CN205910119U - Plant leaf water content detecting system based on terahertz wave - Google Patents
Plant leaf water content detecting system based on terahertz wave Download PDFInfo
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- CN205910119U CN205910119U CN201620631379.0U CN201620631379U CN205910119U CN 205910119 U CN205910119 U CN 205910119U CN 201620631379 U CN201620631379 U CN 201620631379U CN 205910119 U CN205910119 U CN 205910119U
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title abstract description 29
- 238000001228 spectrum Methods 0.000 claims abstract description 70
- 238000001514 detection method Methods 0.000 claims abstract description 35
- 239000004698 Polyethylene Substances 0.000 claims description 9
- -1 polyethylene Polymers 0.000 claims description 9
- 229920000573 polyethylene Polymers 0.000 claims description 9
- 230000006378 damage Effects 0.000 abstract description 3
- 230000003595 spectral effect Effects 0.000 abstract 3
- 208000027418 Wounds and injury Diseases 0.000 abstract 1
- 238000011161 development Methods 0.000 abstract 1
- 230000018109 developmental process Effects 0.000 abstract 1
- 208000014674 injury Diseases 0.000 abstract 1
- 238000000034 method Methods 0.000 description 47
- 238000004611 spectroscopical analysis Methods 0.000 description 14
- 238000001328 terahertz time-domain spectroscopy Methods 0.000 description 8
- 238000005259 measurement Methods 0.000 description 6
- 230000008569 process Effects 0.000 description 4
- 230000002354 daily effect Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000003384 imaging method Methods 0.000 description 3
- 238000004590 computer program Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000000643 oven drying Methods 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 1
- 238000000862 absorption spectrum Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 239000000428 dust Substances 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 230000012010 growth Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 230000002262 irrigation Effects 0.000 description 1
- 238000003973 irrigation Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
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- 230000001105 regulatory effect Effects 0.000 description 1
- 239000011435 rock Substances 0.000 description 1
- 238000009738 saturating Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3554—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for determining moisture content
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3581—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using far infrared light; using Terahertz radiation
- G01N21/3586—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using far infrared light; using Terahertz radiation by Terahertz time domain spectroscopy [THz-TDS]
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N2021/8466—Investigation of vegetal material, e.g. leaves, plants, fruits
Abstract
The utility model relates to a plant leaf water content detecting system based on terahertz wave, include: set gradually the terahertz now spectrum produce device, terahertz spectrum emission device, sample fixing device and terahertz spectral detection device now, terahertz spectrum production now device is used for producing laser pulse, terahertz spectrum emission device now is used for producing terahertz pulse now under laser pulse's of the arousing to with the terahertz now the pulse directive wait the plant leaf that detects, sample fixing device is used for the fixed plant leaf who treats the detection, the terahertz now the spectral detection device be used for receiving after waiting the plant leaf who detects terahertz pulse now and terahertz now spectrum produce the laser pulse that the device produced, the terahertz of being treated each point of plant leaf of detection is spectral data now. The utility model provides a technical scheme need not to take the plant leaf that awaits measuring, has realized the harmless on -line measuring of developments of plant leaf water content, can not cause the injury to the plant, and measuring time is short, the step is simple, has improved detection efficiency.
Description
Technical field
This utility model is related to detection technique field, more particularly, to a kind of plant leaf blade water content inspection based on THz wave
Examining system.
Background technology
Water content in plant leaf is one of important regulatory factor of impact plant growing, to moisture in plant leaf blade
Carry out dynamic detection, during timely coordinate plant growth, moisture irrigation volume is significant.When water content in plant leaf blade
During reduction, if can not carry out in time irrigating, plant insufficient water can be led to and poor growth, serious meeting is withered, greatly
The yield of impact crop.If plant leaf blade water content raises, should postpone irrigating, in order to avoid excess moisture occurs leading to plant
Strain is dead.
Detection to plant leaf blade water content is all the method taking destruction at present, is calculated using weighting method after dried,
But oven drying method is that have loss measurement, sample need to be destroyed, and time of measuring is very long, complex steps.Then occur in that the instrument of water content
Measurement method, has Microwave Water point-score, electric capacity moisture method, Neutron Moisture method, electrode water point-score etc..To plant leaf blade moisture measurement
Conventional method be oven drying method, capacitance method, Electromagnetic Wave Method etc., but these methods typically have destructiveness, noncontinuity, and
Time consuming.
Utility model content
For solving existing plant leaf blade water content method, there is destructiveness, time of measuring length, the defect of complex steps, provide
A kind of lossless plant leaf blade moisture content detection system based on THz wave.
For this purpose it is proposed, the utility model proposes a kind of plant leaf blade moisture content detection system based on THz wave, bag
Include: set gradually tera-hertz spectra generator, tera-hertz spectra discharger, sample fixing device and tera-hertz spectra and detect
Device；
Described tera-hertz spectra generator is used for producing laser pulse；
Described tera-hertz spectra discharger is used for generation terahertz pulse under the exciting of described laser pulse, and by institute
State terahertz pulse directive plant leaf blade to be detected；
Described sample fixing device is used for fixing described plant leaf blade to be detected；
Described tera-hertz spectra detection device is used for receiving the terahertz pulse after described plant leaf blade to be detected
The laser pulse producing with described tera-hertz spectra generator, obtains the Terahertz of described plant leaf blade every bit to be detected
Spectroscopic data.
Preferably, described tera-hertz spectra discharger includes: terahertz transmitter and the first lens；Described first lens
Between described terahertz transmitter and described sample fixing device；
Described terahertz transmitter is used for generation terahertz pulse under the exciting of described laser pulse；Described first lens
For described terahertz pulse is focused on described plant leaf blade to be detected.
Preferably, described sample fixing device is polyethylene board.
Preferably, described tera-hertz spectra generator is femto-second laser.
Preferably, described tera-hertz spectra detection device includes the second lens and terahertz detector；Described second lens
Between described sample fixing device and described terahertz detector；
Described second lens are used for for the terahertz pulse through described plant leaf blade to be detected focusing on described terahertz
The hereby porch of detector；Described terahertz detector is used for detecting the laser pulse intensity receiving and after plant leaf blade
Terahertz pulse intensity, generate the terahertz light modal data of plant leaf blade every bit to be detected.
The plant leaf blade moisture content detection system based on THz wave that this utility model provides, treats measuring plants by collection
The terahertz light modal data of blade, according to the corresponding relation between plant leaf blade water content and terahertz light modal data it is possible to
Obtain the water content of plant leaf blade to be measured, and plant leaf blade to be measured need not be taken it is achieved that plant leaf blade is aqueous from plant
The dynamic lossless on-line checking of amount, will not damage to plant, time of measuring is short, step is simple, improves detection efficiency.
Brief description
Feature and advantage of the present utility model can be more clearly understood from by reference to accompanying drawing, accompanying drawing be schematic and
Should not be construed as carrying out any restriction to this utility model, in the accompanying drawings:
The framework of the plant leaf blade moisture content detection system based on THz wave that Fig. 1 provides for this utility model is illustrated
Figure；
Fig. 2 provides the aqueous quantity detecting system of the plant leaf blade based on THz wave of another embodiment offer for this utility model
The block schematic illustration of system；
Fig. 3 is the method carrying out leaf water content detection using the system that this utility model provides；
The absorption spectrum schematic diagram of the polyethylene board that Fig. 4 provides for this utility model；
Fig. 5 a-5b is the schematic diagram of blade terahertz time-domain reconstructed image；
Fig. 6 a-6f is the schematic diagram of the reconstructed image of blade under different frequency.
Specific embodiment
Below in conjunction with accompanying drawing, embodiment of the present utility model is described in detail.
As shown in figure 1, a kind of plant leaf blade moisture content detection system based on THz wave, comprising: set gradually terahertz
Hereby spectrum generator 11, tera-hertz spectra discharger 12, sample fixing device 13 and tera-hertz spectra detection device 14；
Described tera-hertz spectra generator 11 is used for producing laser pulse；
Described tera-hertz spectra discharger 12 is used for generation terahertz pulse under the exciting of described laser pulse, and will
Described terahertz pulse directive plant leaf blade to be detected；
Described sample fixing device 13 is used for fixing described plant leaf blade to be detected；
Described tera-hertz spectra detection device is used for receiving the terahertz pulse after described plant leaf blade to be detected
The laser pulse producing with described tera-hertz spectra generator 11, obtains the terahertz of described plant leaf blade every bit to be detected
Hereby spectroscopic data.
It should be noted that it is every to can be used for plant leaf blade based on the plant leaf blade moisture content detection system of THz wave
The collection of the terahertz light modal data of a bit, can obtain plant leaf blade according to the terahertz light modal data of plant leaf blade every bit
Tera-hertz spectra meansigma methodss, then according to pre-building with regard to tera-hertz spectra meansigma methodss and plant leaf blade water content pair
The moisture forecast model that should be related to, the plant leaf blade corresponding to terahertz light modal data currently being gathered aqueous
Amount.Therefore by the terahertz light modal data of the system acquisition plant leaf blade to be measured using this utility model offer it is possible to obtain
To the water content of plant leaf blade to be measured, and plant leaf blade to be measured need not be taken from plant it is achieved that plant leaf blade water content
Dynamic lossless on-line checking, plant will not be damaged, time of measuring is short, step is simple, improves detection efficiency.
Preferably, as shown in Fig. 2 described tera-hertz spectra discharger 12 includes: terahertz transmitter and the first lens；
Described first lens are located between described terahertz transmitter and described sample fixing device 13；
Described terahertz transmitter is used for generation terahertz pulse under the exciting of described laser pulse；Described first lens
For described terahertz pulse is focused on described plant leaf blade to be detected.
Preferably, described sample fixing device 13 is polyethylene board.
Preferably, described tera-hertz spectra generator 11 is femto-second laser.
Preferably, described tera-hertz spectra detection device 14 includes the second lens and terahertz detector；Described second is saturating
Mirror is located between described sample fixing device 13 and described terahertz detector；
Described second lens are used for for the terahertz pulse through described plant leaf blade to be detected focusing on described terahertz
The hereby porch of detector；Described terahertz detector is used for detecting the laser pulse intensity receiving and after plant leaf blade
Terahertz pulse intensity, generate the terahertz light modal data of plant leaf blade every bit to be detected.
Below, the plant leaf blade moisture content detection system based on THz wave being provided using above-described embodiment, is planted
The detection of thing leaf water content, specifically includes below step, as shown in figure 3,
S1: obtain the moisture of described plant leaf blade sample；
Wherein, before obtaining the moisture of plant leaf blade sample, pretreatment is carried out to blade, specifically, by plant leaf blade
After sample picks, with napkin etc. by blade surface wiped clean, remove the dust of blade surface, it is to avoid other materials are to leaf
The terahertz imaging spectrum of piece interferes, and weighs leaf quality m1And record, the blade after measurement is placed on clean pollution-free
Place, daily repeated measure step afterwards, continuous measure, weighed quality m of blade daily2, m3... ..., last
After measurement in a day finishes, blade is placed under 110 DEG C of environment and dries 20 minutes, weigh weight m of dry matter weight of leaf0, calculate every
Leaf quality is of poor quality with last day within one day, △ m1=m1-m0, △ m2=m2-m0... ..., obtain leaf water every day and contain
Amount △ m1, △ m2... ...；
S2: obtain the terahertz light modal data of plant leaf blade sample every bit, obtain tera-hertz spectra meansigma methodss；
Specifically, while daily measurement leaf water content, using the offer of this utility model embodiment based on terahertz
Hereby the tera-hertz spectra imaging data of the plant leaf blade moisture content detection system daily herborization blade of ripple, obtains multigroup blade
Water content and corresponding terahertz light modal data, wherein it is possible to multi-group data is divided into calibration set and forecast set；Wherein, gather
Before and after blade tera-hertz spectra imaging data, measure leaf quality simultaneously, keep synchronicity, dynamic for accurate acquisition leaf water
The spectroscopic data of change procedure and moisture data provide safeguard；Before collection terahertz light modal data, one can be measured
The tera-hertz spectra of the polyethylene background plate of lower fixing plant leaf blade sample, it is determined whether shadow is caused to the spectroscopic data of blade
Ring, measure the polyethylene board absorptance that obtains as shown in figure 4, under Terahertz frequency range, polyethylene board be almost transparent (i.e.
THz wave is no absorbed), polyethylene board can serve as measuring the background board of blade.In the terahertz light time spectrum of collection blade,
Secure the vanes on this polyethylene background plate, it is to avoid blade rocks the interference that measurement is produced, setting scans the initial of blade
Point (x0, y0) and sweep stopping point (x1, y1) it is ensured that in two-dimension translational platform moving process, scanning and obtain intact leaf image,
Then start array scanning blade, preserve the terahertz light modal data of blade.
S3: according to described tera-hertz spectra meansigma methodss and described moisture, set up moisture forecast model；
S4: obtain the tera-hertz spectra meansigma methodss of plant leaf blade to be measured, will be average for the tera-hertz spectra of plant leaf blade to be measured
Value inputs described moisture forecast model, obtains the moisture of described plant leaf blade to be measured.
By pre-building the moisture forecast model between plant leaf blade sample moisture content and tera-hertz spectra, after
Continuing can be according to this moisture forecast model set up, by the system acquisition leaves of plants to be measured being provided using this utility model
The terahertz light modal data of piece, and need not be by plant leaf blade to be measured from plant it is possible to obtain the water content of plant leaf blade to be measured
On take the dynamic lossless on-line checking it is achieved that plant leaf blade water content, plant will not be damaged, time of measuring is short,
Step is simple, improves detection efficiency.
Wherein, step s2 obtains the terahertz light modal data of plant leaf blade sample every bit, obtains tera-hertz spectra average
Value, comprising:
Obtain terahertz time-domain spectroscopy data and the frequency domain spectra data of plant leaf blade sample every bit；
According to the terahertz time-domain spectroscopy data of described plant leaf blade sample every bit, obtain described plant not in the same time
The terahertz time-domain spectroscopy meansigma methodss of blade sample；
According to the Terahertz frequency domain spectra data of described plant leaf blade sample every bit, obtain the described plant under different frequency
The Terahertz frequency domain spectra meansigma methodss of the absorptance of thing blade sample.
Specifically, terahertz time-domain spectroscopy instrument collects the time-domain spectroscopy data of blade every bit, every for blade
A bit, in time domain respectively in t0-tnBetween effectively in the Terahertz period, and be spaced the time domain of test step-length as feature, calculate respectively
The not spectrum mean value of lower blade magnitude image in the same time.Time-domain spectroscopy data is carried out Fourier transformation, and can to obtain blade every
The frequency domain value of a bit, in order to eliminate the impact to frequency domain data for the vane thickness, is entered by calculating the absorptance of blade every bit
Row tera-hertz spectra image reconstruction, selects f respectively in frequency domain0-fnBetween effective Terahertz bandwidth be spaced the frequency conduct of 0.1thz
Feature, calculates the frequency domain spectra meansigma methodss of the blade absorptance image of reconstruct under different frequency respectively.
Wherein, step s3, according to described tera-hertz spectra meansigma methodss and described moisture, sets up moisture prediction mould
Type, comprising:
S301: according to not the terahertz time-domain spectroscopy meansigma methodss of plant leaf blade sample and described plant leaf blade sample in the same time
Moisture, set up the first forecast model, choose optimum time domain charactreristic parameter combination under the conditions of time domain；
S302: the Terahertz frequency domain spectra meansigma methodss according to the plant leaf blade sample under different frequency and described leaves of plants
The moisture of piece sample, sets up the second forecast model, chooses optimum frequency domain character parameter combination under frequency domain condition；
S303: combined and optimum frequency domain character parameter combination according to described optimum time domain charactreristic parameter, set up described moisture
Content prediction model.
It should be noted that in order to noise not substituted in the moisture forecast model set up, when can adopt first
Domain and frequency domain individually model, and select respectively after the best features parameter combination of time domain and frequency domain, then carry out follow-up time domain and
Frequency domain compositional modeling.
Wherein, step s301 is according to not the terahertz time-domain spectroscopy meansigma methodss of plant leaf blade sample and described plant in the same time
The moisture of blade sample, sets up the first forecast model, chooses optimum time domain charactreristic parameter combination under the conditions of time domain, comprising:
The tera-hertz spectra meansigma methodss of the plant of known moisture levels are inputted described first forecast model；
The correlation coefficient of described first forecast model relatively do not set up in the same time and root-mean-square error；
Choose the time-domain spectroscopy meansigma methodss of optimum and the moisture of corresponding plant leaf blade sample under the conditions of time domain.
Specifically, it is possible to use the calibration set spectroscopic data in step s2 is set up time-domain spectroscopy meansigma methodss and contained with corresponding blade
The multivariate regression models of the water yield, i.e. this first forecast model, by this first forecast model to the forecast set data in step s2
Be predicted, compare not the correlation coefficient of modeling method drag and root-mean-square error in the same time, choose correlation coefficient highest and
The data of the moisture of time-domain spectroscopy meansigma methodss when root-mean-square error is minimum and corresponding plant leaf blade sample.
Likewise, step s302 is according to the Terahertz frequency domain spectra meansigma methodss of the plant leaf blade sample under different frequency and institute
State the moisture of plant leaf blade sample, set up the second forecast model, choose optimum frequency domain character parameter combination under frequency domain condition,
Including:
The tera-hertz spectra meansigma methodss of the plant of known moisture levels are inputted described second forecast model；
The correlation coefficient of described second forecast model and root-mean-square error that relatively different frequency is set up；
Choose optimum frequency domain spectra meansigma methodss under frequency domain condition and corresponding plant leaf blade water content.
Specifically, the second prediction mould between frequency domain spectra meansigma methodss and leaf water content is set up using calibration set data
Type, and this model being verified using forecast set data, compares the correlation coefficient and all of different frequency modeling method drag
Square error, the frequency domain spectra meansigma methodss chosen when correlation coefficient highest and root-mean-square error minimum are aqueous with corresponding plant leaf blade
Amount data.
Specifically, step s303, obtaining optimum time domain, after frequency domain character parameter combination, set up multivariate regression models, and
Can be corrected using the terahertz light modal data of collection and predict, be commented by the model of correlation coefficient and root-mean-square error
Valency, filters out correlation coefficient highest, the mathematical model of root-mean-square error minimum combines as optimum modeling, obtains based on time domain light
Spectrum, the optimum moisture forecast model of frequency domain absorption coefficient combination.
Wherein, after obtaining the terahertz time-domain spectroscopy data of plant leaf blade sample every bit, described plant can be extracted
The Time Domain Amplitude of thing blade sample every bit time-domain spectroscopy, the Time Domain Amplitude according to described every bit is to described plant leaf blade sample
Carry out image reconstruction.Using image reconstruction, can determine that the spectroscopic data in which moment is optimal by eye-observation, beneficial to moisture
The foundation of content prediction model.As shown in Fig. 5 a-5b, t1Compare t2The blade profile in moment is clear, so t1The spectroscopic data in moment
Preferably.
Wherein, after obtaining the terahertz time-domain spectroscopy data of plant leaf blade sample every bit, during by described Terahertz
Domain spectroscopic data carries out Fourier transformation, obtains the Terahertz frequency domain spectra data of described plant leaf blade sample every bit；
According to described Terahertz frequency domain spectra data, calculate the absorptance of described plant leaf blade sample every bit, and root
Absorptance according to described plant leaf blade sample every bit carries out image reconstruction to described plant leaf blade sample.
In order to eliminate the impact to frequency domain data for the vane thickness, carried out by calculating the absorptance of plant leaf blade every bit
Tera-hertz spectra image reconstruction, as shown in Fig. 6 a-6f.Can be seen that in 1.1thz from Fig. 6 a-6f about, blade border is relatively
For clear, therefore, in 1.1thz about frequency domain spectra data preferable.Wherein, according to described Terahertz frequency domain spectra data,
Calculate the absorptance of described plant leaf blade sample every bit, using below equation:
Wherein: ω is frequency, d is the thickness of plant leaf blade sample, and ρ (ω) is the amplitude of frequency domain spectra, and n (ω) is to absorb
Coefficient,For phase place.
Afterwards it should be noted that if being related to computer program and corresponding method is equal in this utility model embodiment
It is that the method directly applying existing comparative maturity is realized, be not related to the improvement of the methods such as computer program.
It should be noted that herein, such as first and second or the like relational terms are used merely to a reality
Body or operation are made a distinction with another entity or operation, and not necessarily require or imply these entities or deposit between operating
In any this actual relation or order.And, term " inclusion ", "comprising" or its any other variant are intended to
Comprising of nonexcludability, wants so that including a series of process of key elements, method, article or equipment and not only including those
Element, but also include other key elements being not expressly set out, or also include for this process, method, article or equipment
Intrinsic key element.In the absence of more restrictions, the key element that limited by sentence "including a ..." it is not excluded that
Also there is other identical element including in the process of described key element, method, article or equipment.Term " on ", D score etc. refers to
The orientation showing or position relationship are based on orientation shown in the drawings or position relationship, be for only for ease of description this utility model and
Simplify description, rather than the device of instruction or hint indication or element must have specific orientation, with specific azimuth configuration
And operation, therefore it is not intended that to restriction of the present utility model.Unless otherwise clearly defined and limited, term " installation ",
" being connected ", " connection " should be interpreted broadly, for example, it may be being fixedly connected or being detachably connected, or integratedly connect
Connect；Can be to be mechanically connected or electrically connect；Can be to be joined directly together it is also possible to be indirectly connected to by intermediary, can
To be the connection of two element internals.For the ordinary skill in the art, can understand as the case may be above-mentioned
Concrete meaning in this utility model for the term.
In description of the present utility model, illustrate a large amount of details.Although it is understood that, of the present utility model
Embodiment can be put into practice in the case of not having these details.In some instances, be not been shown in detail known method,
Structure and technology, so as not to obscure the understanding of this description.Similarly it will be appreciated that disclosing to simplify this utility model
And help understand one or more of each inventive aspect, in the description to exemplary embodiment of the present utility model above
In, each feature of the present utility model is grouped together in single embodiment, figure or descriptions thereof sometimes.However, simultaneously
The method of the disclosure should not be explained to be in reflect an intention that i.e. this utility model required for protection requires than in each right
The more feature of feature being expressly recited in requirement.More precisely, as the following claims reflect, inventive aspect
It is all features less than single embodiment disclosed above.Therefore, it then follows claims of specific embodiment are thus
It is expressly incorporated in this specific embodiment, wherein each claim itself is as separate embodiments of the present utility model.
Last it is noted that various embodiments above is only in order to illustrating the technical solution of the utility model, rather than it is limited
System；Although being described in detail to this utility model with reference to foregoing embodiments, those of ordinary skill in the art should
Understand: it still can be modified to the technical scheme described in foregoing embodiments, or to wherein some or all of
Technical characteristic carries out equivalent；And these modifications or replacement, do not make the essence of appropriate technical solution depart from this practicality new
The scope of type each embodiment technical scheme, it all should be covered in the middle of the scope of claim of the present utility model and description.
Claims (5)
1. a kind of plant leaf blade moisture content detection system based on THz wave is it is characterised in that include: sets gradually Terahertz
Spectrum generator, tera-hertz spectra discharger, sample fixing device and tera-hertz spectra detection device；
Described tera-hertz spectra generator is used for producing laser pulse；
Described tera-hertz spectra discharger be used under the exciting of described laser pulse generation terahertz pulse, and by described too
Hertz pulse directive plant leaf blade to be detected；
Described sample fixing device is used for fixing described plant leaf blade to be detected；
Described tera-hertz spectra detection device is used for receiving the terahertz pulse after described plant leaf blade to be detected and institute
State the laser pulse of tera-hertz spectra generator generation, obtain the tera-hertz spectra of described plant leaf blade every bit to be detected
Data.
2. the plant leaf blade moisture content detection system based on THz wave according to claim 1 is it is characterised in that described
Tera-hertz spectra discharger includes: terahertz transmitter and the first lens；Described first lens are located at described terahertz sources
Between device and described sample fixing device；
Described terahertz transmitter is used for generation terahertz pulse under the exciting of described laser pulse；Described first lens are used for
Described terahertz pulse is focused on described plant leaf blade to be detected.
3. the plant leaf blade moisture content detection system based on THz wave according to claim 1 is it is characterised in that described
Sample fixing device is polyethylene board.
4. the plant leaf blade moisture content detection system based on THz wave according to claim 1 is it is characterised in that described
Tera-hertz spectra generator is femto-second laser.
5. the plant leaf blade moisture content detection system based on THz wave according to claim 1 is it is characterised in that described
Tera-hertz spectra detection device includes the second lens and terahertz detector；Described second lens are located at described sample fixing device
And described terahertz detector between；
Described second lens are used for for the terahertz pulse through described plant leaf blade to be detected focusing on described Terahertz spy
Survey the porch of device；Described terahertz detector be used for detecting the laser pulse intensity that receives and after plant leaf blade too
Hertz pulse strength, generates the terahertz light modal data of plant leaf blade every bit to be detected.
Priority Applications (1)
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| CN106018327A (en) * | 2016-06-23 | 2016-10-12 | 北京农业信息技术研究中心 | Terahertz wave based method and system for detecting water content of plant leaves |
| CN107389594A (en) * | 2017-06-30 | 2017-11-24 | 北京农业信息技术研究中心 | A kind of stem stalk terminal bud recognition positioning method and device based on terahertz light |
| CN108760838A (en) * | 2018-04-25 | 2018-11-06 | 江苏大学 | A kind of leaf water content prediction model and its method for building up based on dielectric property |
| CN110320247A (en) * | 2019-07-09 | 2019-10-11 | 中国农业大学 | Plant haulm water content damage-free measuring apparatus and method |
| CN113218908A (en) * | 2021-05-10 | 2021-08-06 | 北京理工大学 | Plant leaf water content detection method based on terahertz back scattering signal |
| CN114162065A (en) * | 2021-08-17 | 2022-03-11 | 南昌大学 | Terahertz wave-based feed water content detection method and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106018327A (en) * | 2016-06-23 | 2016-10-12 | 北京农业信息技术研究中心 | Terahertz wave based method and system for detecting water content of plant leaves |
| CN107389594A (en) * | 2017-06-30 | 2017-11-24 | 北京农业信息技术研究中心 | A kind of stem stalk terminal bud recognition positioning method and device based on terahertz light |
| CN108760838A (en) * | 2018-04-25 | 2018-11-06 | 江苏大学 | A kind of leaf water content prediction model and its method for building up based on dielectric property |
| CN110320247A (en) * | 2019-07-09 | 2019-10-11 | 中国农业大学 | Plant haulm water content damage-free measuring apparatus and method |
| CN113218908A (en) * | 2021-05-10 | 2021-08-06 | 北京理工大学 | Plant leaf water content detection method based on terahertz back scattering signal |
| CN114162065A (en) * | 2021-08-17 | 2022-03-11 | 南昌大学 | Terahertz wave-based feed water content detection method and system |
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