259A - A kind of tomato disease based on computer vision identification method for early warning and device - Google Patents
A kind of tomato disease based on computer vision identification method for early warning and device Download PDFInfo
- Publication number
- CN109886259A CN109886259A CN201910132111.0A CN201910132111A CN109886259A CN 109886259 A CN109886259 A CN 109886259A CN 201910132111 A CN201910132111 A CN 201910132111A CN 109886259 A CN109886259 A CN 109886259A
- Authority
- CN
- China
- Prior art keywords
- target
- tomato
- image
- disease
- panoramic picture
- 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
Landscapes
- Image Processing (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a kind of tomato diseases based on computer vision to identify method for early warning, is related to agriculture field, and the mode working efficiency mainly for solving the problems, such as traditional artificial identification tomato disease is low and can not precise positioning；Method includes the following steps: obtaining the identification information of target tomato, the identification information includes position parameter data and parameter information growth cycle；The image for acquiring target tomato, obtains target image；Panoramic picture coordinate system is established according to the position parameter data of multiple target tomatoes；The target image for integrating multiple target tomatoes generates target panoramic picture by reference system of the panoramic picture coordinate system；The target panoramic picture is analyzed to obtain defect information.The present invention is based on computer vision techniques to identify tomato disease, can effectively improve working efficiency, while the accurate positionin by disease tomato in planting area may be implemented, and avoids impacting post-processing.
Description
Technical field
The present invention relates to agriculture field, specifically a kind of tomato disease identification method for early warning based on computer vision and dress
It sets.
Background technique
Tomato, i.e. tomato, are a kind of annual or herbaceos perennials of tubular flower mesh, Solanaceae, tomato genus, and body is high
0.6-2 meters, the raw cement glandular hairs of entirety have overpowering odor, stem easily lodges, and leaf pinnate compound leaf or pinniform drastic crack, inflorescence always obstruct long 2-5
Centimetre, normal 3-7 flower, calyx spoke shape, corolla spoke shape, berry oblate spheroid shape or close spherical, meat and many juice, seed yellow, flower
Fruiting period summer and autumn.
All crops all can inevitably encounter disease during growth, and tomato is no exception.At present for tomato
Disease recognition is carried out by the way of artificial mostly, and efficiency is more low, while being directed to the tomato of greenhouse gardening, plantation
Area is big, and when manual identified is easy to misremember or lose the position by disease tomato, to influence subsequent processing.
Summary of the invention
The purpose of the present invention is to provide a kind of tomato diseases based on computer vision to identify method for early warning and device, with
It solves the above problems.
To achieve the above object, the invention provides the following technical scheme:
A kind of tomato disease identification method for early warning based on computer vision, comprising the following steps:
The identification information of target tomato is obtained, the identification information includes position parameter data and parameter information growth cycle；
The image for acquiring target tomato, obtains target image；
Panoramic picture coordinate system is established according to the position parameter data of multiple target tomatoes；
The target image for integrating multiple target tomatoes generates target panoramic picture by reference system of the panoramic picture coordinate system；
The target panoramic picture is analyzed to obtain defect information.
In a kind of optinal plan: the image of the acquisition target tomato, obtaining target image includes:
The external image for acquiring target tomato, obtains target external image；
The internal image for acquiring target tomato, obtains target internal image.
In a kind of optinal plan: the analysis panoramic picture includes: to obtain defect information
The health that identical growth cycle is in target tomato is transferred from cloud according to parameter growth cycle of target tomato
The image of tomato, obtains contrast images, and the cloud is stored with the image of the healthy tomato in different growth cycles；
The target image and the contrast images are compared, whether analysis target tomato is by disease, if it is, generating disease
Label；
The disease label is added in the target panoramic picture according to the position parameter data of target tomato.
In a kind of optinal plan: the disease label includes level-one disease label and second level disease label, described primary
The degree of disease of the corresponding target tomato of disease label is greater than the degree of disease of the corresponding target tomato of the second level disease label.
A kind of tomato disease identification prior-warning device based on computer vision, comprising:
Module is obtained, for obtaining the identification information of target tomato, the identification information includes position parameter data and development week
Period parameters information；
Acquisition module obtains target image for acquiring the image of target tomato；
Mark module is built, for establishing panoramic picture coordinate system according to the position parameter data of multiple target tomatoes；
Generation module is generated for integrating the target image of multiple target tomatoes by reference system of the panoramic picture coordinate system
Target panoramic picture；
Analysis module, for analyzing the target panoramic picture to obtain defect information.
In a kind of optinal plan: the acquisition module includes:
External image acquisition unit obtains target external image for acquiring the external image of target tomato；
Internal image acquisition unit obtains target internal image for acquiring the internal image of target tomato.
In a kind of optinal plan: the analysis module includes:
Unit is transferred, is transferred for parameter growth cycle according to target tomato from cloud and is in identical hair with target tomato
The image for educating the healthy tomato in period, obtains contrast images, and the cloud is stored with the healthy tomato in different growth cycles
Image；
Whether comparison unit analyzes target tomato by disease for comparing the target image and the contrast images, if
It is then to generate disease label；
Unit is labelled, it is complete that the disease label is added to the target for the position parameter data according to target tomato
In scape image.
A kind of computer storage medium, which is characterized in that the media storage has at least one instruction, and described at least one
Instruction is loaded by processor and is executed to realize that above-mentioned tomato disease based on computer vision identifies method for early warning.
Compared to the prior art, beneficial effects of the present invention are as follows:
For the present invention by the identification information of acquisition target tomato, the identification information includes position parameter data and ginseng growth cycle
Number information；The image for acquiring target tomato, obtains target image；It is established according to the position parameter data of multiple target tomatoes
Panoramic picture coordinate system；The target image for integrating multiple target tomatoes generates mesh by reference system of the panoramic picture coordinate system
Mark panoramic picture；The target panoramic picture is analyzed to obtain defect information.I.e. the present invention is based on computer vision technique to kind
Eggplant disease is identified, working efficiency can be effectively improved, while may be implemented by disease tomato in the accurate fixed of planting area
Position, avoids impacting post-processing.
Detailed description of the invention
Fig. 1 is the flow diagram that tomato disease based on computer vision identifies method for early warning.
Fig. 2 is the flow diagram that tomato disease based on computer vision identifies acquisition step in method for early warning.
Fig. 3 is the flow diagram that tomato disease based on computer vision identifies analytical procedure in method for early warning.
Fig. 4 is the structural schematic diagram that tomato disease based on computer vision identifies prior-warning device.
Fig. 5 is the structural schematic diagram that tomato disease based on computer vision identifies acquisition module in prior-warning device.
Fig. 6 is the structural schematic diagram that tomato disease based on computer vision identifies analysis module in prior-warning device.
Specific embodiment
Each embodiment cited by the present invention is only to illustrate the present invention, is not used to limit the scope of the present invention.To this
Any modification apparent easy to know or change are without departure from spirit and scope of the invention made by invention.
Embodiment 1
As shown in Figure 1-3, in the embodiment of the present invention, a kind of tomato disease identification method for early warning based on computer vision, including
Following steps:
S10, obtains the identification information of target tomato, and the identification information includes position parameter data and parameter growth cycle letter
Breath.
What wherein position parameter data was recorded is target tomato in the location of planting area, and parameter growth cycle is believed
What breath was then recorded is puberty locating for target tomato (germination period, Seedling Stage, florescence, fruiting period).
S20 acquires the image of target tomato, obtains target image.
Traditional manual identified mode is merely able to the characterization external to tomato mostly and observes, and can not understand tomato
Inner case, to easily lead to, information is not comprehensive, and to solve this problem, in the present embodiment, the step S20 includes:
S201 acquires the external image of target tomato, obtains target external image；
S202 acquires the internal image of target tomato, obtains target internal image.
Wherein external image can be acquired using traditional photographic device, and internal image can then use infrared scan instrument
It is acquired, naturally it is also possible to which the acquisition that external image and internal image are carried out using other equipment is not repeated them here herein.
S30 establishes panoramic picture coordinate system according to the position parameter data of multiple target tomatoes.
S40 integrates the target image of multiple target tomatoes, and it is complete to generate target using the panoramic picture coordinate system as reference system
Scape image.
S50 analyzes the target panoramic picture to obtain defect information.
In the present embodiment, the step S50 includes:
S501 is transferred from cloud according to parameter growth cycle of target tomato and is in identical growth cycle with target tomato
The image of healthy tomato, obtains contrast images, and the cloud is stored with the image of the healthy tomato in different growth cycles；
S502 compares the target image and the contrast images, and whether analysis target tomato is by disease, if it is, raw
At disease label；
The disease label is added to the target panoramic picture according to the position parameter data of target tomato by S503
In.
Analytical procedure is subjected to above-mentioned refinement, analysis result can be allowed more intuitively to be presented on staff face
Before, subsequent processing is carried out convenient for staff.
Further, the disease label includes level-one disease label and second level disease label, a disease
The degree of disease of the corresponding target tomato of label is greater than the degree of disease of the corresponding target tomato of the second level disease label.
Staff carries out priority processing according to the tomato deeper to those degree of disease of the difference according to disease label, this
Kind is dealt with more rationally.
Embodiment 2
As Figure 4-Figure 6, a kind of tomato disease based on computer vision identifies that prior-warning device, the device are based on embodiment 1
What the mode was designed, comprising:
Module is obtained, for obtaining the identification information of target tomato, the identification information includes position parameter data and development week
Period parameters information；
Acquisition module obtains target image for acquiring the image of target tomato；
Mark module is built, for establishing panoramic picture coordinate system according to the position parameter data of multiple target tomatoes；
Generation module is generated for integrating the target image of multiple target tomatoes by reference system of the panoramic picture coordinate system
Target panoramic picture；
Analysis module, for analyzing the target panoramic picture to obtain defect information.
Further, the acquisition module includes:
External image acquisition unit obtains target external image for acquiring the external image of target tomato；
Internal image acquisition unit obtains target internal image for acquiring the internal image of target tomato.
Further, the analysis module includes:
Unit is transferred, is transferred for parameter growth cycle according to target tomato from cloud and is in identical hair with target tomato
The image for educating the healthy tomato in period, obtains contrast images, and the cloud is stored with the healthy tomato in different growth cycles
Image；
Whether comparison unit analyzes target tomato by disease for comparing the target image and the contrast images, if
It is then to generate disease label；
Unit is labelled, it is complete that the disease label is added to the target for the position parameter data according to target tomato
In scape image.
Embodiment 3
A kind of computer storage medium, which is characterized in that the media storage has at least one instruction, at least one instruction
It is loaded by processor and is executed to realize tomato disease identification method for early warning based on computer vision as described in Example 1.
The above, the only specific embodiment of the disclosure, but the protection scope of the disclosure is not limited thereto, it is any
Those familiar with the art can easily think of the change or the replacement in the technical scope that the disclosure discloses, and should all contain
It covers within the protection scope of the disclosure.Therefore, the protection scope of the disclosure should be subject to the protection scope in claims.
Claims (8)
1. a kind of tomato disease based on computer vision identifies method for early warning, which comprises the following steps:
The identification information of target tomato is obtained, the identification information includes position parameter data and parameter information growth cycle；
The image for acquiring target tomato, obtains target image；
Panoramic picture coordinate system is established according to the position parameter data of multiple target tomatoes；
The target image for integrating multiple target tomatoes generates target panoramic picture by reference system of the panoramic picture coordinate system；
The target panoramic picture is analyzed to obtain defect information.
2. tomato disease based on computer vision according to claim 1 identifies method for early warning, which is characterized in that described
The image of target tomato is acquired, obtaining target image includes:
The external image for acquiring target tomato, obtains target external image；
The internal image for acquiring target tomato, obtains target internal image.
3. tomato disease based on computer vision according to claim 1 identifies method for early warning, which is characterized in that described
The panoramic picture, which is analyzed, to obtain defect information includes:
The health that identical growth cycle is in target tomato is transferred from cloud according to parameter growth cycle of target tomato
The image of tomato, obtains contrast images, and the cloud is stored with the image of the healthy tomato in different growth cycles；
The target image and the contrast images are compared, whether analysis target tomato is by disease, if it is, generating disease
Label；
The disease label is added in the target panoramic picture according to the position parameter data of target tomato.
4. tomato disease based on computer vision according to claim 3 identifies method for early warning, which is characterized in that described
Disease label includes level-one disease label and second level disease label, the disease journey of the corresponding target tomato of the disease label
Degree is greater than the degree of disease of the corresponding target tomato of the second level disease label.
5. a kind of tomato disease based on computer vision identifies prior-warning device characterized by comprising
Module is obtained, for obtaining the identification information of target tomato, the identification information includes position parameter data and development week
Period parameters information；
Acquisition module obtains target image for acquiring the image of target tomato；
Mark module is built, for establishing panoramic picture coordinate system according to the position parameter data of multiple target tomatoes；
Generation module is generated for integrating the target image of multiple target tomatoes by reference system of the panoramic picture coordinate system
Target panoramic picture；
Analysis module, for analyzing the target panoramic picture to obtain defect information.
6. tomato disease based on computer vision according to claim 5 identifies prior-warning device, which is characterized in that described
Acquisition module includes:
External image acquisition unit obtains target external image for acquiring the external image of target tomato；
Internal image acquisition unit obtains target internal image for acquiring the internal image of target tomato.
7. tomato disease based on computer vision according to claim 5 identifies prior-warning device, which is characterized in that described
Analysis module includes:
Unit is transferred, is transferred for parameter growth cycle according to target tomato from cloud and is in identical hair with target tomato
The image for educating the healthy tomato in period, obtains contrast images, and the cloud is stored with the healthy tomato in different growth cycles
Image；
Whether comparison unit analyzes target tomato by disease for comparing the target image and the contrast images, if
It is then to generate disease label；
Unit is labelled, it is complete that the disease label is added to the target for the position parameter data according to target tomato
In scape image.
8. a kind of computer storage medium, which is characterized in that the media storage has at least one instruction, and described at least one refers to
It enables and is loaded by processor and executed to realize the tomato disease based on computer vision identification as described in claim 1-4 is any
Method for early warning.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910132111.0A CN109886259A (en) | 2019-02-22 | 2019-02-22 | A kind of tomato disease based on computer vision identification method for early warning and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910132111.0A CN109886259A (en) | 2019-02-22 | 2019-02-22 | A kind of tomato disease based on computer vision identification method for early warning and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109886259A true CN109886259A (en) | 2019-06-14 |
Family
ID=66928907
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910132111.0A Pending CN109886259A (en) | 2019-02-22 | 2019-02-22 | A kind of tomato disease based on computer vision identification method for early warning and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109886259A (en) |
Citations (36)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1940555A (en) * | 2006-09-04 | 2007-04-04 | 江苏大学 | Internal and external quality inspecting method and device for agricultural products |
| CN1995987A (en) * | 2007-02-08 | 2007-07-11 | 江苏大学 | Non-destructive detection method and device for agricultural and animal products based on hyperspectral image technology |
| CN101419166A (en) * | 2008-11-18 | 2009-04-29 | 江苏大学 | Tea quality nondestructive detecting method and device based on near-infrared spectrum and machine vision technology |
| CN201382896Y (en) * | 2009-03-05 | 2010-01-13 | 华中农业大学 | Sorting experimental bench for multisource information detection of chesnut quality |
| CN102274833A (en) * | 2011-04-26 | 2011-12-14 | 浙江大学 | Automatic grading device of botrytis of solanaceous vegetables |
| CN102495005A (en) * | 2011-11-17 | 2012-06-13 | 江苏大学 | Method for diagnosing crop water deficit through hyperspectral image technology |
| CN103210896A (en) * | 2013-04-19 | 2013-07-24 | 北京理工大学 | Greenhouse tomato injurious insect intelligent monitoring and trapping system |
| CN103439265A (en) * | 2013-08-15 | 2013-12-11 | 湖南农业大学 | Real-time monitoring method for growth characters of tea trees in intensive cultivation |
| CN104655275A (en) * | 2013-11-19 | 2015-05-27 | 南京理工大学 | Farm-oriented portable integrated spectrum device |
| CN105208344A (en) * | 2015-09-28 | 2015-12-30 | 中国水稻研究所 | Distributed mobile agricultural pest information acquisition and diagnosis system and embedded type camera |
| CN105606473A (en) * | 2016-01-20 | 2016-05-25 | 华中农业大学 | Red globe grape hardness nondestructive detection method based on machine vision |
| CN106157304A (en) * | 2016-07-01 | 2016-11-23 | 成都通甲优博科技有限责任公司 | A kind of Panoramagram montage method based on multiple cameras and system |
| CN106331171A (en) * | 2016-10-17 | 2017-01-11 | 王卫斌 | Pest and disease networking monitoring system and monitoring control method |
| CN106442358A (en) * | 2016-10-11 | 2017-02-22 | 西安工业大学 | Apple quality detection device and apple quality detection method |
| CN106508513A (en) * | 2016-09-05 | 2017-03-22 | 深圳市双赢伟业科技股份有限公司 | Agricultural interconnected deinsectization weeding method |
| CN106778888A (en) * | 2016-12-27 | 2017-05-31 | 浙江大学 | A kind of orchard pest and disease damage survey system and method based on unmanned aerial vehicle remote sensing |
| CN106791659A (en) * | 2016-12-26 | 2017-05-31 | 安徽天立泰科技股份有限公司 | A kind of forest pest and disease monitoring and guard system based on splicing of taking photo by plane |
| CN107222252A (en) * | 2017-06-22 | 2017-09-29 | 中永（苏州）信息技术有限公司 | A kind of moonlet combination public network monitors forest scheme |
| CN107295310A (en) * | 2017-07-31 | 2017-10-24 | 深圳前海弘稼科技有限公司 | Planting monitoring method and planting monitoring device |
| CN107368129A (en) * | 2017-06-30 | 2017-11-21 | 深圳前海弘稼科技有限公司 | Greenhouse planting display method and greenhouse planting display system |
| CN107505325A (en) * | 2017-08-18 | 2017-12-22 | 西北农林科技大学 | The comprehensive quality detecting method of winter-jujube fruit |
| CN107578089A (en) * | 2017-09-13 | 2018-01-12 | 中国水稻研究所 | A kind of crops lamp lures the automatic identification and method of counting for observing and predicting insect |
| CN107807540A (en) * | 2017-10-16 | 2018-03-16 | 吉林农业大学 | A kind of agriculture intelligent platform based on image procossing |
| CN107844771A (en) * | 2017-11-03 | 2018-03-27 | 深圳春沐源控股有限公司 | Method, system, computer installation and the storage medium of crop production management |
| CN107992828A (en) * | 2017-12-05 | 2018-05-04 | 深圳春沐源控股有限公司 | Pest control method and device, electronic equipment and computer-readable recording medium |
| CN108073908A (en) * | 2017-12-28 | 2018-05-25 | 深圳春沐源控股有限公司 | Pest identification method and device, computer device and storage medium |
| CN108197794A (en) * | 2017-12-27 | 2018-06-22 | 深圳春沐源控股有限公司 | A kind of pest and disease damage risk Prediction System and method |
| CN108364300A (en) * | 2018-03-15 | 2018-08-03 | 山东财经大学 | Vegetables leaf portion disease geo-radar image dividing method, system and computer readable storage medium |
| CN108519775A (en) * | 2017-10-30 | 2018-09-11 | 北京博鹰通航科技有限公司 | A kind of UAV system and its control method precisely sprayed |
| CN108622412A (en) * | 2018-05-08 | 2018-10-09 | 苏州农业职业技术学院 | A kind of plant protection drone system |
| CN108732133A (en) * | 2018-04-12 | 2018-11-02 | 杭州电子科技大学 | It is a kind of based on the plant disease of optical image technology in body nondestructive detection system |
| CN208091900U (en) * | 2018-02-01 | 2018-11-13 | 华南农业大学 | A kind of greenhouse crop growth tracing and detecting apparatus |
| CN108782492A (en) * | 2018-05-18 | 2018-11-13 | 仲恺农业工程学院 | A kind of pest control method based on pest control equipment |
| CN108858122A (en) * | 2018-08-13 | 2018-11-23 | 中国农业大学 | A kind of greenhouse plant disease crusing robot and method for inspecting |
| CN109087291A (en) * | 2018-07-26 | 2018-12-25 | 杭州国翌科技有限公司 | Tunnel location information library method for building up and tunnel defect localization method |
| CN109191074A (en) * | 2018-08-27 | 2019-01-11 | 宁夏大学 | Wisdom orchard planting management system |
-
2019
- 2019-02-22 CN CN201910132111.0A patent/CN109886259A/en active Pending
Patent Citations (36)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1940555A (en) * | 2006-09-04 | 2007-04-04 | 江苏大学 | Internal and external quality inspecting method and device for agricultural products |
| CN1995987A (en) * | 2007-02-08 | 2007-07-11 | 江苏大学 | Non-destructive detection method and device for agricultural and animal products based on hyperspectral image technology |
| CN101419166A (en) * | 2008-11-18 | 2009-04-29 | 江苏大学 | Tea quality nondestructive detecting method and device based on near-infrared spectrum and machine vision technology |
| CN201382896Y (en) * | 2009-03-05 | 2010-01-13 | 华中农业大学 | Sorting experimental bench for multisource information detection of chesnut quality |
| CN102274833A (en) * | 2011-04-26 | 2011-12-14 | 浙江大学 | Automatic grading device of botrytis of solanaceous vegetables |
| CN102495005A (en) * | 2011-11-17 | 2012-06-13 | 江苏大学 | Method for diagnosing crop water deficit through hyperspectral image technology |
| CN103210896A (en) * | 2013-04-19 | 2013-07-24 | 北京理工大学 | Greenhouse tomato injurious insect intelligent monitoring and trapping system |
| CN103439265A (en) * | 2013-08-15 | 2013-12-11 | 湖南农业大学 | Real-time monitoring method for growth characters of tea trees in intensive cultivation |
| CN104655275A (en) * | 2013-11-19 | 2015-05-27 | 南京理工大学 | Farm-oriented portable integrated spectrum device |
| CN105208344A (en) * | 2015-09-28 | 2015-12-30 | 中国水稻研究所 | Distributed mobile agricultural pest information acquisition and diagnosis system and embedded type camera |
| CN105606473A (en) * | 2016-01-20 | 2016-05-25 | 华中农业大学 | Red globe grape hardness nondestructive detection method based on machine vision |
| CN106157304A (en) * | 2016-07-01 | 2016-11-23 | 成都通甲优博科技有限责任公司 | A kind of Panoramagram montage method based on multiple cameras and system |
| CN106508513A (en) * | 2016-09-05 | 2017-03-22 | 深圳市双赢伟业科技股份有限公司 | Agricultural interconnected deinsectization weeding method |
| CN106442358A (en) * | 2016-10-11 | 2017-02-22 | 西安工业大学 | Apple quality detection device and apple quality detection method |
| CN106331171A (en) * | 2016-10-17 | 2017-01-11 | 王卫斌 | Pest and disease networking monitoring system and monitoring control method |
| CN106791659A (en) * | 2016-12-26 | 2017-05-31 | 安徽天立泰科技股份有限公司 | A kind of forest pest and disease monitoring and guard system based on splicing of taking photo by plane |
| CN106778888A (en) * | 2016-12-27 | 2017-05-31 | 浙江大学 | A kind of orchard pest and disease damage survey system and method based on unmanned aerial vehicle remote sensing |
| CN107222252A (en) * | 2017-06-22 | 2017-09-29 | 中永（苏州）信息技术有限公司 | A kind of moonlet combination public network monitors forest scheme |
| CN107368129A (en) * | 2017-06-30 | 2017-11-21 | 深圳前海弘稼科技有限公司 | Greenhouse planting display method and greenhouse planting display system |
| CN107295310A (en) * | 2017-07-31 | 2017-10-24 | 深圳前海弘稼科技有限公司 | Planting monitoring method and planting monitoring device |
| CN107505325A (en) * | 2017-08-18 | 2017-12-22 | 西北农林科技大学 | The comprehensive quality detecting method of winter-jujube fruit |
| CN107578089A (en) * | 2017-09-13 | 2018-01-12 | 中国水稻研究所 | A kind of crops lamp lures the automatic identification and method of counting for observing and predicting insect |
| CN107807540A (en) * | 2017-10-16 | 2018-03-16 | 吉林农业大学 | A kind of agriculture intelligent platform based on image procossing |
| CN108519775A (en) * | 2017-10-30 | 2018-09-11 | 北京博鹰通航科技有限公司 | A kind of UAV system and its control method precisely sprayed |
| CN107844771A (en) * | 2017-11-03 | 2018-03-27 | 深圳春沐源控股有限公司 | Method, system, computer installation and the storage medium of crop production management |
| CN107992828A (en) * | 2017-12-05 | 2018-05-04 | 深圳春沐源控股有限公司 | Pest control method and device, electronic equipment and computer-readable recording medium |
| CN108197794A (en) * | 2017-12-27 | 2018-06-22 | 深圳春沐源控股有限公司 | A kind of pest and disease damage risk Prediction System and method |
| CN108073908A (en) * | 2017-12-28 | 2018-05-25 | 深圳春沐源控股有限公司 | Pest identification method and device, computer device and storage medium |
| CN208091900U (en) * | 2018-02-01 | 2018-11-13 | 华南农业大学 | A kind of greenhouse crop growth tracing and detecting apparatus |
| CN108364300A (en) * | 2018-03-15 | 2018-08-03 | 山东财经大学 | Vegetables leaf portion disease geo-radar image dividing method, system and computer readable storage medium |
| CN108732133A (en) * | 2018-04-12 | 2018-11-02 | 杭州电子科技大学 | It is a kind of based on the plant disease of optical image technology in body nondestructive detection system |
| CN108622412A (en) * | 2018-05-08 | 2018-10-09 | 苏州农业职业技术学院 | A kind of plant protection drone system |
| CN108782492A (en) * | 2018-05-18 | 2018-11-13 | 仲恺农业工程学院 | A kind of pest control method based on pest control equipment |
| CN109087291A (en) * | 2018-07-26 | 2018-12-25 | 杭州国翌科技有限公司 | Tunnel location information library method for building up and tunnel defect localization method |
| CN108858122A (en) * | 2018-08-13 | 2018-11-23 | 中国农业大学 | A kind of greenhouse plant disease crusing robot and method for inspecting |
| CN109191074A (en) * | 2018-08-27 | 2019-01-11 | 宁夏大学 | Wisdom orchard planting management system |
Non-Patent Citations (6)
| Title |
|---|
| 周正等: "基于计算机视觉技术的番茄叶部病害识别研究", 《安徽农业科学》 * |
| 张萌: ""农作物害虫图像识别研究进展与展望"", 《安徽农业科学》 * |
| 李盛兰: ""图像处理经典算法在农作物病害识别中的应用"", 《电脑知识与技术》 * |
| 杨断利等: "番茄脐腐病果的机器视觉识别", 《科技经济导刊》 * |
| 杭腾等: "基于机器视觉的番茄长势信息无损检测的研究", 《农机化研究》 * |
| 柴阿丽等: "基于计算机视觉技术的番茄叶部病害识别", 《园艺学报》 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10568316B2 (en) | Apparatus and methods for in-field data collection and sampling | |
| KR101399213B1 (en) | Systems and methods for selecting a desired quantity of follicular units | |
| US11935282B2 (en) | Server of crop growth stage determination system, growth stage determination method, and storage medium storing program | |
| ES2686293T3 (en) | Method to quantify and capture images of elements of a tumor | |
| CN104636981A (en) | Treatment mode suggestion system and method for plant symptoms | |
| WO2017071928A1 (en) | Method and information system for detecting at least one plant planted on a field | |
| CN109613022A (en) | A kind of method, apparatus and system of low latitude high-spectrum remote-sensing detection Citrus Huanglongbing pathogen | |
| Boyle et al. | Automated estimation of tiller number in wheat by ribbon detection | |
| Liu et al. | UAV-based crops classification with joint features from orthoimage and DSM data | |
| Wang et al. | Root anatomy based on root cross-section image analysis with deep learning | |
| CN106896077B (en) | The detection method of transgenic corns glyphosate tolerant phenotype based on imaging-PAM | |
| CN108122224B (en) | System for measuring crown canopy density | |
| Anderegg et al. | Combining high-resolution imaging, deep learning, and dynamic modeling to separate disease and senescence in wheat canopies | |
| CN115620151A (en) | Method and device for identifying phenological period, electronic equipment and storage medium | |
| US11462008B2 (en) | Device for collecting breeding data in farm field, device for analyzing feature in breeding, method for collecting breeding data in farm field, program, and recording medium | |
| CN109886259A (en) | A kind of tomato disease based on computer vision identification method for early warning and device | |
| Lati et al. | 3-D image-driven morphological crop analysis: a novel method for detection of sunflower broomrape initial subsoil parasitism | |
| Khairunniza-Bejo et al. | Basal Stem Rot (BSR) detection using textural analysis of Unmanned Aerial Vehicle (UAV) image | |
| Aleynikov et al. | Automated diseases detection of plant diseases in space greenhouses | |
| CN115660236B (en) | Crop weather period prediction method, device, electronic equipment and storage medium | |
| Millan-Arias et al. | General Cephalometric Landmark Detection for Different Source of X-Ray Images | |
| CN114485612B (en) | Route generation method and device, unmanned operation vehicle, electronic equipment and storage medium | |
| Vong | Quantifying Corn Emergence Using UAV Imagery and Machine Learning | |
| WO2024128041A1 (en) | Cultivation support device, cultivation support method and program | |
| Yang | Check for updates Chapter 1 An Overview of High-Throughput Crop Phenotyping: Platform, Image Analysis, Data Mining, and Data Management Wanneng Yang, Hui Feng, Xiao Hu, Jingyan Song, Jing Guo, and Bingjie Lu |
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 |