CN114295575A - Method for identifying variety of pear by using intelligent mobile terminal - Google Patents
Method for identifying variety of pear by using intelligent mobile terminal Download PDFInfo
- Publication number
- CN114295575A CN114295575A CN202111611218.7A CN202111611218A CN114295575A CN 114295575 A CN114295575 A CN 114295575A CN 202111611218 A CN202111611218 A CN 202111611218A CN 114295575 A CN114295575 A CN 114295575A
- Authority
- CN
- China
- Prior art keywords
- pear
- module
- mobile terminal
- analysis module
- variety
- 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
- 235000014443 Pyrus communis Nutrition 0.000 title claims abstract description 217
- 238000000034 method Methods 0.000 title claims abstract description 23
- 241000220324 Pyrus Species 0.000 claims abstract description 223
- 238000010191 image analysis Methods 0.000 claims abstract description 87
- 238000004458 analytical method Methods 0.000 claims abstract description 75
- 238000002329 infrared spectrum Methods 0.000 claims abstract description 26
- 235000021017 pears Nutrition 0.000 claims description 7
- 238000010835 comparative analysis Methods 0.000 claims description 5
- 238000004891 communication Methods 0.000 claims description 3
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 238000004148 unit process Methods 0.000 description 2
- 241000218922 Magnoliophyta Species 0.000 description 1
- 241000693079 Maloideae Species 0.000 description 1
- 235000004789 Rosa xanthina Nutrition 0.000 description 1
- 241000220222 Rosaceae Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 229920002678 cellulose Polymers 0.000 description 1
- 239000001913 cellulose Substances 0.000 description 1
- 235000009508 confectionery Nutrition 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 235000016709 nutrition Nutrition 0.000 description 1
- 230000035764 nutrition Effects 0.000 description 1
- 229940088594 vitamin Drugs 0.000 description 1
- 229930003231 vitamin Natural products 0.000 description 1
- 235000013343 vitamin Nutrition 0.000 description 1
- 239000011782 vitamin Substances 0.000 description 1
Images
Landscapes
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The application provides a method for identifying pear varieties by using an intelligent mobile terminal, a pear pattern image acquisition module is in data connection with an intelligent mobile terminal image input module, the intelligent mobile terminal image input module is in data connection with the intelligent mobile terminal image analysis module, the intelligent mobile terminal image analysis module is in data connection with the pear variety database module, the intelligent mobile terminal image analysis module is in data connection with the pear characteristic analysis module, the intelligent mobile terminal image analysis module is in data connection with the hyperspectral image analysis module, the hyperspectral image analysis module is in data connection with the infrared spectrum analysis module, the infrared spectrum analysis module is in data connection with the texture analysis module, the hyperspectral image analysis module is in data connection with the pear variety identification feedback module, the pear variety identification feedback module is in data connection with the pear variety mobile terminal identification output module.
Description
Technical Field
The invention relates to the field of identification of pear varieties by intelligent mobile terminals, in particular to a method for identifying the pear varieties by using the intelligent mobile terminals.
Background
The general variety of the pear is a deciduous tree or shrub, a few varieties are evergreen, the pear belongs to the subfamily Maloideae of Rosaceae of dicotyledonae of angiosperma, the leaves are mostly egg-shaped and have different sizes due to different varieties, the fruit of the pear is usually eaten, not only is delicious and juicy, but also is sweet and sour, rich in nutrition, contains various vitamins and cellulose, the taste and texture of different varieties of pears are completely different, and the variety of the pear is more, but the prior art lacks a method for identifying the variety of the pear by using an intelligent mobile terminal, so the method is improved, and the method for identifying the variety of the pear by using the intelligent mobile terminal is provided.
Disclosure of Invention
The invention aims to: in order to solve the problems of the prior art, the invention provides the following technical scheme: the utility model provides a method for utilize intelligent mobile terminal to discern the pears variety, in order to improve above-mentioned problem, this application specifically is such: the method comprises the following steps that S1 images are acquired through a pear pattern image acquisition module and images are input through an intelligent mobile terminal image input module, S2 images are input into an intelligent mobile terminal image analysis module through the intelligent mobile terminal image input module to be analyzed, S3 images of pear varieties are analyzed through the intelligent mobile terminal image analysis module and then are compared through a pear variety database module, S4 the intelligent mobile terminal image analysis module analyzes the characteristics of pears through a pear characteristic analysis module, S5 the intelligent mobile terminal image analysis module analyzes hyperspectral images through a hyperspectral image analysis module, S6 the hyperspectral image analysis module analyzes the hyperspectral images and then analyzes the infrared analysis through the infrared spectrum analysis module, and S7 the infrared spectrum analysis module analyzes the texture of the pear surface through the texture analysis module, the hyperspectral image analysis module of S8 is in data connection with the pear variety identification feedback module, the result of pear variety identification is fed back through the pear variety identification feedback module, and the identification result is output through the pear variety mobile terminal identification output module when the feedback result of the pear variety identification feedback module of S9 is correct.
As a preferable technical solution of the present application, in S1, the pear pattern photographing module inputs the collected pear pattern into the image input module of the intelligent mobile terminal, the pear pattern photographing module includes a pear pattern photographing unit and a pear pattern processing unit, and the pear pattern photographing unit performs pattern processing through the pear pattern processing unit.
As a preferred technical solution of the present application, in S2, the intelligent mobile terminal image input module inputs a pear pattern into the intelligent mobile terminal image analysis module for analyzing and identifying the pear pattern, the intelligent mobile terminal image analysis module includes a pear image texture analysis unit, a pear image shape analysis unit, and a pear image color analysis unit, and the pear image texture analysis unit, the pear image shape analysis unit, and the pear image color analysis unit are in data communication.
As the preferable technical scheme, in the S3, the intelligent mobile terminal image analysis module compares the pear varieties in the pear variety database module, and the pear variety database module is connected with the pear variety database on the Internet through a cloud database network.
As a preferred technical solution of the present application, in S4, the intelligent mobile terminal image analysis module performs comparative analysis on the characteristics of the pear sample to be identified through the pear characteristic analysis module.
As an optimal technical scheme, in the S5, the intelligent mobile terminal image analysis module analyzes the hyperspectral image of the pear sample through the hyperspectral image analysis module.
As the preferable technical scheme, in the S6, the hyperspectral image analysis module collects and analyzes the infrared spectrum of the pear sample through the infrared spectrum analysis module.
As a preferred technical solution of the present application, in S7, the infrared spectrum analysis module analyzes the texture of the outer surface of the pear sample through the texture analysis module.
As a preferable technical scheme of the application, in the S8, the hyperspectral image analysis module feeds back the identification result of the pear sample through a pear variety identification feedback module, and the pear variety identification feedback module analyzes the pear sample through the hyperspectral image analysis module again when the pear variety identification feedback module has an error in feedback.
As a preferred technical solution of the present application, the pear variety identification feedback module in S9 outputs data through the pear variety mobile terminal identification output module when the feedback result is correct.
Compared with the prior art, the invention has the beneficial effects that:
in the scheme of the application:
1. inputting the collected pear patterns into an intelligent mobile terminal image input module through the pear pattern image collecting module, wherein the pear pattern image collecting module comprises a pear pattern shooting unit and a pear pattern processing unit, the pear pattern shooting unit processes the patterns through the pear pattern processing unit, and the intelligent mobile terminal image input module inputs the pear patterns into an intelligent mobile terminal image analysis module to analyze and identify the pear patterns;
2. the intelligent mobile terminal image analysis module is in data connection with a pear variety database module, the intelligent mobile terminal image analysis module in S3 is compared with pear varieties in the pear variety database module, the pear variety database module is in network connection with a pear variety database on the Internet, and the intelligent mobile terminal image analysis module is in data connection with a pear characteristic analysis module in S4;
3. the intelligent mobile terminal image analysis module is used for carrying out comparative analysis on the pear sample to be identified through the pear characteristics of a pear, the intelligent mobile terminal image analysis module is in data connection with the hyperspectral image analysis module, and the intelligent mobile terminal image analysis module is used for carrying out hyperspectral image analysis on the pear sample through the hyperspectral image analysis module;
4. the infrared spectrum of the pear sample is collected and analyzed through the hyperspectral image analysis module and the infrared spectrum analysis module, the infrared spectrum analysis module is in data connection with the texture analysis module, the infrared spectrum analysis module analyzes the texture of the outer surface of the pear sample through the texture analysis module, the hyperspectral image analysis module is in data connection with the pear variety identification feedback module, the hyperspectral image analysis module feeds back the identification result of the pear sample through the pear variety identification feedback module, when the pear variety identification feedback module has an error in feedback, the hyperspectral image analysis module is used for analyzing again, the pear variety identification feedback module is in data connection with the pear variety mobile terminal identification output module, and when the pear variety identification feedback module feeds back a correct result, data output is carried out through the pear variety mobile terminal identification output module.
Description of the drawings:
FIG. 1 is a flowchart of a method for identifying a variety of a pear by using an intelligent mobile terminal.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments.
Thus, the following detailed description of the embodiments of the invention is not intended to limit the scope of the invention as claimed, but is merely representative of some embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that, without conflict, the embodiments of the present invention and the features and technical solutions in the embodiments may be combined with each other, and it should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
As shown in FIG. 1, the embodiment provides a method for identifying a variety of a pear by using an intelligent mobile terminal, which includes the following steps that S1 performs image acquisition of a pear pattern through a pear pattern image acquisition module and inputs an image through an intelligent mobile terminal image input module, S2 inputs the image into an intelligent mobile terminal image analysis module through the intelligent mobile terminal image input module for analysis, S3 performs image analysis on the variety of the pear through the intelligent mobile terminal image analysis module and then compares the variety of the pear through a pear variety database module, S4 performs analysis on characteristics of the pear through a pear characteristic analysis module, S5 performs hyperspectral image analysis through a hyperspectral image analysis module, and S6 performs infrared analysis through an infrared spectrum analysis module after performing hyperspectral image analysis, the S7 infrared spectrum analysis module analyzes the texture of the surface of the pear through the texture analysis module, the S8 hyperspectral image analysis module is in data connection with the pear variety identification feedback module, the result of the pear variety identification is fed back through the pear variety identification feedback module, and the identification result is output through the pear variety mobile terminal identification output module when the feedback result of the S9 pear variety identification feedback module is correct.
As a preferred embodiment, in addition to the above-mentioned mode, in S1, the pear pattern capturing module inputs the captured pear pattern into the image input module of the smart mobile terminal, and the pear pattern capturing module includes a pear pattern capturing unit and a pear pattern processing unit, and the pear pattern capturing unit performs pattern processing through the pear pattern processing unit.
As a preferred embodiment, on the basis of the above-mentioned manner, further, in S2, the intelligent mobile terminal image input module inputs the pear pattern into the intelligent mobile terminal image analysis module for analyzing and identifying the pear pattern, the intelligent mobile terminal image analysis module includes a pear image texture analysis unit, a pear image shape analysis unit, and a pear image color analysis unit, and the pear image texture analysis unit, the pear image shape analysis unit, and the pear image color analysis unit are in data communication connection.
As a preferred embodiment, on the basis of the above manner, further, the intelligent mobile terminal image analysis module in S3 compares the pear varieties in the pear variety database module, and the pear variety database module is connected to the pear variety database via the internet cloud.
As a preferred embodiment, on the basis of the above manner, further, in S4, the intelligent mobile terminal image analysis module performs comparative analysis on the features of the pear sample to be identified through the pear feature analysis module.
In a preferred embodiment, in addition to the above-mentioned manner, in S5, the intelligent mobile terminal image analysis module performs hyperspectral image analysis on the pear sample by using the hyperspectral image analysis module.
In a preferred embodiment, on the basis of the above manner, the hyperspectral image analysis module in S6 further performs infrared spectrum collection and analysis on the pear sample through the infrared spectrum analysis module.
In a preferred embodiment, based on the above manner, the S7 mid-infrared spectrum analysis module further performs texture analysis on the outer surface of the pear sample by the texture analysis module.
In a preferred embodiment, in addition to the above-mentioned mode, the hyperspectral image analysis module in S8 feeds back the result of identifying the pear sample through the pear variety identification feedback module, and the hyperspectral image analysis module analyzes the result again when the pear variety identification feedback module has an error in feedback.
In addition to the above, in a preferred embodiment, in S9, when the result fed back by the pear variety discrimination feedback module is correct, the data is output by the pear variety mobile terminal discrimination output module.
A method for identifying pear varieties by using an intelligent mobile terminal is characterized in that when the method is used: s1 pear pattern image-capturing module is connected with intelligent mobile terminal image input module, S1 pear pattern image-capturing module inputs the collected pear pattern into intelligent mobile terminal image input module, the pear pattern image-capturing module comprises a pear pattern shooting unit and a pear pattern processing unit, the pear pattern shooting unit processes the pattern through the pear pattern processing unit, S2 intelligent mobile terminal image input module is connected with intelligent mobile terminal image analysis module, S2 intelligent mobile terminal image input module inputs the pear pattern into the intelligent mobile terminal image analysis module for analyzing and identifying the pear pattern, the intelligent mobile terminal image analysis module comprises a pear image texture analysis unit, a pear image shape analysis unit and a pear image color analysis unit, the pear image texture analysis unit, the pear image shape analysis unit and the pear image color analysis unit are connected in data intercommunication, the S3 intelligent mobile terminal image analysis module is in data connection with the pear variety database module, the S3 intelligent mobile terminal image analysis module is in data connection with the pear variety in the pear variety database module, the pear variety database module is in network connection with the cloud database on the pear variety database, the S4 intelligent mobile terminal image analysis module is in data connection with the pear characteristic analysis module, the S4 intelligent mobile terminal image analysis module performs comparative analysis on a pear sample to be identified through the pear characteristic analysis module, the S5 intelligent mobile terminal image analysis module is in data connection with the hyperspectral image analysis module, the S5 intelligent mobile terminal image analysis module performs hyperspectral image analysis on the pear sample through the hyperspectral image analysis module, the S6 hyperspectral image analysis module is in data connection with the infrared spectrum analysis module, the S6 hyperspectral image analysis module performs acquisition and analysis on the pear sample through the infrared spectrum analysis module, the S7 infrared spectrum analysis module is in data connection with the texture analysis module, the S7 infrared spectrum analysis module analyzes the texture of the outer surface of the pear sample through the texture analysis module, the S8 hyperspectral image analysis module is in data connection with the pear variety identification feedback module, the S8 hyperspectral image analysis module feeds back the identification result of the pear sample through the pear variety identification feedback module, the pear variety identification feedback module analyzes the result again through the hyperspectral image analysis module when the feedback is wrong, the S9 pear variety identification feedback module is in data connection with the pear variety mobile terminal identification output module, and the S9 pear variety identification feedback module outputs the data through the pear variety mobile terminal identification output module when the feedback result is correct.
The above embodiments are only used for illustrating the invention and not for limiting the technical solutions described in the invention, and although the present invention has been described in detail in the present specification with reference to the above embodiments, the present invention is not limited to the above embodiments, and therefore, any modification or equivalent replacement of the present invention is made; all such modifications and variations are intended to be included herein within the scope of this disclosure and the appended claims.
Claims (10)
1. A method for identifying pear varieties by using an intelligent mobile terminal is characterized by comprising the following steps of S1 acquiring an image of a pear pattern through a pear pattern acquisition module and inputting the image through an intelligent mobile terminal image input module, S2 inputting the image into an intelligent mobile terminal image analysis module through the intelligent mobile terminal image input module for analysis, S3 analyzing the image of the pear varieties through the intelligent mobile terminal image analysis module and then comparing the images through a pear variety database module, S4 analyzing the characteristics of pears through a pear characteristic analysis module, S5 analyzing the hyperspectral image through a hyperspectral image analysis module by the intelligent mobile terminal image analysis module, S6 analyzing the hyperspectral image through the hyperspectral image analysis module and then analyzing the infrared spectrum through the infrared spectrum analysis module, the infrared spectrum analysis module of S7 analyzes the texture of the pear surface through the texture analysis module, the hyperspectral image analysis module of S8 is in data connection with the pear variety identification feedback module, the result of pear variety identification is fed back through the pear variety identification feedback module, and the identification result is output through the pear variety mobile terminal identification output module when the feedback result of the pear variety identification feedback module of S9 is correct.
2. The method for identifying the variety of pears by using the intelligent mobile terminal as claimed in claim 1, wherein the pear pattern photographing module in S1 inputs the acquired pear pattern into the intelligent mobile terminal image input module, and the pear pattern photographing module comprises a pear pattern photographing unit and a pear pattern processing unit, and the pear pattern photographing unit performs pattern processing through the pear pattern processing unit.
3. The method for identifying the variety of pears by using the intelligent mobile terminal as claimed in claim 1, wherein in S2, the intelligent mobile terminal image input module inputs a pear pattern into the intelligent mobile terminal image analysis module for analyzing and identifying the pear pattern, the intelligent mobile terminal image analysis module comprises a pear image texture analysis unit, a pear image shape analysis unit and a pear image color analysis unit, and the pear image texture analysis unit, the pear image shape analysis unit and the pear image color analysis unit are in data communication.
4. The method for identifying the variety of the pear by using the intelligent mobile terminal as claimed in claim 1, wherein the intelligent mobile terminal image analysis module in the S3 compares the variety of the pear with a pear variety database module, and the pear variety database module is connected with a pear variety database on an internet cloud side through a network.
5. The method for identifying the variety of the pear by using the intelligent mobile terminal as claimed in claim 1, wherein the intelligent mobile terminal image analysis module in S4 performs comparative analysis on the pear sample to be identified through the feature of the pear by using a pear feature analysis module.
6. The method for identifying the pear varieties according to claim 1, wherein in the step S5, the intelligent mobile terminal image analysis module performs hyperspectral image analysis on the pear samples through the hyperspectral image analysis module.
7. The method for identifying the variety of the pear according to claim 1, wherein the hyperspectral image analysis module in the S6 performs infrared spectrum collection and analysis on the pear sample through the infrared spectrum analysis module.
8. The method for identifying the variety of the pears with the intelligent mobile terminal as claimed in claim 1, wherein the infrared spectrum analysis module in S7 analyzes the texture of the outer surface of the pear sample through a texture analysis module.
9. The method for identifying the variety of the pear according to claim 1, wherein the hyperspectral image analysis module in S8 feeds back the identification result of the pear sample through a pear variety identification feedback module, and the pear variety identification feedback module analyzes the pear sample again through the hyperspectral image analysis module when the pear variety identification feedback module has an error in feedback.
10. The method for identifying the variety of the pear according to claim 1, wherein the pear variety identification feedback module in S9 outputs data through the pear variety mobile terminal identification output module when the feedback result is correct.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111611218.7A CN114295575A (en) | 2021-12-27 | 2021-12-27 | Method for identifying variety of pear by using intelligent mobile terminal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111611218.7A CN114295575A (en) | 2021-12-27 | 2021-12-27 | Method for identifying variety of pear by using intelligent mobile terminal |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114295575A true CN114295575A (en) | 2022-04-08 |
Family
ID=80969431
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111611218.7A Pending CN114295575A (en) | 2021-12-27 | 2021-12-27 | Method for identifying variety of pear by using intelligent mobile terminal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114295575A (en) |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN201041553Y (en) * | 2007-05-29 | 2008-03-26 | 浙江大学 | Fruit quality non-damage detection system base on multi-spectrum imaging technology |
| KR101249537B1 (en) * | 2012-01-20 | 2013-05-14 | 김태효 | Jewelry appraisal method using cellular phones |
| US20140093138A1 (en) * | 2011-06-29 | 2014-04-03 | Fujitsu Limited | Plant species identification apparatus and method |
| CN104359838A (en) * | 2014-11-11 | 2015-02-18 | 上海理工大学 | Non-destructive detection method for hardness of pear |
| CN105403507A (en) * | 2014-09-10 | 2016-03-16 | 中国农业科学院茶叶研究所 | Mobile-terminal-based Longjing tea identification and judgment system and method |
| CN107561040A (en) * | 2016-07-01 | 2018-01-09 | 苑高强 | A kind of mobile phone material identifier, mobile phone and Internet of Things |
| CN107576600A (en) * | 2017-08-04 | 2018-01-12 | 江苏大学 | A kind of quick determination method for smearing tea grain size category |
| CN109827910A (en) * | 2019-01-22 | 2019-05-31 | 塔里木大学 | A kind of quick monitoring process method of orchard establishing data |
| CN111487247A (en) * | 2020-04-28 | 2020-08-04 | 前海国兴(深圳)高科技有限公司 | Fruit maturity AI identification system |
| CN112801119A (en) * | 2021-03-09 | 2021-05-14 | 中国农业科学院果树研究所 | Pear variety identification method based on image identification |
| CN112884058A (en) * | 2021-03-09 | 2021-06-01 | 天津中医药大学 | Fritillaria variety identification method and system based on image and hyperspectrum combination |
| CN113686803A (en) * | 2021-08-11 | 2021-11-23 | 哈尔滨工业大学 | Apple sugar degree nondestructive measurement device and method based on smart phone |
-
2021
- 2021-12-27 CN CN202111611218.7A patent/CN114295575A/en active Pending
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN201041553Y (en) * | 2007-05-29 | 2008-03-26 | 浙江大学 | Fruit quality non-damage detection system base on multi-spectrum imaging technology |
| US20140093138A1 (en) * | 2011-06-29 | 2014-04-03 | Fujitsu Limited | Plant species identification apparatus and method |
| KR101249537B1 (en) * | 2012-01-20 | 2013-05-14 | 김태효 | Jewelry appraisal method using cellular phones |
| CN105403507A (en) * | 2014-09-10 | 2016-03-16 | 中国农业科学院茶叶研究所 | Mobile-terminal-based Longjing tea identification and judgment system and method |
| CN104359838A (en) * | 2014-11-11 | 2015-02-18 | 上海理工大学 | Non-destructive detection method for hardness of pear |
| CN107561040A (en) * | 2016-07-01 | 2018-01-09 | 苑高强 | A kind of mobile phone material identifier, mobile phone and Internet of Things |
| CN107576600A (en) * | 2017-08-04 | 2018-01-12 | 江苏大学 | A kind of quick determination method for smearing tea grain size category |
| CN109827910A (en) * | 2019-01-22 | 2019-05-31 | 塔里木大学 | A kind of quick monitoring process method of orchard establishing data |
| CN111487247A (en) * | 2020-04-28 | 2020-08-04 | 前海国兴(深圳)高科技有限公司 | Fruit maturity AI identification system |
| CN112801119A (en) * | 2021-03-09 | 2021-05-14 | 中国农业科学院果树研究所 | Pear variety identification method based on image identification |
| CN112884058A (en) * | 2021-03-09 | 2021-06-01 | 天津中医药大学 | Fritillaria variety identification method and system based on image and hyperspectrum combination |
| CN113686803A (en) * | 2021-08-11 | 2021-11-23 | 哈尔滨工业大学 | Apple sugar degree nondestructive measurement device and method based on smart phone |
Non-Patent Citations (4)
| Title |
|---|
| 葛迪;李绍稳;魏同;徐静;: "基于移动溯源与图像分析的茶叶品级鉴定方法研究", 中国农学通报, no. 26, pages 261 - 265 * |
| 蒋爱民: "《食品原料学》", vol. 2021, 30 June 2021, 中国轻工业出版社, pages: 169 - 170 * |
| 郑小东;王杰;: "机器视觉在玉米籽粒品质检测中的应用研究", 中国粮油学报, no. 04, pages 124 - 128 * |
| 陈雪鑫;卜庆凯;: "基于改进的最大类间方差法的水果图像识别研究", 青岛大学学报(工程技术版), no. 02, pages 33 - 38 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111860533B (en) | Image recognition method and device, storage medium and electronic device | |
| Aquino et al. | vitisBerry: An Android-smartphone application to early evaluate the number of grapevine berries by means of image analysis | |
| Tripathi et al. | Recent machine learning based approaches for disease detection and classification of agricultural products | |
| Liu et al. | Detection of citrus fruit and tree trunks in natural environments using a multi-elliptical boundary model | |
| WO2017215206A1 (en) | Automatic plant identification system and method | |
| CN110569786B (en) | Fruit tree identification and quantity monitoring method and system based on unmanned aerial vehicle data acquisition | |
| CN109002851B (en) | Fruit classification method based on image multi-feature fusion and application | |
| US20240013380A1 (en) | Cigar tobacco leaf harvesting maturity identification method and system based on integrated learning | |
| CN106778786A (en) | Apple disease recognition methods based on log-spectral domain laminated gradient direction histogram | |
| CN112539785A (en) | Tobacco grade identification system and method based on multi-dimensional characteristic information | |
| CN106803093A (en) | A kind of plant species recognition methods based on blade textural characteristics and iOS platforms | |
| CN112834442A (en) | Multispectral data-based large-area-scale crop growth real-time monitoring method | |
| CN109325410B (en) | Electroencephalogram EEG (electroencephalogram) feature extraction method based on convolutional neural network | |
| CN113252584A (en) | Crop growth detection method and system based on 5G transmission | |
| CN116524224A (en) | Machine vision-based method and system for detecting type of cured tobacco leaves | |
| CN114596274A (en) | Natural background citrus greening disease detection method based on improved Cascade RCNN network | |
| CN114295575A (en) | Method for identifying variety of pear by using intelligent mobile terminal | |
| CN110363075A (en) | Suspicious ill face detection system based on big data server | |
| CN111122453A (en) | Information processing method, device and system | |
| CN116863452A (en) | Plant disease identification method, storage medium, and identification system | |
| CN113361315A (en) | Banana string identification method based on background saturation compression and difference threshold segmentation fusion | |
| CN110399887B (en) | Representative color extraction method based on visual saliency and histogram statistical technology | |
| Harini et al. | Identification of leaf diseases in tomato plant based on wavelets and PCA | |
| CN112529838A (en) | Tobacco leaf maturity online judging method based on image processing technology | |
| CN116050253A (en) | Dish flavor intelligent identification method, device, equipment and storage medium |
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 |