CN109001244A - Device and its detection method based on infrared thermal imaging technique detection Potato Quality - Google Patents
Device and its detection method based on infrared thermal imaging technique detection Potato Quality Download PDFInfo
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- CN109001244A CN109001244A CN201811043837.9A CN201811043837A CN109001244A CN 109001244 A CN109001244 A CN 109001244A CN 201811043837 A CN201811043837 A CN 201811043837A CN 109001244 A CN109001244 A CN 109001244A
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Abstract
The invention discloses a kind of devices and its detection method based on infrared thermal imaging technique detection Potato Quality, belong to detection of agricultural products field.The device includes cabinet, the sample cell for being set to bottom of box center, the computerized control system for being respectively arranged at intracorporal four headlamps of case, being set to the infrared thermal imaging detection system in top of the box center and connecting respectively with sample cell, infrared thermal imaging detection system.The present invention carries out non-destructive testing to dynamic quality during storage of potato using thermal imaging, and it is fast to can be very good to reduce the dimension of image feature information, detection speed using the technology, and can be detected on the spot in storage of potato.
Description
Technical field
The present invention relates to the surface detection devices of sample, more particularly to a kind of infrared thermal imaging technique that is based on is for horse
The non-destructive testing of bell potato storage period quality provides certain theory for the storage of potato, processing and promotion processing quality
Foundation and practical advice.
Background technique
During potato industrialization development, the intensive processing of potato is the pass of Potato Industry upgrading synergy
Key, material quality are basis and the guarantee of converted products quality.Stem tuber storage before potato processing is the pass of potato processing
One of key link.Therefore, Quality Detection is most important during understanding storage of potato.Thermal imaging (thermal
Imaging, TI) it is a kind of lossless, contactless detection system, it is generally used for the non-destructive quality evaluation of material table temperature.Heat
Imager is small in size, easy to operate, can removably dispose in sample storeroom.Meanwhile graphic images are set a song to music than infrared light
The dimension of line test sample information is more, but and the speed of service fewer than high spectrum image information dimension is fast.Therefore, thermal imaging
It applies to study in terms of food inspection and gradually increase, wherein the fruit sample stored at 1.5 DEG C is moved to 20 by Baranowski etc.
In DEG C environment (18.5 DEG C of the temperature difference), heat up 20min, determines whether fruit infects water core by the difference of heating rate.
Bulanon etc. has studied the heat transient change of citrus tree crown, fruit, and thermal imaging is used for the detection of citrus maturity.
Ginesu etc. successfully detects the foreign matter in food, such as rotten nut, shell, handstone using thermal sensation video camera.But
It is had not been reported with the research of thermal imaging detection Potato Quality.
Summary of the invention
In view of this, the object of the present invention is to provide based on infrared thermal imaging technique detection Potato Quality device and its
Detection method, for the non-destructive testing for quality during storage of potato.
An object of the present invention is achieved through the following technical solutions, and one kind is detected based on infrared thermal imaging technique
The device of Potato Quality including cabinet, the sample cell that is set to bottom of box center, is respectively arranged at intracorporal four photographs of case
Bright lamp is set to the infrared thermal imaging detection system in top of the box center and connects respectively with sample cell, infrared thermal imaging detection system
The computerized control system connect.
Further, headlamp is respectively arranged at the quadrangle of top of the box.
Further, the sample cell is rotatable sample pond.
The second object of the present invention is to what is be achieved through the following technical solutions, one kind detecting horse based on infrared thermal imaging technique
The method of bell potato quality, comprising the following steps: S1 acquires normal and rotten potato graphic images；S2 utilizes thermal image processing
The characteristic information of algorithm and each sample of artificial neural network analysis；S3 is based on force sensor technologies to normal and rotten potato
Physical characteristic carry out feature information extraction, the detection of measured biochemical method normally contains with the chemical component of rotten potato
Amount；S4 studies the physical characteristic of potato and the correlation of chemical analysis and graphic images, and finds out normal and rotten Ma Ling
Otherness between potato；S5 establishes the Quality Detection analysis model of normal, rotten potato graphic images；S6 utilizes foundation
Normally, the Quality Detection analysis model of rotten potato graphic images, to dynamic quality non-destructive testing during storage of potato
Experimental study.
Further, in the S4 specifically: the heat for shooting Normal potato and rotten potato using thermal infrared camera is red
Outer image, shooting 200 altogether, one per second, including heating in 40 seconds and 160 seconds cooling thermal infrared images；It is obtained by formula
Average temperature difference curve:
Wherein ∑ T1(x, y) indicates the temperature cumulative value of each pixel of the first width thermal infrared images, ∑ Ti(x, y) is indicated
The temperature cumulative value of each pixel of current thermal infrared images, n indicate total pixel value of thermal infrared images；
The average temperature difference curve of same potato samples different number of days is compared, the variation of potato freshness is observed；
The average temperature difference curve of same number of days difference sample is compared, the difference of Normal potato and rotten potato is observed, selection is flat
Equal temperature difference curve, carries out ANN modeling.
Further, in the S5 specifically: using noise equivalent temperature difference (NETD), minimum resolvable temperature difference (MRTD), adjust
Modulation trnasfer function (MTF) and signal transfer function (SiTF) evaluate the stability and accuracy of thermal imaging system；
It is examined using F and T is examined and chosen suitable mean temperature difference data progress ANN modeling, wherein 2/3 modeling data is defeated
Enter and be trained and verify in Matlab, establishes ANN model；1/3 modeling data carries out last ANN model verifying, and verifying is lost
It loses to come back in Matlab and be trained, be proved to be successful then finally modeling achievement, and carried out pair with the biochemical method of standard
Than the accuracy for verifying model.
Further, the physical characteristic includes weight, density and hardness.
Further, the chemical component includes dry matter, starch and reduced sugar.
Further, using dispositif de traitement lineaire adapte transformation, wavelet transform infrared image enhancement method and be based on
The infrared image of suprathreshold stochastic resonance is to the noise reduction process of graphic images, to extract potato graphic images characteristic information.
Further, the measurement of potato dry matter uses Suo Mujifa, reduced sugar using slice weighting method after dried, starch test
Measurement uses As-Mo blue hydrometer method.
By adopting the above-described technical solution, the present invention has the advantage that:
The present invention carries out non-destructive testing to dynamic quality during storage of potato using thermal imaging, can using the technology
It is fast with the dimension, the detection speed that reduce image feature information well, and can be detected on the spot in storage of potato.
Detailed description of the invention
To make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with attached drawing to the present invention make into
The detailed description of one step, in which:
Fig. 1 is Potato Quality detection technique route map；
Fig. 2 is ANN modeling procedure figure；
Fig. 3 is Quality Detection analysis model validity check flow chart；
Fig. 4 is the device of the invention figure.
Specific embodiment
Below with reference to attached drawing, a preferred embodiment of the present invention will be described in detail；It should be appreciated that preferred embodiment
Only for illustrating the present invention, rather than limiting the scope of protection of the present invention.
As shown in figure 4, a kind of device based on infrared thermal imaging technique detection Potato Quality, including cabinet, be set to
The sample cell 6 in bottom of box center is respectively arranged at intracorporal four headlamps 1,2,3,4 of case, is set to top of the box center
Infrared thermal imaging detection system 5 and the computerized control system 7 that is connect respectively with sample cell, infrared thermal imaging detection system.
Infrared thermal imaging detection system is used to shoot the top view of potato, and headlamp is respectively arranged at the four of top of the box
Angle, the brightness after whether the extinguishing or light and light of headlamp can pass through computerized control system；Sample cell be by
What one transparent organic glass made, mainly fixed potato samples, sample cell can rotate 360 °；Storage battery power supply
System is the graphic images detection system to the device, headlamp, sample stage power supply；The computer system control mechanism is used for
It collects the image information of infrared thermal imaging detection system acquisition and image information is handled and is realized to sample cell orientation
The motion control of console.
As shown in Figure 1, a kind of method based on infrared thermal imaging technique detection Potato Quality, includes the following steps
S1 acquires normal and rotten potato graphic images.Using dispositif de traitement lineaire adapte transformation, wavelet transform
The method of infrared image enhancement.And using the infrared image based on suprathreshold stochastic resonance to the noise reduction process of graphic images, with
Extract potato graphic images characteristic information.
S2 utilizes thermal image processing algorithm and the characteristic information of each sample of artificial neural network analysis.
S3 carries out feature information extraction based on physical characteristic of the force sensor technologies to normal and rotten potato, based on mark
The quasi- normal chemical composition content with rotten potato of biochemical method detection；Physical characteristic includes weight, density, hardness etc.；
Chemical component dry matter, starch, reduced sugar etc..Wherein the measurement of potato dry matter is using slice weighting method after dried, starch test
Using Suo Mujifa (i.e. copper reduction iodimetric titration), reducing sugar test uses As-Mo blue hydrometer method.
S4 study potato physical characteristic and chemical analysis and graphic images correlation, and find out it is normal with it is rotten
Otherness between potato.The correlation research of potato graphic images is analyzed using artificial neural network (ANN) algorithm,
And otherness therein is found out, process is as shown in Figure 2.
Using the thermal infrared images of thermal infrared camera shooting Normal potato and rotten potato, 200 are shot altogether, often
Second one, including heating in 40 seconds and 160 seconds cooling thermal infrared images.Average temperature difference curve is obtained by formula:
Wherein ∑ T1(x, y) indicates the temperature cumulative value of each pixel of the first width thermal infrared images, ∑ Ti(x, y) is indicated
The temperature cumulative value of each pixel of current thermal infrared images, n indicate total pixel value of thermal infrared images.
The average temperature difference curve of same potato samples different number of days is compared, the variation of potato freshness is observed；
The average temperature difference curve of same number of days difference sample is compared, the difference of Normal potato and rotten potato is observed, selection is flat
Equal temperature difference curve, carries out ANN modeling.
S5 establishes the Quality Detection analysis model of normal, rotten potato graphic images.Detailed process is as shown in Figure 3.
It is passed using noise equivalent temperature difference (NETD), minimum resolvable temperature difference (MRTD), modulation transfer function (MTF), signal
Delivery function (SiTF) etc. evaluates the stability and accuracy of thermal imaging system.
It is examined using F and T is examined and chosen suitable mean temperature difference data progress ANN modeling, wherein 2/3 modeling data is defeated
Enter and be trained and verify in Matlab, establishes ANN model；1/3 modeling data carries out last ANN model verifying, and verifying is lost
It loses to come back in Matlab and be trained, be proved to be successful then finally modeling achievement, and carried out pair with the biochemical method of standard
Than the accuracy for verifying model.
S6 is using normal, the potato graphic images that go bad the Quality Detection analysis models established, to storage of potato
Period dynamic quality non-destructive test research.Using noise equivalent temperature difference (NETD), minimum resolvable temperature difference (MRTD), modulation
Transmission function (MTF), signal transfer function (SiTF) etc. evaluate the stability and accuracy of thermal imaging system.
In the workflow detected with the device based on infrared thermal imaging technique detection Potato Quality are as follows:
(1) potato is placed on sample cell 6；
(2) it opens 1,2,3,4 headlamp of headlamp 3 minutes；
(3) headlamp is closed, infrared light supply is opened and potato is irradiated and acquires image；
(4) 180 ° of rotations are carried out using computer control sample cell, continues to collect image；
(5) laptop control system will collect image and carry out image procossing, during detecting storage of potato
Dynamic quality.
The above description is only a preferred embodiment of the present invention, is not intended to restrict the invention, it is clear that those skilled in the art
Various changes and modifications can be made to the invention by member without departing from the spirit and scope of the present invention.If in this way, of the invention
Within the scope of the claims of the present invention and its equivalent technology, then the present invention is also intended to encompass these to these modifications and variations
Including modification and variation.
Claims (10)
1. a kind of device based on infrared thermal imaging technique detection Potato Quality, it is characterised in that: including cabinet, be set to case
The sample cell (6) of body bottom center is respectively arranged at intracorporal four headlamps (1,2,3,4) of case, is set in top of the box
The infrared thermal imaging detection system (5) of centre and the computerized control system being connect respectively with sample cell, infrared thermal imaging detection system
(7)。
2. a kind of device based on infrared thermal imaging technique detection Potato Quality according to claim 1, feature exist
In: headlamp is respectively arranged at the quadrangle of top of the box.
3. a kind of device based on infrared thermal imaging technique detection Potato Quality according to claim 1, feature exist
In: the sample cell is rotatable sample pond.
4. a kind of method based on infrared thermal imaging technique detection Potato Quality, it is characterised in that: include the following steps
S1 acquires normal and rotten potato graphic images；
S2 utilizes thermal image processing algorithm and the characteristic information of each sample of artificial neural network analysis；
S3 carries out feature information extraction based on physical characteristic of the force sensor technologies to normal and rotten potato, measured
The normal chemical composition content with rotten potato of biochemical method detection；
S4 studies the physical characteristic of potato and the correlation of chemical analysis and graphic images, and finds out normal and rotten Ma Ling
Otherness between potato；
S5 establishes the Quality Detection analysis model of normal, rotten potato graphic images；
S6 is using normal, the potato graphic images that go bad the Quality Detection analysis models established, to during storage of potato
Dynamic quality non-destructive test research.
5. a kind of method based on infrared thermal imaging technique detection Potato Quality according to claim 4, feature exist
In: in the S4 specifically: using the thermal infrared images of thermal infrared camera shooting Normal potato and rotten potato, clap altogether
Take the photograph 200, one per second, including heating in 40 seconds and 160 seconds cooling thermal infrared images；Average temperature difference song is obtained by formula
Line:
Wherein ∑ T1(x, y) indicates the temperature cumulative value of each pixel of the first width thermal infrared images, ∑ Ti(x, y) indicates current
The temperature cumulative value of each pixel of thermal infrared images, n indicate total pixel value of thermal infrared images；
The average temperature difference curve of same potato samples different number of days is compared, the variation of potato freshness is observed；Comparison
The average temperature difference curve of same number of days difference sample observes the difference of Normal potato and rotten potato, selects average temperature
Poor curve is spent, ANN modeling is carried out.
6. a kind of method based on infrared thermal imaging technique detection Potato Quality according to claim 5, feature exist
In: in the S5 specifically: use noise equivalent temperature difference (NETD), minimum resolvable temperature difference (MRTD), modulation transfer function
(MTF) stability and accuracy of thermal imaging system are evaluated with signal transfer function (SiTF)；
It is examined using F and T is examined and chosen suitable mean temperature difference data progress ANN modeling, wherein 2/3 modeling data inputs
It is trained and verifies in Matlab, establish ANN model；1/3 modeling data carries out last ANN model verifying, authentication failed
It comes back in Matlab and is trained, be proved to be successful then finally modeling achievement, and compared with the biochemical method of standard
Verify the accuracy of model.
7. a kind of method based on infrared thermal imaging technique detection Potato Quality according to claim 4, feature exist
In: the physical characteristic includes weight, density and hardness.
8. a kind of method based on infrared thermal imaging technique detection Potato Quality according to claim 4 or 5, feature
Be: the chemical component includes dry matter, starch and reduced sugar.
9. a kind of method based on infrared thermal imaging technique detection Potato Quality according to claim 4, feature exist
In: using dispositif de traitement lineaire adapte transformation, the method for wavelet transform infrared image enhancement and based on suprathreshold stochastic resonance
Infrared image to the noise reduction process of graphic images, to extract potato graphic images characteristic information.
10. a kind of method based on infrared thermal imaging technique detection Potato Quality according to claim 8, feature exist
In: for the measurement of potato dry matter using slice weighting method after dried, starch test uses Suo Mujifa, and reducing sugar test uses arsenic molybdenum
Blue hydrometer method.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109856188A (en) * | 2019-04-03 | 2019-06-07 | 黑龙江科技大学 | It is a kind of to reduce the device that ambient enviroment interference and detected component surface emissivity influence when infrared thermal imaging detects |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6795784B1 (en) * | 1998-02-25 | 2004-09-21 | Thermal Wave Imaging, Inc. | Data integration and registration method and apparatus for non-destructive evaluation of materials |
| CN101158651A (en) * | 2007-11-16 | 2008-04-09 | 南京农业大学 | Device and method for detecting apples inside and outside quality based on laser image analyse |
| CN103336010A (en) * | 2013-07-10 | 2013-10-02 | 江苏大学 | Early tobacco virus disease detection method based on infrared thermal imaging technology |
| CN205091271U (en) * | 2015-11-16 | 2016-03-16 | 华中农业大学 | Quick detection device of outside phenotypic characteristics of portable field potato |
| CN105606473A (en) * | 2016-01-20 | 2016-05-25 | 华中农业大学 | Red globe grape hardness nondestructive detection method based on machine vision |
| CN106067173A (en) * | 2016-05-30 | 2016-11-02 | 湖南生物机电职业技术学院 | The Complexity Measurement lossless detection method of citrusfruit pol |
| CN106596566A (en) * | 2017-02-07 | 2017-04-26 | 贵阳学院 | Method for testing and comprehensively evaluating invisible damage to surface of fruit through infrared images |
| CN107609111A (en) * | 2017-09-13 | 2018-01-19 | 福建省农业科学院果树研究所 | The search method that a kind of loquat Variety identification, quality grading and maturity judge |
-
2018
- 2018-09-07 CN CN201811043837.9A patent/CN109001244B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6795784B1 (en) * | 1998-02-25 | 2004-09-21 | Thermal Wave Imaging, Inc. | Data integration and registration method and apparatus for non-destructive evaluation of materials |
| CN101158651A (en) * | 2007-11-16 | 2008-04-09 | 南京农业大学 | Device and method for detecting apples inside and outside quality based on laser image analyse |
| CN103336010A (en) * | 2013-07-10 | 2013-10-02 | 江苏大学 | Early tobacco virus disease detection method based on infrared thermal imaging technology |
| CN205091271U (en) * | 2015-11-16 | 2016-03-16 | 华中农业大学 | Quick detection device of outside phenotypic characteristics of portable field potato |
| CN105606473A (en) * | 2016-01-20 | 2016-05-25 | 华中农业大学 | Red globe grape hardness nondestructive detection method based on machine vision |
| CN106067173A (en) * | 2016-05-30 | 2016-11-02 | 湖南生物机电职业技术学院 | The Complexity Measurement lossless detection method of citrusfruit pol |
| CN106596566A (en) * | 2017-02-07 | 2017-04-26 | 贵阳学院 | Method for testing and comprehensively evaluating invisible damage to surface of fruit through infrared images |
| CN107609111A (en) * | 2017-09-13 | 2018-01-19 | 福建省农业科学院果树研究所 | The search method that a kind of loquat Variety identification, quality grading and maturity judge |
Non-Patent Citations (4)
| Title |
|---|
| 吴佳 等: "马铃薯品质无损检测技术研究进展", 《食品与机械》 * |
| 周建民等: "红外热成像技术在农业生产中的应用", 《农机化研究》 * |
| 韩彦中等: "基于神经网络客观测量MRTD的方法探讨", 《应用光学》 * |
| 高继森等: "基于人工神经网络的马铃薯品质分级系统", 《五邑大学学报（自然科学版）》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109856188A (en) * | 2019-04-03 | 2019-06-07 | 黑龙江科技大学 | It is a kind of to reduce the device that ambient enviroment interference and detected component surface emissivity influence when infrared thermal imaging detects |
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