CN111414791A - Rice seed type identification method and device based on laser-induced breakdown spectroscopy inverse Fourier transform - Google Patents
Rice seed type identification method and device based on laser-induced breakdown spectroscopy inverse Fourier transform Download PDFInfo
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Abstract
The invention discloses a rice seed type identification method and device based on laser-induced breakdown spectroscopy inverse Fourier transform, wherein the method comprises the following steps: measuring the laser-induced breakdown spectrum of the standard rice seeds; carrying out noise reduction treatment on the laser-induced breakdown spectrum of the standard rice seeds; carrying out Fourier inverse transformation on the laser induced breakdown spectrum subjected to noise reduction on the standard rice seeds to obtain time domain signals of the laser induced breakdown spectrum; establishing a rice seed type identification model by adopting a BP neural network method according to the time domain signal; measuring the laser induced breakdown spectrum of the rice seed sample to be detected, and carrying out noise reduction treatment on the laser induced breakdown spectrum of the rice seed to be detected; carrying out Fourier inverse transformation on the laser induced breakdown spectrum of the rice seeds to be detected after noise reduction; and finally, identifying the type of the rice seed to be detected according to the rice seed type identification model. The method provided by the invention can reduce the influence of spectral noise and weak intensity spectral lines on the identification effect and improve the identification effect of the rice seed type.
Description
Technical Field
The invention relates to the technical field of rice seed type identification, in particular to a rice seed type identification method based on laser-induced breakdown spectroscopy inverse Fourier transform.
Background
The rice is one of the most important grain crops in China, at present, the sowing area of the rice in China accounts for 1/4 of the total grain crop area, but the yield of the rice accounts for 1/2 of the total grain crop yield in China. The fluctuation of the rice yield can directly influence the grain supply and threaten the grain safety in China. Identification of rice seed type is an important problem in agricultural production, crop breeding and seed inspection at present. Due to the increasing phenomena of counterfeit and shoddy seeds for sale and production, the economic loss caused each year is huge. Meanwhile, because the identification of the types of rice seeds is difficult, huge economic losses are caused due to the fact that varieties are mistaken and the purity is poor every year. Therefore, the identification of rice seed types is increasingly paid attention by the departments of seed quality inspection, rice breeding research and other units.
At present, the common methods for identifying the types of rice seeds at home and abroad mainly comprise a morphological method, a chemical identification method, an electrophoresis identification method, a seedling identification method, a field plot planting identification method, a DNA molecular marking method and the like. However, the above-mentioned methods for identifying rice varieties have some disadvantages, such as: the precision is not high, the operation process is complicated, the efficiency is low, non-professionals are difficult to perform, and the method is not suitable for batch analysis, nondestructive online detection and the like of samples. Therefore, it is necessary to establish a set of simple, fast, efficient, economical and highly accurate rice variety identification technology.
The spectrum technology is more and more widely applied in various industries due to the characteristics of rapidness, wide coverage and the like, and the development of the spectrum technology provides a new technical means for identifying the types of rice seeds. The method for identifying the rice varieties by using the spectrum has the characteristics of high speed, high efficiency, low cost, good test reproducibility, convenience in measurement and the like. At present, the method for identifying the type of rice seeds by using the spectrum technology mainly comprises the following steps: visible/near infrared spectroscopy, photoluminescence spectroscopy, diffuse reflectance spectroscopy, hyperspectral imaging techniques, laser-induced breakdown spectroscopy, and the like. However, the current spectral identification methods directly combine spectral signals with chemical analysis methods to identify the types of rice seeds. The recognition effect is not ideal because noise and a large number of spectra with weak intensity have a large influence on the recognition.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a rice seed type identification method based on laser-induced breakdown spectrum inverse Fourier transform.
In order to achieve the purpose, the invention specifically adopts the following technical scheme:
the invention provides a rice seed type identification method based on laser-induced breakdown spectroscopy inverse Fourier transform, which comprises the following steps:
s1, measuring the laser-induced breakdown spectrum of the standard rice seeds;
s2, carrying out noise reduction treatment on the laser-induced breakdown spectrum of the standard rice seeds;
s3, performing Fourier inverse transformation on the laser-induced breakdown spectrum subjected to noise reduction of the standard rice seeds to obtain time domain signals of the laser-induced breakdown spectrum;
s4, establishing a rice seed type identification model by adopting a BP neural network method according to the standard rice seed induced breakdown spectrum time domain signal obtained after the Fourier inverse transformation;
s5, measuring the laser induced breakdown spectrum of the rice seed sample to be measured, wherein the measured spectrum range is the same as that in the step S1;
s6, carrying out noise reduction treatment on the laser-induced breakdown spectrum of the rice seeds to be detected, wherein the adopted noise reduction treatment method is the same as that in the step S2;
s7, performing inverse Fourier transform on the denoised laser induced breakdown spectrum of the rice seed to be detected, wherein the adopted inverse Fourier transform processing method is the same as that in the step S3, and obtaining a time domain signal of the induced breakdown spectrum of the rice seed to be detected;
and S8, identifying the type of the rice seed to be detected by adopting a BP neural network method according to the induced breakdown spectrum time domain signal of the rice seed to be detected obtained after the Fourier inverse transformation and the rice seed type identification model established in the step S4.
Preferably, in step S1, N standard rice varieties are determined, N is equal to or greater than 5, M samples are taken from each standard rice variety for measurement, and M is equal to or greater than 5.
Preferably, in the steps S1 and S5, the wavelength ranges of the laser-induced breakdown spectra measured by the laser-induced breakdown spectrometer are both 230 nm to 850 nm.
Preferably, the standard rice seed sample is: full, no mildew, basically similar size and shape, and removing impurities and mixed seeds.
Preferably, the method for establishing the rice seed type identification model in step S4 is a neural network method, a partial least squares method or a support vector machine method.
In a second aspect of the present invention, there is provided a rice seed type identification device based on inverse fourier transform of laser-induced breakdown spectroscopy, the device comprising:
a spectrum measurement module: the device is used for measuring the laser-induced breakdown spectrum of a standard rice seed or a rice seed sample to be measured;
standard rice seed treatment module: carrying out noise reduction treatment on the laser-induced breakdown spectrum of the standard rice seeds; carrying out inverse Fourier transform on the laser induced breakdown spectrum after the noise reduction of the standard rice seeds to obtain time domain signals of the standard rice seed induced breakdown spectrum;
a model building module: establishing a rice seed type identification model by adopting a BP neural network method according to a standard rice seed induced breakdown spectrum time-domain signal obtained after Fourier inverse transformation;
the rice seed treatment module that awaits measuring: carrying out noise reduction treatment on the laser-induced breakdown spectrum of the rice seeds to be detected, wherein the adopted noise reduction treatment method is the same as that in the standard rice seed treatment module; performing inverse Fourier transform on the denoised laser induced breakdown spectrum of the rice seed to be detected, wherein the adopted inverse Fourier transform processing method is the same as that in the standard rice seed processing module, and obtaining a time domain signal of the induced breakdown spectrum of the rice seed to be detected;
a type identification module: and the model establishing module is used for identifying the type of the rice seed to be detected by combining the rice seed type identification model established by the model establishing module according to the induced breakdown spectrum time-domain signal of the rice seed to be detected obtained after the Fourier inverse transformation.
Preferably, in the spectrum measurement module, when the spectrum of the standard rice seed and the spectrum of the rice seed to be measured are measured by using the laser-induced breakdown spectrometer, the wavelength ranges of the two spectra are the same.
Compared with the prior art, the invention has the beneficial effects that: the method provided by the invention is that the laser induced breakdown spectrum after the noise reduction of the rice seeds is subjected to Fourier inverse transformation to obtain time domain signals of the rice seeds, and then the time domain signals are combined with a chemical analysis method to construct a rice seed type identification model. Because the laser-induced breakdown spectrum is subjected to inverse Fourier transform, the influence of spectral noise and weak intensity spectral lines on the identification effect can be reduced, and the method provided by the invention can improve the identification effect of the rice seed type.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the technical description of the present invention will be briefly introduced below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive labor.
Fig. 1 is a schematic flow chart of a rice seed type identification method based on inverse fourier transform of laser-induced breakdown spectroscopy according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a rice seed type identification device based on laser-induced breakdown spectroscopy inverse fourier transform according to an embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention provides a rice seed type identification method based on inverse fourier transform of laser-induced breakdown spectroscopy, which includes the following steps:
s1, measuring the laser-induced breakdown spectra of the N standard rice seeds in the range of 230-850 nm.
Determining N standard rice varieties according to the rice varieties commonly used in each place, wherein N is not less than 5. The larger N is, the more rice types can be identified, and the better the identification effect is.
According to the performance parameters and the actual requirements of the used test instrument, different laser-induced breakdown spectrum wavelength ranges can be selected, such as: 250-800nm wavelength range.
In order to better construct a rice seed type identification model later, M samples can be taken for each type of standard rice seed sample to carry out measurement, and M is not less than 5. When the M value is larger, the constructed rice seed type identification model has better effect.
S2, carrying out noise reduction treatment on the laser-induced breakdown spectrum of the standard rice seeds.
And S3, performing Fourier inverse transformation on the laser-induced breakdown spectrum subjected to noise reduction of the standard rice seeds to obtain time domain signals of the laser-induced breakdown spectrum.
Since the measured laser-induced breakdown spectrum is a discrete signal, when performing the inverse fourier transform on the laser-induced breakdown spectrum, the inverse discrete fourier transform is used for processing.
S4, establishing a rice seed type identification model by adopting a BP neural network method according to the standard rice seed induced breakdown spectrum time domain signal obtained after the Fourier inverse transformation;
besides the BP neural network method for identifying the type of the sample, other algorithms such as a partial least square method, a support vector machine and the like can be adopted, but the key point is that the input signal is time domain signal data obtained by performing inverse Fourier transform on a laser-induced breakdown spectrum.
S5, measuring the laser induced breakdown spectrum of the rice seed sample to be detected within the range of 230-850nm, wherein the spectral range is the same as that in S1.
According to the performance parameters and the actual requirements of the used test instrument, different laser-induced breakdown spectrum wavelength ranges can be selected, such as: 250-800nm wavelength range. However, the wavelength range of the laser-induced breakdown spectrum of the rice to be identified tested in the step is the same as that of the standard rice sample tested in the step S1.
S6, carrying out noise reduction treatment on the laser induced breakdown spectrum of the rice seeds to be detected, wherein the noise reduction treatment method is the same as that in the step S2.
And S7, performing inverse Fourier transform on the denoised laser-induced breakdown spectrum of the rice seed to be detected, wherein the inverse Fourier transform processing method is the same as that in the step S3.
Since the measured laser-induced breakdown spectrum is a discrete signal, when performing the inverse fourier transform on the laser-induced breakdown spectrum, the inverse discrete fourier transform is used for processing.
And S8, identifying the type of the rice seed to be detected by adopting a BP neural network method according to the induced breakdown spectrum time domain signal of the rice seed to be detected obtained after the Fourier inverse transformation and the rice seed type identification model established in the step S4.
Besides the BP neural network method for identifying the type of the sample, other algorithms such as a partial least square method, a support vector machine and the like can be adopted, but the key point is that the input signal adopts laser-induced breakdown spectroscopy to perform Fourier inverse transformation to obtain time domain signal data. Next, the algorithm used in this step should be the same as the algorithm in step S4, and the model thereof is the model constructed in step S4.
Referring to fig. 2, the present invention provides a rice seed type identification device based on inverse fourier transform of laser-induced breakdown spectroscopy, the device comprising:
the spectral measurement module 210: the device is used for measuring the laser-induced breakdown spectrum of a standard rice seed or a rice seed sample to be measured; in the spectrum measurement module, when the spectra of the standard rice seed and the rice seed to be measured are measured by using the laser-induced breakdown spectrometer, the wavelength ranges of the spectra are the same.
Standard rice seed treatment module 220: carrying out noise reduction treatment on the laser-induced breakdown spectrum of the standard rice seeds; carrying out inverse Fourier transform on the laser induced breakdown spectrum after the noise reduction of the standard rice seeds to obtain time domain signals of the standard rice seed induced breakdown spectrum;
the model building module 230: establishing a rice seed type identification model by adopting a BP neural network method according to a standard rice seed induced breakdown spectrum time-domain signal obtained after Fourier inverse transformation;
the rice seed treatment module 240 to be tested: carrying out noise reduction treatment on the laser-induced breakdown spectrum of the rice seeds to be detected, wherein the adopted noise reduction treatment method is the same as that in the standard rice seed treatment module; performing inverse Fourier transform on the denoised laser induced breakdown spectrum of the rice seed to be detected, wherein the adopted inverse Fourier transform processing method is the same as that in the standard rice seed processing module, and obtaining a time domain signal of the induced breakdown spectrum of the rice seed to be detected;
the type identification module 250: and the model establishing module is used for identifying the type of the rice seed to be detected by combining the rice seed type identification model established by the model establishing module according to the induced breakdown spectrum time-domain signal of the rice seed to be detected obtained after the Fourier inverse transformation.
The above apparatus embodiments and method embodiments are in one-to-one correspondence, and reference may be made to the method embodiments for a brief point of the apparatus embodiments.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, similar parts between the embodiments are referred to, and parts not described in the specification are all the prior art or common general knowledge.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in random access memory, read only memory, electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
Although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (7)
1. A rice seed type identification method based on laser-induced breakdown spectroscopy inverse Fourier transform is characterized by comprising the following steps:
s1, measuring the laser-induced breakdown spectrum of the standard rice seeds;
s2, carrying out noise reduction treatment on the laser-induced breakdown spectrum of the standard rice seeds;
s3, performing Fourier inverse transformation on the laser-induced breakdown spectrum subjected to noise reduction of the standard rice seeds to obtain time domain signals of the laser-induced breakdown spectrum;
s4, establishing a rice seed type identification model by adopting a BP neural network method according to the standard rice seed induced breakdown spectrum time domain signal obtained after the Fourier inverse transformation;
s5, measuring the laser induced breakdown spectrum of the rice seed sample to be measured, wherein the measured spectrum range is the same as that in the step S1;
s6, carrying out noise reduction treatment on the laser-induced breakdown spectrum of the rice seeds to be detected, wherein the adopted noise reduction treatment method is the same as that in the step S2;
s7, performing inverse Fourier transform on the denoised laser induced breakdown spectrum of the rice seed to be detected, wherein the adopted inverse Fourier transform processing method is the same as that in the step S3, and obtaining a time domain signal of the induced breakdown spectrum of the rice seed to be detected;
and S8, identifying the type of the rice seed to be detected by adopting a BP neural network method according to the induced breakdown spectrum time domain signal of the rice seed to be detected obtained after the Fourier inverse transformation and the rice seed type identification model established in the step S4.
2. The method for identifying rice seed types through laser-induced breakdown spectroscopy inverse Fourier transform as recited in claim 1, wherein in the step S1, N standard rice varieties are determined, wherein N is larger than or equal to 5, M samples are taken from each standard rice variety for measurement, and M is larger than or equal to 5.
3. The method for identifying rice seed types through inverse Fourier transform of laser-induced breakdown spectroscopy as claimed in claim 1, wherein in steps S1 and S5, the wavelength ranges of the laser-induced breakdown spectroscopy measured by the laser-induced breakdown spectroscopy are 230-850 nm.
4. The method for identifying rice seed types based on the inverse Fourier transform of laser-induced breakdown spectroscopy as claimed in claim 1, wherein the standard rice seed samples are: full, no mildew, basically similar size and shape, and removing impurities and mixed seeds.
5. The method for identifying rice seed types through inverse laser-induced breakdown spectroscopy Fourier transform of claim 1, wherein the method for establishing the rice seed type identification model in the step S4 is a neural network method, a partial least squares method or a support vector machine method.
6. A rice seed type identification device based on laser-induced breakdown spectroscopy (LIBS) inverse Fourier transform, the device comprising:
a spectrum measurement module: the device is used for measuring the laser-induced breakdown spectrum of a standard rice seed or a rice seed sample to be measured;
standard rice seed treatment module: carrying out noise reduction treatment on the laser-induced breakdown spectrum of the standard rice seeds; carrying out inverse Fourier transform on the laser induced breakdown spectrum after the noise reduction of the standard rice seeds to obtain time domain signals of the standard rice seed induced breakdown spectrum;
a model building module: establishing a rice seed type identification model by adopting a BP neural network method according to a standard rice seed induced breakdown spectrum time-domain signal obtained after Fourier inverse transformation;
the rice seed treatment module that awaits measuring: carrying out noise reduction treatment on the laser-induced breakdown spectrum of the rice seeds to be detected, wherein the adopted noise reduction treatment method is the same as that in the standard rice seed treatment module; performing inverse Fourier transform on the denoised laser induced breakdown spectrum of the rice seed to be detected, wherein the adopted inverse Fourier transform processing method is the same as that in the standard rice seed processing module, and obtaining a time domain signal of the induced breakdown spectrum of the rice seed to be detected;
a type identification module: and the model establishing module is used for identifying the type of the rice seed to be detected by combining the rice seed type identification model established by the model establishing module according to the induced breakdown spectrum time-domain signal of the rice seed to be detected obtained after the Fourier inverse transformation.
7. The method for identifying rice seed types through inverse Fourier transform of laser-induced breakdown spectroscopy as claimed in claim 6, wherein in the spectrum measurement module, when the spectra of the standard rice seed and the rice seed to be measured are measured by the laser-induced breakdown spectroscopy, the wavelength ranges of the spectra of the standard rice seed and the spectra of the rice seed to be measured are the same.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105181678A (en) * | 2015-09-07 | 2015-12-23 | 长江大学 | Identification method of rice varieties based on laser-induced breakdown spectroscopy (LIBS) |
| CN108444953A (en) * | 2018-03-13 | 2018-08-24 | 长江大学 | Rice varieties method for quick identification based on laser induced breakdown spectroscopy differential signal |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105181678A (en) * | 2015-09-07 | 2015-12-23 | 长江大学 | Identification method of rice varieties based on laser-induced breakdown spectroscopy (LIBS) |
| CN108444953A (en) * | 2018-03-13 | 2018-08-24 | 长江大学 | Rice varieties method for quick identification based on laser induced breakdown spectroscopy differential signal |
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