CN101074927A - Method for diagnosing fruit diseases based on visible and near-infrared spectral characteristic band - Google Patents
Method for diagnosing fruit diseases based on visible and near-infrared spectral characteristic band Download PDFInfo
- Publication number
- CN101074927A CN101074927A CN 200710069633 CN200710069633A CN101074927A CN 101074927 A CN101074927 A CN 101074927A CN 200710069633 CN200710069633 CN 200710069633 CN 200710069633 A CN200710069633 A CN 200710069633A CN 101074927 A CN101074927 A CN 101074927A
- Authority
- CN
- China
- Prior art keywords
- water core
- fruit
- core disease
- spectral
- visible
- 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
- 235000013399 edible fruits Nutrition 0.000 title claims abstract description 37
- 230000003595 spectral effect Effects 0.000 title claims abstract description 36
- 238000000034 method Methods 0.000 title claims abstract description 18
- 201000010099 disease Diseases 0.000 title claims abstract 17
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 title claims abstract 17
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 62
- 238000002329 infrared spectrum Methods 0.000 claims abstract description 18
- 208000015181 infectious disease Diseases 0.000 claims abstract description 16
- 238000003745 diagnosis Methods 0.000 claims abstract description 8
- 230000003750 conditioning effect Effects 0.000 claims abstract description 4
- 239000000523 sample Substances 0.000 claims description 9
- 238000004458 analytical method Methods 0.000 abstract description 10
- 238000001514 detection method Methods 0.000 abstract description 7
- 230000001066 destructive effect Effects 0.000 abstract description 4
- 238000002310 reflectometry Methods 0.000 description 9
- 238000001228 spectrum Methods 0.000 description 5
- 238000012937 correction Methods 0.000 description 4
- 238000002405 diagnostic procedure Methods 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 238000010183 spectrum analysis Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 125000004435 hydrogen atom Chemical group [H]* 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 229920002472 Starch Polymers 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000003321 amplification Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000009614 chemical analysis method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 239000012141 concentrate Substances 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 230000003760 hair shine Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000010238 partial least squares regression Methods 0.000 description 1
- 238000012628 principal component regression Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000010223 real-time analysis Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000008107 starch Substances 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000001429 visible spectrum Methods 0.000 description 1
Images
Landscapes
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
本发明公开了一种可见和近红外光谱特征波段的水果水心病诊断方法。光线照射到水果表面，被光敏传感器接受，每个光敏传感器采集一个特征波段上的水果内部的光谱反射率信息，通过数据线经过信号调理电路去除噪音，A/D转换器输入单片机；水心病诊断程序将得到的光谱反射率信息输入至水心病光谱模型，通过模型的计算，输出水心病诊断结果；LCD显示器输出水果水心病的诊断结果。本发明可以应用于水果水心病感染程度的诊断，检测成本低，分析速度快，检测无损，非破坏；本发明基于特征波段进行检测，易于携带，程序简单。
The invention discloses a method for diagnosing fruit water core disease in characteristic bands of visible and near infrared spectra. The light irradiates the surface of the fruit and is accepted by the photosensitive sensor. Each photosensitive sensor collects the spectral reflectance information inside the fruit on a characteristic band, and the noise is removed through the signal conditioning circuit through the data line, and the A/D converter is input into the microcontroller; water core disease diagnosis The program inputs the obtained spectral reflectance information into the spectral model of water core disease, and outputs the diagnosis result of water core disease through the calculation of the model; the LCD display outputs the diagnosis result of fruit water core disease. The invention can be applied to the diagnosis of fruit water core disease infection degree, has low detection cost, fast analysis speed, non-destructive and non-destructive detection; the invention detects based on characteristic wave bands, is easy to carry, and has simple procedures.
Description
Technical field
The present invention relates to utilize optical instrument to come the method for analysis of material, especially relate to a kind of fruit water core diagnostic method based on visible and near infrared spectrum characteristic wave bands.
Background technology
Near infrared spectrum is meant the electromagnetic wave of wavelength at 780～2526nm, between visible region and mid-infrared light district.Because the sum of fundamental frequencies that contains hydrogen group (C-H, N-H) vibration near infrared spectrum district and the organic molecule and the absorption of frequencys multiplication at different levels are consistent, so the near infrared spectrum by scanning samples, can obtain the eigen vibration information that organic molecule in the sample contains hydrogen group.Compare with traditional chemical analysis method, the major technique characteristics of near-infrared spectrum technique are: analysis speed is fast, and simultaneous determination of multiponents, sample do not need pre-service, non-destructive analysis, and long distance is measured and real-time analysis, harmonic analysis cost and simple to operate.
As seen in the check and analysis of agricultural product and food, obtained using widely with near-infrared spectrum technique.In the fruit context of detection, people such as lammertyn utilize visible spectrum and near infrared spectrum to detect apple acidity, hardness and soluble solid content, and combine with the analysis that routinizes, and have set up the quantitative math-model of prediction apple sugar content and acidity.People such as McGlone utilize the 500-1100nm spectral range to detect Royal Gala apple internal composition---starch, soluble solid and acidity.People such as Lu utilize near infrared spectrum to detect Empire and Delicious apple hardness and pol, by spectrum is handled, and combine with the physico-chemical analysis of routine, have set up the mathematical model of prediction apple sugar content and acidity.Less relatively in research to the fruit water core.
Yet visible and the near infrared spectrum regional extent is bigger, though have now much be used for measuring whole as seen and the spectrometer of near infrared spectrum regional extent, these equipment are all bigger, are unfavorable for portable measurement.By discovering, existence can reflect the several features wave band of fruit water core degree, and its spectral information changes closely related with the water core degree.Therefore can objectively reflect the water core gradient of infection by the spectral information of measuring these characteristic wave bands, and not be subjected to the influence of other factors.Based on a large amount of tests, obtain the several features wave band of reflection water core degree in visible and near infrared spectrum zone, thereby design portable set based on microcontroller embedded system.By detecting the characteristic wave bands reflectivity information that these light sensors obtain, adopt simple homing method to replace chemometrics method to carry out modeling.Portable water core degree detecting instrument is made in the embedding of implementation model algorithm in single-chip microcomputer.
Summary of the invention
The object of the present invention is to provide a kind of fruit water core diagnostic method based on visible and near infrared spectrum characteristic wave bands, can gather the reflectivity information of tested fruit internal characteristic wave bands quickly and accurately, analysis obtains tested fruit water core gradient of infection, thereby realizes the fruit water core diagnostic system of real non-destructive.
The technical solution used in the present invention is that the step of this method is as follows:
1) probe with instrument shines tested fruit, by the fruit internal spectral reflectivity information on the light sensor acquisition characteristics wave band of instrument;
2) the fruit internal reflective information that collects is removed noise by data line through signal conditioning circuit, A/D converter input single-chip microcomputer;
3) the water core diagnostic routine inputs to the water core spectral model with the spectral reflectivity information that obtains, by the calculating of model, and output water core diagnostic result;
4) diagnostic result of LCD display output fruit water core.
The foundation of described water core spectral model may further comprise the steps:
1) obtains to reflect the several features wave band of water core gradient of infection by test.
2) set up water core spectral model between these characteristic wave bands spectral reflectance information and the water core gradient of infection.
Compare with traditional water worry detection means, the beneficial effect that the present invention has is:
(1) utilize spectral technique to carry out the water core diagnosis, as long as can find several spectral signature wave bands that can reflect the water core degree, thinking of the present invention can be applied to quick, accurate, stable, real-time, the nondestructive diagnosis of fruit water core.
(2) reduce the detection cost, accelerate analysis speed, reduce labor intensity, and can can't harm discriminating, need not destroy fruit analyzing samples.
(3) because the water core of system detects is the whole spectral informations that give several features wave band rather than whole visible and near infrared spectrum.Therefore need not to give calculated amount big chemometrics method, program is simple, can write to embed in the single-chip microcomputer.
(4) whole detection system is a portable system that has the embedded scm of several light sensors and LED lamp, is easy to carry about with one.
Description of drawings
Fig. 1 is a service system block diagram of the present invention.
Fig. 2 is the software flow pattern of system of the present invention.
Embodiment
As shown in Figure 1, a kind of fruit water core diagnostic system based on visible and near infrared spectrum characteristic wave bands comprises a portable system that has the embedded scm of several light sensors and LED lamp.System is the damascene structures of microcontroller with the single-chip microcomputer, with signal conditioning circuit signal of sensor is carried out filtering, the processing of amplification etc., make signal meet the input requirement of A/D converter, A/D converter in the system is realized the conversion of simulating signal to digital signal, uses for single-chip microcomputer, and keyboard is realized the input of external command in the system, the operation of control instrument, LCD display output water core diagnostic result.
With the probe irradiation fruit surface of instrument, the light sensor in the probe can be gathered the spectral reflectivity of the several features wave band that can reflect fruit water core degree in real time.Use the LED lamp as light source in the probe, by data line input single-chip microcomputer, the spectral analysis process software is analyzed the spectral information of water core in various degree.Obtain fruit water core gradient of infection by the water core spectral analysis program that embeds in the single-chip microcomputer.
The spectral measurement of system of the present invention is simple, only needs the tested fruit surface of system's probe irradiation can be carried out the detection of water core degree.
As shown in Figure 2, the water core spectral model in the water core spectral analysis process software is established as foundation, just sets up in the software development stage, may further comprise the steps:
1) acquisition correction sample set spectral information.Adopt the demarcation blank that the spectral information of gathering is demarcated before the spectral measurement.Concentrate the spectral reflectivity information of all band (400-2500nm) of fruit then by visible and near infrared spectrometer acquisition correction.
2) the spectrum pre-service of reflection water core gradient of infection.Because the original spectrum information that collects has certain noise, so adopt methods such as convolution is level and smooth, standardization, polynary scatter correction, differentiate, small echo to carry out the spectrum pre-service.
3) set up the spectrum correction model of fruit water core gradient of infection.The fruit that calibration samples is concentrated carries out the gradient of infection assessment of each fruit water core after gathering reflective information through visible and near infrared spectrometer through the water core diagnostic criteria.And set up the mathematical model of pretreated spectral reflectance information and water core gradient of infection by chemometrics method.Comprise: principal component regression, partial least squares regression, neural network model etc.
4) obtain to reflect the several features wave band of water core gradient of infection by a large amount of tests.By analyzing each wave band, select the big minority wave band of contribution rate to substitute whole spectral band in contribution rate to reflection water core gradient of infection.
5) by chemometrics method, set up the relational model between these characteristic wave bands spectral reflectivity information and the water core gradient of infection.Database model has good robustness and adaptability, can carry out practical application.
As shown in Figure 2, water core spectral analysis process software may further comprise the steps in actual applications:
1) by the fruit internal spectral reflectivity information on the light sensor acquisition characteristics wave band of instrument.
2) the water core diagnostic routine inputs to the water core spectral model with the spectral reflectivity information that obtains.By the calculating of model, output water core gradient of infection.
3) LCD display output water core degree diagnostic result.
Claims (2)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 200710069633 CN101074927A (en) | 2007-06-22 | 2007-06-22 | Method for diagnosing fruit diseases based on visible and near-infrared spectral characteristic band |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 200710069633 CN101074927A (en) | 2007-06-22 | 2007-06-22 | Method for diagnosing fruit diseases based on visible and near-infrared spectral characteristic band |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN101074927A true CN101074927A (en) | 2007-11-21 |
Family
ID=38976103
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN 200710069633 Pending CN101074927A (en) | 2007-06-22 | 2007-06-22 | Method for diagnosing fruit diseases based on visible and near-infrared spectral characteristic band |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101074927A (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101949834A (en) * | 2010-08-02 | 2011-01-19 | 扬州福尔喜果蔬汁机械有限公司 | Method for detecting and grading internal quality of fruits |
| CN102636455A (en) * | 2012-05-21 | 2012-08-15 | 山东理工大学 | Method for measuring hardness of agaricus bisporus by using near infrared spectrum |
| CN102788754A (en) * | 2012-08-13 | 2012-11-21 | 浙江大学 | Device and method for measuring multi-index parameters of pear |
| CN102829849A (en) * | 2012-08-13 | 2012-12-19 | 浙江大学 | Device and method for multi-index parametric measurement of pears |
| CN103308457A (en) * | 2013-04-10 | 2013-09-18 | 浙江工商大学 | Establishment method of prediction model for bergamot pear maturity |
| CN103472011A (en) * | 2013-09-20 | 2013-12-25 | 华东交通大学 | Portable fruit internal-quality detection device using optical detector |
| CN103808689A (en) * | 2014-02-21 | 2014-05-21 | 山东省农业科学院农业质量标准与检测技术研究所 | Five-point near infrared fruit maturity and quality detector |
| CN106018320A (en) * | 2015-10-26 | 2016-10-12 | 沈阳农业大学 | Carotenoid detection method based on near infrared spectroscopy analysis |
| CN110501301A (en) * | 2019-07-15 | 2019-11-26 | 中国农业大学 | Fruit internal quality detection method and system |
-
2007
- 2007-06-22 CN CN 200710069633 patent/CN101074927A/en active Pending
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101949834A (en) * | 2010-08-02 | 2011-01-19 | 扬州福尔喜果蔬汁机械有限公司 | Method for detecting and grading internal quality of fruits |
| CN102636455A (en) * | 2012-05-21 | 2012-08-15 | 山东理工大学 | Method for measuring hardness of agaricus bisporus by using near infrared spectrum |
| CN102788754A (en) * | 2012-08-13 | 2012-11-21 | 浙江大学 | Device and method for measuring multi-index parameters of pear |
| CN102829849A (en) * | 2012-08-13 | 2012-12-19 | 浙江大学 | Device and method for multi-index parametric measurement of pears |
| CN102788754B (en) * | 2012-08-13 | 2014-08-06 | 浙江大学 | Device and method for measuring multi-index parameters of pear |
| CN103308457B (en) * | 2013-04-10 | 2015-01-28 | 浙江工商大学 | Establishment method of prediction model for bergamot pear maturity |
| CN103308457A (en) * | 2013-04-10 | 2013-09-18 | 浙江工商大学 | Establishment method of prediction model for bergamot pear maturity |
| CN103472011A (en) * | 2013-09-20 | 2013-12-25 | 华东交通大学 | Portable fruit internal-quality detection device using optical detector |
| CN103808689A (en) * | 2014-02-21 | 2014-05-21 | 山东省农业科学院农业质量标准与检测技术研究所 | Five-point near infrared fruit maturity and quality detector |
| CN106018320A (en) * | 2015-10-26 | 2016-10-12 | 沈阳农业大学 | Carotenoid detection method based on near infrared spectroscopy analysis |
| CN106018320B (en) * | 2015-10-26 | 2019-02-12 | 沈阳农业大学 | A kind of carotenoid detection method based on near-infrared spectroscopy |
| CN110501301A (en) * | 2019-07-15 | 2019-11-26 | 中国农业大学 | Fruit internal quality detection method and system |
| CN110501301B (en) * | 2019-07-15 | 2020-11-06 | 中国农业大学 | Fruit internal quality detection method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101074927A (en) | Method for diagnosing fruit diseases based on visible and near-infrared spectral characteristic band | |
| CN102879353B (en) | The method of content of protein components near infrared detection peanut | |
| CN101915744B (en) | Near infrared spectrum nondestructive testing method and device for material component content | |
| CN103983595B (en) | A kind of water turbidity calculation method processed based on ultraviolet-visible spectrum | |
| CN102590129B (en) | Method for detecting content of amino acid in peanuts by near infrared method | |
| CN101221125A (en) | Method of Measuring Characteristic Parameters of Eutrophic Water Body Using Spectral Technology | |
| CN103234922A (en) | Rapid soil organic matter detection method based on large sample soil visible-near infrared spectrum classification | |
| CN102636450A (en) | Method for detecting wolfberry polyose content in Chinese wolfberry in a nondestructive way based on near infrared spectrum technology | |
| CN1831516A (en) | Non-destructive identification of cigarette varieties and authenticity using visible light and near-infrared spectroscopy | |
| CN111380809B (en) | A method for testing oil film types based on polarization characteristics | |
| CN101738383A (en) | Mid-infrared spectrum-based method for quickly detecting contents of heavy metal elements in soil | |
| CN103487397B (en) | A kind of thunder bamboo shoots hardness method for quick and device | |
| CN101275912A (en) | A kind of liquid food browning detection method | |
| CN102507480A (en) | Method for nondestructively and quickly measuring moisture content of tea leaf based on 12 characteristic wavelengths | |
| CN102937575B (en) | Watermelon sugar degree rapid modeling method based on secondary spectrum recombination | |
| CN101074926A (en) | Method and system for diagnosing plant-leaf or crown botrytis of visible and near-infrared spectral | |
| CN101769866A (en) | Device for detecting milk components and method thereof | |
| CN101074925A (en) | Method for diagnosing plant-leaf botrytis in visible and near-infrared spectral characteristic band | |
| CN101614663A (en) | Identification method of bee pollen species by visible and near-infrared spectroscopy | |
| CN104255118A (en) | Rapid lossless testing method based on near infrared spectroscopy technology for paddy rice seed germination percentage | |
| CN105259136B (en) | Near infrared correction without measuring point temperature correction | |
| CN110376154A (en) | Fruit online test method and system based on spectrum correction | |
| CN108398400B (en) | A method for non-destructive detection of fatty acid content in wheat by terahertz imaging | |
| CN110231306A (en) | A kind of method of lossless, the quick odd sub- seed protein content of measurement | |
| CN1900697A (en) | Near infrared spectrum quick detecting technique for E.coli |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Open date: 20071121 |