CN104280349A - Method for identifying hollowness of white radishes based on hyperspectral image - Google Patents
Method for identifying hollowness of white radishes based on hyperspectral image Download PDFInfo
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Abstract
The invention relates to a method for identifying hollowness of white radishes based on a hyperspectral image, belonging to the nondestructive testing technology in agricultural product storage and processing industry. The method comprises the following steps: acquiring a transmitting hyperspectral image in the white radish storage process by virtue of a hyperspectral imager, analyzing the difference of spectral responses of normal white radishes and hollowed white radishes, extracting spectral values in the wavelength of 400-1000nm to serve as input values of a neural network, and judging whether the white radishes are hollowed. According to the method, the hollowness of the white radishes can be accurately identified, the manual destructive detection is replaced, unqualified products are effectively prevented from flowing into the market, the eating and processing utilization rate of the white radishes is improved, the deep processing industrial development of white radishes is facilitated, and a reference is provided for applying the hyperspectral technology to the field of agricultural products.
Description
Technical field
The present invention is a kind of method that hyper-spectral image technique detects the chaff heart during ternip postharvest storage, belongs to the technical field of store and process of agricultural products Non-Destructive Testing.
Background technology
Radish Hollowing, also known as hollow, is the spontaneous phenomenon in radish growth, and growth period and storage period all can occur.Cause Radish Hollowing reason to have multiple, water stress, fertilizer condition discomfort, illumination and temperature etc. all can cause the chaff heart of radish.Chaff heart process can make the nutriment such as starch, sugar reduce, and affects its processing, storage and consumption.The traditional detection method of the inner chaff heart of radish adopts artificial sense to detect, and not only waste time and energy, and precision is not high, is difficult to the demand of applicable heavy industrialization automatic classification.Therefore, set up a kind of harmless, reliable method to detect the chaff heart of radish, carry out detection classification to radish, raising radish marketable value and the development of radish process deeply industry have important meaning.
In recent years, high spectrum image detection technique, as a kind of method of not damaged, rapidly assessment and analysis various kinds of foods quality and safety, has been widely recognized.High spectrum image can detect physics and the morphological feature of food, and the chemistry of inside and Molecular information, thus the quality and safety of analysis and inspection food.Good application is had in the food industry at home and abroad of this technology, as [Qin J such as Jianwei Qin, Burks T F.Development of a two-band spectral imaging system for real-time citrus canker detection [J] .Journal of Food Engineering, 2012,1 (108): 87-93.] based on the characteristic wave bands of high spectrum image screening, have developed business fruit grader, its speed is 5/second, and overall classification accuracy is 95.3%.[the Herrero-Langreo A such as Ana Herrero-Langreo, Lunadei L, et al.Multispectral Vision for Monitoring Peach Ripeness [J] .Food science, 2011,2 (76): 178-187.] utilize hyper-spectral image technique to evaluate the degree of ripeness of peach, conveniently determine optimum collecting time.[the Baranowski P such as Piotr Baranowski, et al.Detection of early bruises in apples using hyperspectral data and thermal imaging [J] .Journal of Food Engineering, 2012,3 (110): 345-355.] high spectrum image is utilized to assess apple hardness and soluble solid.Hyper-spectral image technique is also applied to apple, cherry and citrus fruit surface imperfection, the detection of cucumber inherent vice etc.The domestic hyper-spectral image technique that utilizes developed equally rapidly to the detection of agricultural product quality in recent years, as [Huang Wenqian such as Huang Qianwen, Chen Liping, Li Jiangbo, Deng. the apple slight damage based on high light spectrum image-forming detects effective wavelength and chooses [J]. Transactions of the Chinese Society of Agricultural Engineering .2013, 29 (1): 272-277.] apple surface slight damage is detected, [the high sea otter such as high sea otter, Li little Yu, Xu Senmiao, Deng. the transmission hyperspectral detection method [J] of potato tuber heart and single potato quality. Transactions of the Chinese Society of Agricultural Engineering .2013, 29 (15): 279-285.] potato tuber heart is detected, [the Li Jiangbo such as Li Jiangbo, Wang Fujie, Ying Yibin, Deng. EO-1 hyperion Imaging-PAM is identifying the applied research [J] in incipient decay navel orange. spectroscopy and spectral analysis .2012, 32 (1): 142-146.] EO-1 hyperion fluoroscopic examination incipient decay navel orange is utilized, [the Tian Youwen such as Tian Youwen, Li Tian comes, Zhang Lin, Deng the method [J] of. hyper-spectral image technique diagnosis greenhouse cucumber disease. Transactions of the Chinese Society of Agricultural Engineering .2010 (5): 202-206.] aspect such as cucumber disease detection all achieves good result.But the technology of the Non-Destructive Testing of the inner chaff heart of radish has no report both at home and abroad, be necessary to carry out adopting hyper-spectral image technique to study the Non-Destructive Testing of the radish inside chaff heart.
Summary of the invention
Technical matters
In view of above-mentioned state-of-the-art, object of the present invention cannot realize ternip in storage and a difficult problem of selling chaff heart Nondestructive Identification in process mainly for prior art, the quick nondestructive method that exploitation high spectrum image detects, meets the active demand of radish deep processing industry.By utilizing high light spectrum image-forming technology, analyzing the spectral information difference of normal ternip and chaff core white radish, extracting the characteristic parameter of response, build the qualification model of the ternip chaff heart.
Technical scheme
1., based on a method for high spectrum image dialogue Radish Hollowing qualification, its device constitutive characteristic is:
1) system composition, comprise high light spectrum image-forming unit, sample holder, electric platforms, line source, light box, computer and image capture software composition, whole device is placed in airtight black box.Wherein, high light spectrum image-forming unit is by video camera (Imperx, ICL-B1620, wavelength band is 400 ~ 1000nm, spectral resolution is 2.8nm), spectrometer (Specim, ImSpector, V10E) and focal length variable lens composition, light box is the tungsten halogen lamp of 150W, complete transmission by 1 linear optical fiber conduit, computer model is CPU E5800,3.2GHz, internal memory 2G, video card 256M GeForce GT240; Image capture software is the Spectral Image software of independent development;
2) transmission collecting unit, lens are 20cm from ternip sample distance, and sample is close to line source and is placed, and the intensity of light source is 90W, gathers time shutter 70ms, picking rate 2.5mm/s, image resolution ratio 804 × 440;
Its detecting step is:
1) get the ternip sample to be measured that size is homogeneous, have no mechanical damage, surface clean is clean and dry, and is positioned in described high spectrum image detection system, obtains high spectrum image;
2) utilize following formula to the correct image obtained, obtain the high spectrum image after correcting:
Wherein: Rc is the EO-1 hyperion transmission image after correcting, R
0for original EO-1 hyperion transmission image, W be by reflectivity be 99.99% reference white correction plate, be placed on directly over light source, scanning transmission blank obtains entirely white uncalibrated image, and D is by lens cap on lens cap, gathers entirely black uncalibrated image;
3) area-of-interest of 25000 pixels in position, middle, ternip region in image is selected, extract the spectrum average of all pixels in this region in 400-1000nm wavelength band, and as the input value of the neural network model built, Output rusults is the ternip whether chaff heart, the neural network model parameter wherein built is: the hiding number of plies is 1, hidden layer nodes is 13, and hidden layer activation function is tanh; Output layer number is 2, i.e. qualified sample and chaff heart sample, and output layer activation function is Softmax.
Beneficial effect
The present invention utilizes the monitoring to high spectrum image instrument response signal, when can not destroy ternip integrality, by the EO-1 hyperion characteristic of ternip, whether accurately tell the inner chaff heart of ternip, it can be modular product quality, improve radish marketable value, reduce consumer for the misgivings may having bought chaff heart radish, have deep meaning to radish process deeply industry.Detect relative to traditional destructiveness, not only save time, and avoid unnecessary waste.This techniques and methods is novel, achievement in research not only may be used for express-analysis and the detection in laboratory, and can by exploitation online detection instrument and portable instrument, ternip chaff heart qualification in producing for industrial automation, also for the detection of other agricultural product inside qualities provides useful reference.
Four, accompanying drawing explanation
Fig. 1: the device of EO-1 hyperion transmissive system ternip chaff heart qualification
Fig. 2: the device of high spectrum reflection system ternip chaff heart qualification
Fig. 3: the device of EO-1 hyperion half transmitting system ternip chaff heart qualification
Fig. 4: the original average light spectrogram of ternip under transmission mode
Fig. 5: the original average light spectrogram of ternip under reflective-mode
Fig. 6: the original average light spectrogram of ternip under half reflection pattern
Five, embodiment
Based on a method for high spectrum image dialogue Radish Hollowing qualification, embodiment is as follows:
1. test material
Ternip kind is 301 radish of academy of agricultural sciences of Jiangsu Province seed selection, cultivate on May 20th, 2014, according to Radish Hollowing pathogenic factor in planting process, special processing is carried out to part radish and causes its chaff heart, on July 10th, 2014 gathers, select have no mechanical damage, without disease and pest, 120, the homogeneous sample of size, wherein process and each 60 of untreated sample, cleaning is tested after also naturally drying.
2. high spectrum image acquisition system
Hyperspectral imager, primarily of compositions such as video camera, imaging spectrometer, CCD camera, light source, a set of mechanical transmission device and computing machines, is the production of Taiwan five bell company.The spectral effective wavelength band 400-1000nm of imaging spectrometer, totally 440 wave bands, spectral resolution is 2.8nm, and with focal length variable lens, light source is 150W tungsten halogen lamp, and light source totally 10 grades is adjustable, and by Optical Fiber Transmission to line source.For avoiding extraneous light on the impact of spectra collection, pick-up unit entirety is placed in camera bellows, and background is black, not reflective.Test adopts transmission, reflection and half transmitting three kinds of detecting patterns to obtain ternip high-spectrum image signals respectively, and three kinds of drainage pattern hardware form upper identical, unlike relative position and the optimum configurations of light source and sample.
Based on the high spectrum image acquisition system under transmission mode as shown in Figure 1, sample and light source are all fixing on a moving belt, and a line source is positioned at immediately below sample, and light therethrough sample is absorbed by spectrometer, converts data to and imports computing machine into.Its relative parameters setting is time shutter 70ms, line speed 2.5mm/s, and the intensity of light source is 90W, and light source is close to sample, camera lens distance sample 20cm, fixed sample, prevents from rolling, and starts image data.
Based on the high spectrum image acquisition system under reflective-mode as shown in Figure 2, two line sources are individually fixed in directly over sample, sample is fixed on travelling belt, the beam crosses that line source is launched, beam crossover point is fruit center, spectrometer, by gathering image collection spectral information, converts data to and imports computing machine into.Correlation parameter is time shutter 3ms, line speed 3mm/s, intensity of light source 45W, line source angle 45 °, camera lens distance sample 25cm, fixed sample image data.
Based on the high spectrum image acquisition system under half transmitting pattern as shown in Figure 3, two line sources lay respectively at sample both sides, sample is fixing on a moving belt, line source sends light beam and directly injects sample interior, spectrometer is collected by sample diffuse reflection spectral information out directly over sample, converts data to and imports computing machine into.Its relative parameters setting is time shutter 45ms, and line speed is 3mm/s, and the intensity of light source is 75W, and light source levels is positioned in proximity to center of a sample, camera lens distance sample 25cm, and fixed light source and sample start image data.
3. high spectrum image collection and correction
In order to eliminate the noise in data acquisition, with under the similarity condition of ternip sample collection, entirely white uncalibrated image is obtained after scanning white standard correction plate (reflectivity 99.99%), complete black uncalibrated image is obtained after covering lens cap, by formula, the absolute image collected is converted to relative image, formula is:
In formula (1): R is for changing to obtain relative image, and I is for gathering to obtain absolute image, and B is complete black uncalibrated image, W is complete white uncalibrated image.
During data processing, adopt area-of-interest analytic approach, the spectral value of area-of-interest (ROI region) averaged spectrum as this sample that high spectrum image after changing chooses 25000 pixels in centre position is obtained to each sample collection, and carries out modeling with full spectrum differentiate chaff heart radish in conjunction with partial least squares analysis (PLS-DA), support vector machine (SVM), artificial neural network (ANN).
4. Mathematical Modeling Methods
Use PLS-DA, SVM, ANN tri-kinds of methods to carry out modeling, the classification capacity of the spectral information dialogue Radish Hollowing that the lower three kinds of detecting patterns of more each model obtain, match stop accuracy, judges optimum detection pattern and forecast model.
5. the original spectrum analysis under different acquisition pattern
There is the ternip of the chaff heart, its institutional framework and chemical composition can change thereupon, and then affect light through optical characteristics such as, absorptions, with there is not chaff heart radish and have larger difference, therefore to be expected to for judging the radish whether chaff heart by the difference of spectrum (transmission, reflection, half transmitting).Gather the area-of-interest of ternip high spectrum image, calculate its mean value, obtain the spectral value of ternip in 400-1000nm wave band interval.As Fig. 4,5,6 is respectively the averaged spectrum comparison diagram of normal radish and chaff heart radish under transmission, reflection, half transmitting pattern.
The relatively spectral curve of high spectrum image three kinds of detecting patterns acquisitions, can find out that normal radish and chaff heart radish SPECTRAL DIVERSITY are comparatively large, demonstrate the possibility that high spectrum image detects Radish Hollowing, may be used for distinguishing normal ternip and chaff core white radish.And because ternip surface is in white, spectral reflectance is comparatively strong, the spectrum of transmission and half transmitting can fully and radish inside interact, the difference of normal radish and chaff heart radish can be demonstrated better.
6. under the different model of all band spectrum, the ternip chaff heart is differentiated
Utilize PLS-DA to distinguish chaff heart radish and normal radish, result is as shown in table 1.Therefrom can find out, the entirety of PLS-DA model to modeling collection and the normal radish of forecast set based on transmission mode differentiates that accuracy is respectively 85.0% and 90.0%, 97.5% and 90.0% are respectively to the recognition correct rate of chaff heart radish, therefore transmission mode overall recognition correct rate under PLS-DA model is higher, differentiate that the chaff heart is effective.In reflection detecting pattern, PLS-DA model is respectively 97.5% and 90.0% to modeling collection and the overall recognition correct rate of the normal radish of forecast set, and 65.0% and 75.0% are respectively to chaff heart radish discrimination, normal radish and chaff heart radish recognition correct rate instability under this detecting pattern, qualified sample only has 3 to be identified as chaff heart sample, and chaff heart sample has 19 to be judged mistake, institute better can not judge the radish whether chaff heart in this reflection mode under PLS-DA model.In half transmitting pattern, PLS-DA is respectively 72.5% and 75.0% to modeling collection and the overall recognition correct rate of the normal radish of forecast set, 97.5% and 70.0% are respectively to chaff heart radish specimen discerning accuracy, half transmitting pattern under PLS-DA model normal radish and chaff heart radish recognition correct rate low, differentiate that chaff heart effect is bad.
Table 1PLS-DA model dialogue Radish Hollowing predicts the outcome
Utilize SVM to distinguish chaff heart radish and normal radish, predict the outcome as shown in table 2.As can be seen from the table, the entirety of SVM model to modeling collection and the normal radish of forecast set based on transmission mode differentiates that accuracy is respectively 100.0% and 95.0%, 100.0% and 85.0% are respectively to the recognition correct rate of chaff heart radish, transmission mode overall recognition correct rate under SVM model is higher, differentiates that the chaff heart is effective.In reflection detecting pattern, SVM model is respectively 87.5% and 75.0% to modeling collection and the overall recognition correct rate of the normal radish of forecast set, and 95.0% and 90.0% are respectively to chaff heart radish discrimination, under this detecting pattern, normal radish recognition correct rate is lower than chaff heart radish recognition correct rate, and reflective-mode is overall under SVM model identifies that Radish Hollowing ability is general.In half transmitting pattern, SVM is respectively 80.0% and 75.0% to modeling collection and the overall recognition correct rate of the normal radish of forecast set, 92.5% and 70.0% are respectively to chaff heart radish specimen discerning accuracy, half transmitting pattern is normal radish and chaff heart radish recognition correct rate instability under SVM model, can not better differentiate Radish Hollowing.
Table 2SVM model dialogue Radish Hollowing predicts the outcome
Utilize ANN to distinguish chaff heart radish and normal radish, predict the outcome as shown in table 3.Therefrom can find out, the entirety of ANN model to modeling collection and the normal radish of forecast set based on transmission mode differentiates that accuracy is respectively 100% and 94.4%, 97.7% and 94.1% are respectively to the recognition correct rate of chaff heart radish, therefore transmission mode overall recognition correct rate under ANN model is higher, differentiate that the chaff heart is effective.In reflection detecting pattern, ANN model is respectively 88.1% and 100.0% to modeling collection and the overall recognition correct rate of the normal radish of forecast set, and 76.9% and 85.7% are respectively to chaff heart radish discrimination, under this detecting pattern normal radish and chaff heart radish recognition correct rate general.In half transmitting pattern, ANN is respectively 84.8% and 71.4% to modeling collection and the overall recognition correct rate of the normal radish of forecast set, 81.8% and 93.8% are respectively to chaff heart radish specimen discerning accuracy, half transmitting pattern under ANN model normal radish and chaff heart radish recognition correct rate low, differentiate that chaff heart effect is bad.
Table 3ANN model dialogue Radish Hollowing predicts the outcome
7. three kinds of forecast models are to the comparison of Radish Hollowing recognition effect
Can be found by aforementioned, different detecting patterns and the detection of forecast model to Radish Hollowing there are differences.Utilize PLS-DA model to carry out the differentiation of the chaff heart to radish, under transmission mode, modeling collection and forecast set overall accuracy reach 91.3% and 90.0%, but under reflective-mode, recognition correct rate is unstable, and under half transmitting pattern, recognition correct rate is lower; Utilize SVM model to carry out the differentiation of the chaff heart to radish, under transmission mode, modeling collection and forecast set overall accuracy reach 100.0% and 90.0%, and accuracy rate has had certain lifting, but under half transmitting pattern, recognition correct rate is low and unstable; Utilize ANN model to carry out the differentiation of the chaff heart to radish, under transmission mode, modeling collection and forecast set overall accuracy reach 98.8% and 94.3%, and the high and stability of recognition correct rate comparatively first two model increases.
Therefore, first and last, in three kinds of detecting patterns, it is the highest that EO-1 hyperion transmission detecting pattern detects ternip chaff heart accuracy rate, and adopt PLS-DA, SVM, ANN model to be all the highest to the overall accuracy of the identification of the chaff heart, is obviously better than reflection and half transmitting pattern.In three kinds of forecast models, SVM and ANN accuracy rate is all higher, but comprehensive accuracy rate and stability, ANN forecast model differentiates that ternip chaff heart effect is best.
Claims (1)
1., based on a method for high spectrum image dialogue Radish Hollowing qualification, its device constitutive characteristic is,
1) system composition, comprise the high light spectrum image-forming unit, sample holder, electric platforms, line source, light box, computer and the image capture software that are made up of video camera, spectrometer and focal length variable lens and form, whole device is placed in airtight black box.Wherein, video camera is Imperx, ICL-B1620, and wavelength band is 400 ~ 1000nm, and spectral resolution is 2.8nm, spectrometer is SpecimV10E; Light box is the tungsten halogen lamp of 150W, completes transmission by 1 linear optical fiber conduit; Computer model is CPU E5800,3.2GHz, internal memory 2G, video card 256M GeForce GT240; Image capture software is the Spectral Image software of independent development; Light source is transmission mode, and wherein, lens are 20cm from ternip sample distance, and sample is close to line source and is placed, and the intensity of light source is 90W, gathers time shutter 70ms, picking rate 2.5mm/s, image resolution ratio 804 × 440;
2) detecting step is:
1. get the ternip sample to be measured had no mechanical damage, surface clean no-sundries, be positioned in high spectrum image detection system as claimed in claim 1, obtain high spectrum image;
2. utilize following formula to the correct image obtained, obtain the high spectrum image after correcting:
Wherein, Rc is the EO-1 hyperion transmission image after correcting, R
0for original EO-1 hyperion transmission image, W be by reflectivity be 99.99% reference white correction plate, be placed on directly over light source, scanning transmission blank obtains entirely white uncalibrated image, and D is by lens cap on lens cap, gathers entirely black uncalibrated image;
3. the area-of-interest of 25000 pixels in position, middle, ternip region in image is selected, extract the spectrum average of all pixels in this region in 400-1000nm wavelength band, as the input value of the neural network model built, Output rusults is the ternip whether chaff heart, the neural network model parameter wherein built is: the hiding number of plies is 1, hidden layer nodes is 13, and hidden layer activation function is tanh; Output layer number is 2, i.e. qualified sample and chaff heart sample, and output layer activation function is Softmax.
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