CN102788796B - Nutrient diagnosis device and nutrient diagnosis method for nitrogen of crops based on multi-information integration of high spectral images and fluorescent images - Google Patents
Nutrient diagnosis device and nutrient diagnosis method for nitrogen of crops based on multi-information integration of high spectral images and fluorescent images Download PDFInfo
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- CN102788796B CN102788796B CN201210298341.2A CN201210298341A CN102788796B CN 102788796 B CN102788796 B CN 102788796B CN 201210298341 A CN201210298341 A CN 201210298341A CN 102788796 B CN102788796 B CN 102788796B
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Abstract
The invention discloses nutrient diagnosis device and utrient diagnosis method for nitrogen of crops based on multi-information integration of high spectral images and fluorescent images. The device consists of a lens change switch a (1), a lens change switch b (2), a computer (3), a luminoscope main control unit (4), a MINI lens (5), an LED (light emitting diode) light source (6), a high spectroscopic light source control unit (7), a stepper motor controller (8), a visible light lens (9), symmetrically placed visible light tubes (10), a visible light source (11), an objective table (12), an object moving table (13), an image acquisition card (14), a motor (15), a lighting chamber (16), a stepper motor a (17) and a stepper motor b (18). With the adoption of the nutrient diagnosis device and the nutrient diagnosis method, the prediction accuracy of the nutrients of the crops can be greatly improved, and the actual level of the nitrogen can be detected earlier in time, so that the scientific management level of the cultivation can be improved.
Description
Technical field
The present invention relates to the nitrogen nutrition Non-Destructive Testing of agricultural product, specifically a kind of device and method utilizing EO-1 hyperion fluoroscopic image technology to carry out Non-Destructive Testing crop nitrogen nutrition content.
Background technology
Nitrogen plays an important role to plant physiology metabolism and growth fertility, it is one of main restriction factor affecting plant growth, high to nitrogenous fertilizer nutritional requirement during plant growth, nitrogen stress can suppress the differentiation of blade, the number of blade is reduced, also can produce adverse influence to the nutritional quality of crop, output; Nitrogen application is too much, then easily cause groundwater contamination and soil pollution and degeneration, therefore the yield and quality of Rational Application nitrogenous fertilizer to crop is most important.And accurate N-fertilizer management is to carry out precise monitoring and to be detected as basis to crop alimentary element.
For a long time, the Nitrogen Nutrition Diagnosis of crop is all based on laboratory conventionally test, and mainly contain morphological diagnosis, blade card method, chemical diagnosis and enzymology diagnosis method etc., these have damage method measuring accuracy low, poor in timeliness, affects plant growth and is unfavorable for applying.Computer vision technique mainly utilizes the Macroscopic physical characteristics such as the color of blade or canopy, texture, form to diagnose, spectroscopic diagnostics technology mainly utilizes the change of the spectral reflectivity of characteristic wave strong point to carry out the nutrition condition of inverting crop, fully cannot characterize characteristic information abundant when crop alimentary wanes, and adopt the sample mode of point source also to make it well cannot embody the anisotropy of plant, therefore the error of testing result is comparatively large, thus constrains the application of crop alimentary high precision lossless detection method.
Make a general survey of the present Research of domestic and international crop nutrition diagnosis, no matter spectrum detection technique or computer vision technique, when carrying out crop nutrition diagnosis, many research is mainly for the identification and the diagnosis that lack nutrient symptom, even carry out quantitative Diagnosis to trophic level, precision is also low.
The main cause that computer vision technique accuracy of identification is low is: at present computer vision technique only extracts the external feature of the blades such as color (gray scale), texture and morphology within the scope of 400 ~ 1000nm usually.And it is inadequate to only rely on specific several band spectrum image exact inversion crop nitrogen nutrition cogency, such as multi-optical spectrum image collecting equipment can only carry out synchronous acquisition 4 wave band components to R, G, B, IR tetra-passages, and its content comprised far is not enough to the information overall picture that summary crop sends.
The main cause that spectral technique accuracy of identification is low is: the nitrogen that spectral technique is applied to crop detects and achieves comparatively successfully achievement in research, and the change of the spectral reflectivity feature mainly utilizing the change of chlorophyll and inner organic organization to cause is carried out detecting indirectly.Also there is reciprocation between leaf water and nutrition, and spectral detection adopts the mode of some source sampling, what embody is the statistical average of sample areas spectrum in field range, by the spectral reflectivity compositive inversion crop nitrogen level of some characteristic wavelengths, therefore, the anisotropy feature inside and outside blade cannot be reflected.
In addition, no matter be spectrum or visual pattern, its principle is all that the quantification of blade surface reflected light in each sensor apparatus (comprising spectral instrument, Image-forming instrument) presents in fact.And crop nitrogen nutrition lacks, first can cause the change of crop interior tissue Physiology and biochemistry, particularly cause chlorophyll content to decline, blade surface form starts to change, blade surface color starts to engender jaundice, and texture also starts to change, and this wherein has process individual blink.At the crop nitrogen stress initial stage, crop leaf form, color, lightness still do not show exception, and when crop leaf morphology Symptoms, nitrogen stress causes impact to plant growth, growth.So utilize spectral technique and image technique, accurately detect nitrogen level difficulty in time at the crop nitrogen stress initial stage larger.
Summary of the invention
The object of the invention is to solve above-mentioned Problems existing, a kind of crop Nitrogen nutritional status device and method based on high spectrum image and fluoroscopic image Multi-information acquisition is provided.
The object of the invention is to realize as follows: a kind of crop Nitrogen nutritional status device based on high spectrum image and fluoroscopic image Multi-information acquisition, it is characterized in that exchanging switch a by camera lens, switch b exchanged by camera lens, computing machine, luminoscope main control unit, MINI camera lens, LED light source, EO-1 hyperion light source control unit, controllor for step-by-step motor, visible light lens, the symmetrical visible fluorescent tube placed, visible light source, objective table, move thing platform, image pick-up card, motor, daylighting room, stepper motor a, stepper motor b forms, wherein MINI camera lens is exchanged switch a with camera lens be connected by stepper motor a, visible light lens is exchanged switch b by stepper motor b with camera lens and is connected, LED light source is connected with computing machine by luminoscope main control unit, controllor for step-by-step motor by motor with move thing platform and be connected, visible light lens and objective table distance are 50cm, MINI camera lens and objective table distance are 7cm.
The detection method of described pick-up unit, comprises the steps:
1) fluoroscopic image is gathered: by blade dark adatpation to be measured after 20 minutes, be placed on objective table, press camera lens and exchange switch a, MINI camera lens moves to above objective table, moving calculation machine, now LED light source sends faint measurement light, measure maximum amount suboutput, then a saturation pulse light is sent every 20s LED light source, and record fluorescence parameter now, measure fluorescence induction curves, then send an actinic light every 10s LED light source, actinic light raises gradually from low to high and starts to measure fast light response curve;
2) high spectrum image collection: press camera lens and exchange switch b, visible light lens moves to above objective table, moving calculation machine, measures blade high spectrum image to be measured;
3) utilize Analysis of test results modeling and predict the nitrogen nutrition of crop: first, image information portion is extracted from hyperspectral image data, then the high spectrum image found out under the characteristic wavelength and characteristic wavelength that can reflect LTN content to be measured is analyzed, from the high spectrum image these characteristic wavelengths, then utilize image processing method to extract the characteristic parameter that can reflect nitrogen content, finally set up the model of LTN content to be measured, the fluorescence parameter data analysis statistical collected is gone out the change curve of fluorescence parameter with amount of nitrogen, find out the best amount of nitrogen of blade to be measured, the model that the best amount of nitrogen obtained according to fluorescence parameter and EO-1 hyperion obtain, thus judge the nitrogen content of vegetables to be measured and whether lack nitrogen nutrition.
The invention has the beneficial effects as follows:
The present invention can characterize the fluoroscopic image information of crop leaf internal motivation physiologically active in time, with can reflect crop appearance texture, brightness, the high spectrum image information of texture is assisted mutually, mutual fusion, take full advantage of the advantage of high spectrum image and fluoroscopic image, expand the validity feature space that nitrogen detects, seek the comprehensive nitrogen detection method of high precision, be expected to the precision of prediction significantly improving crop alimentary element, can detection nitrogen real standard more early in time, cultivation management level will be improved, simultaneously also for the accurate management of other crop nitrogen nutritions provides the footpath that can reflect.
Accompanying drawing explanation
In order to make content of the present invention more easily be clearly understood, below according to specific embodiment also by reference to the accompanying drawings, the present invention is further detailed explanation, wherein
Fig. 1 is structure of the detecting device schematic diagram of the present invention
Fig. 2 is the process flow diagram of detection method
embodiment:
Following examples are described in further detail the present invention.
The present invention is based on the crop Nitrogen nutritional status device of high spectrum image and fluoroscopic image Multi-information acquisition, switch a1 is exchanged by camera lens, switch b2 exchanged by camera lens, computing machine 3, luminoscope main control unit 4, MINI camera lens 5, LED light source 6, EO-1 hyperion light source control unit 7, controllor for step-by-step motor 8, visible light lens 9, the symmetrical visible fluorescent tube 10 placed, visible light source 11, objective table 12, move thing platform 13, image pick-up card 14, motor 15, daylighting room 16, stepper motor a17, stepper motor b18 forms, wherein MINI camera lens 5 is exchanged switch a1 by stepper motor a17 with camera lens and is connected, visible light lens 9 is exchanged switch b2 by stepper motor b18 with camera lens and is connected, LED light source 6 is connected with computing machine 3 by luminoscope main control unit 4, controllor for step-by-step motor 8 by motor 15 with move thing platform 13 and be connected, visible light lens 9 and objective table 12 are apart from being 50cm, MINI camera lens 5 and objective table 12 are apart from being 7cm.
Detection method, comprise the steps: 1) gather fluoroscopic image: by romaine lettuce dark adatpation after 20 minutes, be placed on objective table 12, press camera lens and exchange switch a1, MINI camera lens 5 moves to above objective table 12, software I magingWin.exe on moving calculation machine, selection window top Setting tab, according to the parameter value on imaging probe, red gain in Absorptivity, red intensity, NIR intensity are set respectively, intense and gain of Meas. Light is set, makes the fluorescent value in AOI region between 0.1-0.2.Now LED light source sends faint measurement light, the F0 below window, Fm button, measures maximum amount suboutput Fv/Fm.Selection window top Kinetic tab, clicks the Start button on the right side of window, sends a saturation pulse light every 20s LED light source, and record fluorescence parameter now, measure fluorescence induction curves.Selection window top Light Curve tab, clicks Start button, now sends an actinic light every 10s LED light source, and actinic light raises gradually from low to high and starts to measure fast light response curve.
2) high spectrum image collection: press camera lens and exchange switch b2, visible light lens 9 moves to above objective table 12, moving calculation machine software Spectral Image System Demo visible ray software.Relevant parameter is set and carries out blank demarcation.Click move and measure romaine lettuce high spectrum image.
3) utilize Analysis of test results modeling and predict the nitrogen nutrition of crop: high spectrum image collection is taken the sample on objective table by camera, computing machine is imported into through image pick-up card, fluoroscopic image is taken the sample on objective table by camera, imports computing machine into through main control unit.First, from hyperspectral image data, image information portion is extracted; Then, the many algorithms such as major component is analysed, wavelet analysis and uneven second order difference are adopted to analyze, find out the high spectrum image under the characteristic wavelength and characteristic wavelength that can reflect romaine lettuce nitrogen content, from the high spectrum image these characteristic wavelengths, then utilize the image processing method such as filtering and noise reduction, skin texture detection to extract the characteristic parameter that can reflect nitrogen content.Finally adopt the model of the method establishment prediction romaine lettuce nitrogen contents such as multiple stepwise regression, partial least squares regression and neural network.The fluorescence parameter data acquisition Excel/SPSS11.5 software collected is carried out statistical study, count the change curve of fluorescence parameter with amount of nitrogen, find out the best amount of nitrogen of romaine lettuce, the model that the best amount of nitrogen obtained according to fluorescence parameter and EO-1 hyperion obtain, thus judge nitrogen content and whether lack nitrogen nutrition.
Claims (2)
1. the crop Nitrogen nutritional status device based on high spectrum image and fluoroscopic image Multi-information acquisition, it is characterized in that exchanging switch a by camera lens, switch b exchanged by camera lens, computing machine, luminoscope main control unit, MINI camera lens, LED light source, EO-1 hyperion light source control unit, controllor for step-by-step motor, visible light lens, the symmetrical visible fluorescent tube placed, visible light source, objective table, move thing platform, image pick-up card, motor, daylighting room, stepper motor a, stepper motor b forms, wherein MINI camera lens is exchanged switch a with camera lens be connected by stepper motor a, visible light lens is exchanged switch b by stepper motor b with camera lens and is connected, LED light source is connected with computing machine by luminoscope main control unit, controllor for step-by-step motor by motor with move thing platform and be connected, visible light lens and objective table distance are 50cm, MINI camera lens and objective table distance are 7cm.
2. the detection method of pick-up unit according to claim 1, is characterized in that comprising the steps:
1) fluoroscopic image is gathered: by blade dark adatpation to be measured after 20 minutes, be placed on objective table, press camera lens and exchange switch a, MINI camera lens moves to above objective table, moving calculation machine, now LED light source sends faint measurement light, measure maximum amount suboutput, then a saturation pulse light is sent every 20s LED light source, and record fluorescence parameter now, measure fluorescence induction curves, then send an actinic light every 10s LED light source, actinic light raises gradually from low to high and starts to measure fast light response curve;
2) high spectrum image collection: press camera lens and exchange switch b, visible light lens moves to above objective table, moving calculation machine, measures blade high spectrum image to be measured;
3) utilize Analysis of test results modeling and predict the nitrogen nutrition of crop: first, image information portion is extracted from hyperspectral image data, then the high spectrum image found out under the characteristic wavelength and characteristic wavelength that can reflect LTN content to be measured is analyzed, from the high spectrum image these characteristic wavelengths, then utilize image processing method to extract the characteristic parameter that can reflect nitrogen content, finally set up the model of LTN content to be measured, the fluorescence parameter data analysis statistical collected is gone out the change curve of fluorescence parameter with amount of nitrogen, find out the best amount of nitrogen of blade to be measured, the model that the best amount of nitrogen obtained according to fluorescence parameter and EO-1 hyperion obtain, thus judge the nitrogen content of vegetables to be measured and whether lack nitrogen nutrition.
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| US20140321714A1 (en) * | 2013-04-24 | 2014-10-30 | Billy R. Masten | Methods of enhancing agricultural production using spectral and/or spatial fingerprints |
| CN104865194A (en) * | 2015-04-03 | 2015-08-26 | 江苏大学 | Detection apparatus and method for pesticide residues in vegetable based on near infrared, fluorescence and polarization multi-spectrum |
| CN105548113B (en) * | 2015-12-31 | 2019-04-16 | 浙江大学 | A kind of plant physiology monitoring method based on chlorophyll fluorescence and multispectral image |
| US12019022B2 (en) | 2018-06-06 | 2024-06-25 | Monsanto Technology Llc | Systems and methods for distinguishing fertile plant specimens from sterile plant specimens |
| CN112418073B (en) * | 2020-11-19 | 2023-10-03 | 安徽农业大学 | Wheat plant nitrogen content estimation method based on unmanned aerial vehicle image fusion characteristics |
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| US7787111B2 (en) * | 2007-04-25 | 2010-08-31 | The United States Of America As Represented By The Secretary Of Agriculture | Simultaneous acquisition of fluorescence and reflectance imaging techniques with a single imaging device for multitask inspection |
| CN102323267A (en) * | 2011-08-10 | 2012-01-18 | 中国农业大学 | System and method used for rapidly evaluating freshness of raw meat products |
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| US7787111B2 (en) * | 2007-04-25 | 2010-08-31 | The United States Of America As Represented By The Secretary Of Agriculture | Simultaneous acquisition of fluorescence and reflectance imaging techniques with a single imaging device for multitask inspection |
| CN102323267A (en) * | 2011-08-10 | 2012-01-18 | 中国农业大学 | System and method used for rapidly evaluating freshness of raw meat products |
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| 基于叶片叶绿素荧光参数的麦稻氮素营养监测研究;马吉锋;《中国优秀硕士学位论文全文数据库 农业科技辑》;20071215(第6期);第11,18-24页 * |
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