CN101915738A - Method and device for rapidly detecting nutritional information of tea tree based on hyperspectral imaging technique - Google Patents
Method and device for rapidly detecting nutritional information of tea tree based on hyperspectral imaging technique Download PDFInfo
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Abstract
The invention relates to a method and a device for rapidly detecting main nutritional information during the growth of a tea tree based on a hyperspectral imaging technique. The method comprises the following steps of: firstly, actually measuring three main nutritional element content, such as N, P and K, of leaf samples and forming a database by using the measuring result as a reference measuring result; secondly, acquiring hyperspectral image data of the leaf samples of the tea tree in different visible light and near-infrared wave bands and transferring the data to a computer by using an image acquisition card; thirdly, preprocessing the data to complete the corresponding characteristic extraction and associating the characteristic variables with the measured N, P and K content in the established database to build a predictive model of the N, P and K content of the leaf; and finally, performing the corresponding data acquisition and characteristic extraction on the samples to be measured and predicting the N, P and K content of the leaf by using the built model. The method and the device have the advantages of high detection speed, simple and convenient operation, more comprehensive information and improvement on the accuracy and the stability of the detection result.
Description
Technical field
The present invention relates to a kind of based on main nutritional information quick detecting method and device in the growth of tea plant of high light spectrum image-forming technology.
Technical background
Tea tree is a perennial plant, and growth of tea plant needs a large amount of nutrients, and the tea tree nutrient is the important component part of its synthetic various organic compounds, participates in the developmental multiple metabolic process of growth of tea plant, has the important physical effect.Waning of tea tree nutrient directly influences growing of tea tree, also can have a negative impact to quality of tea leaves and output.Tea tree is that leaf is used plant, will carry out repeatedly leaf picking every year, tea tree all will consume macronutrient after each the harvesting, as carbon, hydrogen, oxygen, nitrogen, phosphorus, potassium, calcium, magnesium, manganese, boron, zinc etc., wherein, beyond de-carbon, hydrogen, the moisture and air of oxygen from the Nature, other nutrient is all from soil, thereby requires soil fertility to have high flow rate, high complementary characteristics.Tea tree is easy to occur nitrogen, phosphorus, potassium in growth course out of proportion, soil obstacle and nutritional deficiency symptom.Therefore, in the growth of tea plant process, be necessary its nutritional information is diagnosed fast, accurately,, improve tea place intelligent management level so that fertilising is effectively realized precisely in the tea place.
For a long time, the nutritional information of plant diagnosis all is based on the laboratory conventionally test, comprises that morphological diagnosis, leaf colour atla sheet method, chemical diagnosis, fertilizer window use diagnosis and enzymology diagnosis method etc.These traditional means of testing not only can destroy making deposits yields, influence plant growth, and need expend great amount of manpower and material resources at aspects such as sampling, mensuration, data analyses, and poor in timeliness is unfavorable for applying.In recent years, some novel optical technology means begin to be applied to the quick detection of nutritional information in the crop growth.These technology have fast, the workable and high repeatability and other advantages as a result of detection speed.Whether the disappearance of plant nutrient, not only with the texture of blade, surface such as color and luster and shape is directly related, and it is closely related with the blade interior institutional framework, Computer Image Processing can characterize the surface of blade well, near infrared spectrum can reflect the blade interior institutional framework well, at present on crop alimentary information quick detection, Computer Image Processing and near-infrared spectrum technique are considered to two kinds of means the most effective, but in the application specific to corps nutrient information quick detection, they all have certain limitation: spectral technique can detect crop leaf internal feature information well, but can not express the color of crop leaf, surface such as texture and shape; Though Computer Image Processing can be expressed the surface of crop leaf, local characteristic wave bands is not carried out refinement and enhancing, so the refine of crop spectral information is not obvious; Currently be based on mostly that single technological means finishes, single technological means is not enough to reflect the crop alimentary situation accurately, comprehensively.
High light spectrum image-forming technology light harvesting spectral analysis technology and graphical analysis are, can carry out visual analyzing to the inside and outside feature of fresh leaves of tea plant, compare with the traditional detection means, what this technology obtained contains much information, the image information that had both contained reflection tea leaf surface, the spectral information that contains reflection tea leaf internal organizational structure is again considered the inside and outside characteristic information of blade simultaneously, has improved the accuracy of nutritional information of tea tree quick detection like this.Therefore, provided by the invention based on nutritional information lossless detection method and device in the growth of tea plant of filtering chip high light spectrum image-forming technology, can realize nutritional information quick detection in the growth of tea plant process.This invention is accurately applied fertilizer for the tea place science reference is provided, and to improving tea place intelligent management level, increase tea yield and improving tea leaf quality direct significance is arranged all.
Summary of the invention
In view of above-mentioned prior art development, purpose of the present invention is exactly to provide a kind of based on nitrogen, phosphorus, the main nutritional information quick detecting method of potassium and device in the growth of tea plant of high light spectrum image-forming technology.By the high-spectral data of filtering chip high spectrum image system and device collection tea leaf, these data can be reacted the image information of the spectral information and the tealeaves external appearance characteristic of blade internal characteristics simultaneously.Raw data is through after demarcating, and extraction can characterize the characteristic image and the spectral information of the inside and outside feature of blade; From characteristic image, extract the color characteristic and the textural characteristics variable that can reflect bright leaf outside again, utilize principal component analysis (PCA) from spectral information, to extract the major component characteristic variable, and these characteristic variables are merged mutually; At last N, P in these proper vectors and the blade, K content reference measurement values (being measured by conventional method) are associated, set up the forecast model of N in the blade, P, three kinds of nutrients of K by non-linear method.Sample to be tested is predicted the content of N, P, K in this blade again by corresponding data acquisition and feature extraction with the model of having set up.So that survey the main nutritional information in the growth of tea plant process in real time, exactly, for the accurate fertilising in tea place provides theoretical foundation.
The objective of the invention is to realize by the following method:
(1) sets up forecast model: choose the tea leaf sample, utilize the atomic absorption detection method to measure the content of N, P, three kinds of main nutrient elements of K in the leaf samples, form a database; Obtain the high spectrum image data of these samples at different visible light and near-infrared band, data are imported computing machine into through image pick-up card; Computing machine carries out pre-service to data, finish the extraction of corresponding image information and spectral information characteristic variable, then these characteristic variables are merged mutually, and be associated, set up the forecast model of N in the blade, P, K content with N, P, the K content measured in the database of aforementioned foundation;
(2) carry out test sample: for bright leaf sample to be measured, the extraction of obtaining sample to be tested high spectrum image data, data pre-service, image information and spectral information characteristic variable according to identical mode in the step (1), then with the forecast model of N, P, K content in the blade of the above-mentioned foundation of characteristic variable substitution of extracting, draw N, P, K content prediction result, finish real-time detection bright leaf sample to be measured.
The collection of described high spectrum image data, the even Horizon of tea leaf sample is layered on objective table top in the light-source box, the Halogen lamp LED of two 30W provides stable illumination condition in the light-source box, the light that Halogen lamp LED sends is radiated on the tea leaf on the objective table equably, light carries out diffuse reflection on blade, obtain special wavelength light by optical filter then and enter CCD camera (visible light-near infrared camera), camera is transferred to computing machine with the view data that collects by capture card.By the rotation of RS-232 control wheel disc, allowing respectively diffuses enters the CCD camera by 6 different centre wavelength optical filters, obtains 6 images under the different wave length, superposes then, forms a three-dimensional data piece.
Described raw data pre-service and characteristic information extract, and in the data pre-service of high spectrum, at first the blank by standard carries out the black and white correction to original image; The three-dimensional data piece is carried out dimension-reduction treatment, therefrom extract image information and spectral information; Then from image information, extract the characteristic variables such as texture, CF of the surface that can describe the tealeaves blade; Can show the major component characteristic variable of tea leaf internal feature from withdrawing spectral information.
Described characteristic variable merges and forecast model is set up, exactly extraction is obtained spectral signature information and image feature information and construct the associating characteristic variable, N, P, the K content reference measurement values that these characteristic variables and atomic absorption light spectral method are measured is associated again, makes up N, P, K content prediction model in the tea leaf by nonlinear method.
Realize the filtering chip Hyperspectral imager device of said method, comprise visible light-near infrared camera, halogen light source, light-source box, objective table, image pick-up card and computing machine, also have optical filter, automatic rotation roulette and RS-232; A rotatable wheel disc is installed directly over light-source box, circumferentially have circular hole in this wheel disc upper edge, be provided with optical filter in the circular hole from visible light and near-infrared band, visible light-near infrared camera is installed in the autoamtic disk top, autoamtic disk is controlled its rotation by RS-232, the every rotation of wheel disc once just makes the optical filter on the wheel disc aim at fully with the camera lens of camera; Halogen light source and objective table are arranged in the light-source box; Image pick-up card is connected with visible light-near infrared camera and computing machine, with visible light-near infrared camera collection to data import computing machine into; Computing machine is used to store data, data are carried out pre-service, finishing corresponding image information and spectral information characteristic variable extracts, then these characteristic variables are merged mutually, and be associated with N, P, the K content reference measurement values measured in the database, set up N in the blade, P, K content prediction model; The forecast model of N, P, K content draws N, P, K content prediction result in the blade of setting up according to the sample to be tested characteristic variable substitution of extracting during actual measurement.
Accompanying drawing 1 is filtering chip Hyperspectral imager device synoptic diagram.Interference for fear of extraneous parasitic light, the present invention has designed an airtight light-source box, rotatable wheel disc of design directly over light-source box, the circular hole that to have 6 diameters on this wheel disc be 25cm has 6 optical filters from different-waveband to be absorbed in the hole.A visible light-near infrared camera is installed above wheel disc, and autoamtic disk is controlled its rotation by RS-232, and the every rotation of wheel disc once just makes the optical filter on the wheel disc aim at fully with the camera lens of camera.
The invention has the beneficial effects as follows:
By gathering the high-spectral data of tea leaf sample, extraction can characterize the image and the spectral information of the inside and outside feature of blade based on main nutritional information quick detecting method in the growth of tea plant of filtering chip high light spectrum image-forming technology; From characteristic image, extract characteristic variables such as color, texture and shape again, utilize principal component analysis (PCA) from spectral information, to extract the major component characteristic variable, and these characteristic variables are merged mutually; In conjunction with the standard value of N, P, K content in the blade of atomic absorption light spectrometry, make up tea leaf N, P, K content prediction model at last by non-linear multivariate calibration method.Sample to be tested is predicted N, P, the K content of this sample again by corresponding data acquisition and feature extraction with the model of having set up.
The present invention compares with conventional atomic absorption spectroscopy method, fast, the easy and simple to handle convenience of detection speed; Compare with single near infrared spectrum or computer vision technique means, the information that obtains more comprehensively, the accuracy and the stability of testing result all increase.Provided by the invention based on nutritional information quick detecting method and device in the growth of tea plant of filtering chip high light spectrum image-forming technology, can realize nutritional information quick detection in the growth of tea plant process.This invention is accurately applied fertilizer for the tea place science reference is provided, and to improving tea place intelligent management level, increase tea yield and improving tea leaf quality direct significance is arranged all.
The annex explanation
Fig. 1: apparatus of the present invention system schematic.
Wherein: 1, visible light-near infrared camera; 2, optical filter; 3, automatic rotation roulette; 4, halogen light source; 5, light-source box; 6, tea leaf; 7, objective table; 8, RS-232; 9, image pick-up card; 10, computing machine
Fig. 2: method flow diagram of the present invention.
Specific implementation method
Example performing step of the present invention is with reference to Fig. 2, and the example implement device is consulted Fig. 1.Choose a collection of fresh leaves of tea plant sample (generally greater than 100 samples) earlier and be used for carrying out model tuning, utilize filtering chip high light spectrum image-forming device (as Fig. 1) that bright leaf sample is carried out the high-spectral data collection; After data acquisition is finished, by Atomic Absorption Spectrometry its inner N, P, K content, as the standard value of N, P, K content in this sample; To original high-spectral data dimensionality reduction, extraction can reflect the image information and the spectral information of the inside and outside feature of blade again; Then, from characteristic image, extract characteristic variables such as color, texture and shape, from spectral information, extract the major component characteristic variable; At last, these characteristic variables are merged mutually,, make up N, P, K content prediction model in the tea leaf by non-linear multivariate calibration method in conjunction with the standard value of N, P, K content in the tea leaf.
Next just can carry out fast prediction to unknown blade sample.The even Horizon of blade sample to be measured is layered on the objective table 7 in the light-source box 5 carries out raw data acquisition; During work, the light that halogen light source 4 is sent is radiated on the blade sample equably, and light carries out diffuse reflection on blade, obtains special wavelength light by optical filter then and enters visible light-near infrared camera 1; Control the rotation of automatic rotation roulette 3 by RS-2328, rotation roulette 3 every rotations once just make the optical filter 2 on the wheel disc 3 aim at fully with the camera lens of camera 1 automatically; Sample of every collection, wheel disc 3 rotations 6 times allow light enter camera 1 by 6 different centre wavelength optical filters 2 respectively, obtain 6 images under the different wave length, and superposeing then forms the data block of a three-dimensional; Camera 1 is transferred to computing machine 10 with the view data that collects by capture card 9.In computing machine, finish the pre-service of high spectrum image raw data, the extraction and the information fusion of characteristic variable, the content of N, P, K in just can fast prediction blade to be measured in the forecast model that these characteristic variable substitutions are set up in advance, and on computer interface, show.So far N, P that should the unknown blade, K content measurement finish.
Claims (5)
1. nutritional information of tea tree quick detecting method based on the high light spectrum image-forming technology is characterized in that:
(1) sets up forecast model: choose the tea leaf sample, measure the content of N, P, three kinds of main nutrient elements of K in the leaf samples earlier, form a database as the reference measurement result; By obtain the high spectrum image data of tea leaf sample at different visible light and near-infrared band based on filtering chip Hyperspectral imager, data are imported computing machine into through image pick-up card; Computing machine carries out pre-service to data, finishes corresponding characteristic extraction, and N, P, the K content of measuring in the database with these characteristic variables and aforementioned foundation is associated then, sets up the forecast model of N in the blade, P, K content;
(2) carry out test sample: for bright leaf sample to be measured, obtain sample to be tested high spectrum image data, data pre-service, feature extraction according to identical mode in the step (1), then with N, P, the K content prediction model of the above-mentioned foundation of characteristic variable substitution of extracting, draw N, P, K content prediction result, finish real-time detection bright leaf sample to be measured.
2. method according to claim 1, it is characterized in that, the concrete operations of the collection of said high spectrum image data are: the even Horizon of tea leaf sample is layered on objective table top in the light-source box, Halogen lamp LED provides stable illumination condition in the light-source box, the light that Halogen lamp LED sends is radiated on the tea leaf on the objective table equably, light carries out diffuse reflection on blade, the light that diffuse reflection is come out obtains special wavelength light by optical filter and enters visible light-near infrared camera, and camera is transferred to computing machine with the view data that collects by capture card; Then, by the rotation of wheel disc, allowing respectively diffuses enters visible light-near infrared camera by the different wave length optical filter, obtains the image under the different wave length, superposes then, forms a three-dimensional data piece.
3. method according to claim 2 is characterized in that, described raw data pre-service and characteristic information extract, and is that first blank by standard carries out the black and white correction to original image; The three-dimensional data piece is carried out dimension-reduction treatment, therefrom extract image information and spectral information; Then from image information, extract the characteristic variables such as texture, CF of the surface that can describe the tealeaves blade; Can show the major component characteristic variable of tea leaf internal feature from withdrawing spectral information.
4. method according to claim 3, it is characterized in that, described characteristic variable merges and forecast model is set up, exactly extraction is obtained spectral signature information and image feature information and construct the associating characteristic variable, again these characteristic variables are associated with N, P, the K content value of measurement, make up N, P, K content prediction model by non-linear multivariate calibration method.
5. a filtering chip Hyperspectral imager of realizing the nutritional information of tea tree quick detecting method comprises visible light-near infrared camera, halogen light source, light-source box, objective table, image pick-up card and computing machine, it is characterized in that:
Also have optical filter, automatic rotation roulette and RS-232; Directly over light-source box, be provided with a rotatable wheel disc, circumferentially have circular hole in this wheel disc upper edge, be provided with the optical filter of different visible light and near-infrared band in the circular hole, visible light-near infrared camera is installed in the autoamtic disk top, autoamtic disk is controlled its rotation by RS-232, the every rotation of wheel disc once just makes the optical filter on the wheel disc aim at fully with the camera lens of camera; Halogen light source and objective table are arranged in the light-source box; Image pick-up card is connected with visible light-near infrared camera and computing machine, with visible light-near infrared camera collection to data import computing machine into; Computing machine is used to store data, data are carried out pre-service, finish the extraction of corresponding image information and spectral information characteristic variable, then these characteristic variables are merged mutually, and be associated with N, P, the K content measured in the database, set up the forecast model of N in the blade, P, K content; The forecast model of N, P, K content draws N, P, K content prediction result in the blade of setting up according to the sample to be tested characteristic variable substitution of extracting during actual measurement.
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