CN105092532A - Method for rapid detection of corn leaf blight by portable non-contact laser - Google Patents
Method for rapid detection of corn leaf blight by portable non-contact laser Download PDFInfo
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- CN105092532A CN105092532A CN201410202429.9A CN201410202429A CN105092532A CN 105092532 A CN105092532 A CN 105092532A CN 201410202429 A CN201410202429 A CN 201410202429A CN 105092532 A CN105092532 A CN 105092532A
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Abstract
The present invention provides a method for rapid detection of corn leaf blight by portable non-contact laser, the portable non-contact laser includes an optical data collector, a laser irradiation device, a laser generating controller and a data analyzer, in corn growth stage, the laser generating controller wavelength is adjusted in the visible light range of 500nm-760nm, the 500nm-760nm visible light is divided into two zones comprising a 500nm-650nm zone and a 700nm-760nm zone, the laser irradiation device irradiates different positions of a same leaf, different data is collected, data collected by the optical data collector is sent to the data analyzer, the data is analyzed and calculated by the data analyzer to form a light reflectance curve, the degree of the disease incidence increases with the increase of the r reflectance, and the disease incidence can be calculated by continuous detection of a plurality of leaves of different plants to use as corn leaf blight prevention and treatment basis.
Description
Technical field
The invention provides a kind of method that portable non-contact laser detects the leaf blight of corn fast, belong to crops disease forecast forecasting technique field.
Background technology
China is a large agricultural country, maize diseases is one of major casualty, milpa is subject to the invasion of disease and pest, the mesophyll tissue of blade is impaired, causes photosynthetic tissue damaged, causes corn can not normal growth, production declining, quality declines, and according to estimates, the average loss that corn causes after suffering disease is 10 ~ 15% of total production.
The main of the current maize diseases of China carries out prediction by satellite remote sensing technology, satellite remote sensing technology resolution is low, expensive, poor in timeliness, owing to being subject to the impact of crop leaf canopy spatial structure, mulch, meteorological condition, degree of accuracy reduces, dependable with function is poor, the prediction of applicable large-area maize diseases, the plot for small size occurs, popular, great development, reaches Accurate Prediction forecast very difficult.
In present agricultural production, the leaf blight of corn determines whether to prevent and treat mainly through observing blade incidence, the method detected by an unaided eye generally not easily is observed at the initial stage of a disease, miss optimum control period, often there are some hysteresis phenomenon in Classical forecast forecast, cause the large generation of the leaf blight of corn, cause and drop in production over a large area, the large harm environment of dosage, causes the pollution grows worse of air and water.
Classic method is carried out Accurate Prediction forecast to leaf blight of corn period of disease and is become very difficult, observed by professional and technical personnel with eye, touch with hand and determine according to a preliminary estimate, the plant leaf gathering field morbidity measures to laboratory, optical microscope, transmission electron microscope observation diseased plant Spores volume morphing, size is adopted to diagnose the period of the state of an illness, complex procedures, detects sample time long.
Summary of the invention
The object of this invention is to provide that a kind of to overcome the Field sampling that existing leaf blight of corn microscopic examination exists loaded down with trivial details, detection time is long, provide a kind of portable field fast, harmless, random multiple spot, accurate and calculate the leaf blight of corn incidence of disease.
Laser detects the principle of the leaf blight of corn fast: laser visible ray has to be irradiated on maize leaf and has reflection, refraction, absorb and scattering phenomenon, under same soil fertility and natural conditions, in same breeding time, the chlorophyll content of the blade of same corn is substantially identical, blade reflects, refraction, absorb consistent with scattered light, maize leaf is subject to leaf blight strain, in mesophyll cell, leaf green reduces, blade reflects, absorption visible ray reduces, reflected light increases, by the reflectivity of correlation curve, the incidence of disease equals the ratio that morbidity units accounts for total investigation number, Pyatyi can be divided into, one-level is less than 5%, control optimal period, prevention effect is obvious, secondary 5 ~ 10%, morbidity is light, little to yield effect, three grade 10 ~ 20%, fall ill medium, large to yield effect, level Four 20 ~ 40%, morbidity is heavy, larger to yield effect, Pyatyi 40 ~ 50%, morbidity is serious, large especially to yield effect, judge the incidence of disease degree of the leaf blight of corn.
Its technical scheme is.
Comprise optical data acquisition device, laser irradiation device, laser generator controller, data-analyzing machine, optical data acquisition device is by optical lens and charge-coupled image sensor, it can be converted to digital signal optical image, laser generator controller is provided with wavelength regulation knob, data-analyzing machine calculates the data of optical data acquisition device by analysis, screen generates figure automatic analysis relatively and store, the light of a certain wavelength that laser generator controller produces is irradiated on the appointed area of tested maize leaf by laser irradiation device.
Described a kind of portable non-contact laser detects the method for the leaf blight of corn fast, regulates the wavelength regulation knob of laser generator controller, makes the wavelength of visible ray between 500nm ~ 650nm and 700nm ~ 760nm.
Described a kind of portable non-contact laser detects the method for the leaf blight of corn fast, select milpa blade, the different position of laser irradiation device 2 at the same blade of corn is moved, irradiate by different visible light within the scope of wavelength 500nm ~ 650nm and 700nm ~ 760nm, the reflectivity of the curve of the visible ray of maize leaf is observed at data-analyzing machine 5, the reflectivity of site of pathological change is higher than position, check plot, when the ratio of site of pathological change and check plot is about 5% time, the initial phase of the leaf blight of corn, for optimum control period, 5 ~ 10% time, the epizootic modeling of the leaf blight of corn, it is the large emergence period during more than 20%.
Described a kind of portable non-contact laser detects the method for the leaf blight of corn fast, sensitizing range 600nm and 750nm of different visible light within the scope of wavelength 500nm ~ 650nm and 700nm ~ 760nm.
Compared with prior art, tool has the following advantages in the present invention.
1, laser is utilized to irradiate maize leaf, after maize leaf is subject to disease, chlorophyll tissue wrecks, photosynthesis weakens, the hypofunctions such as nutrient and water absorption, transport, conversion, finally cause chlorophyll fluorescence, visible ray, near infrared, mid-infrared spectral behavior to change, in the scope of visible ray, the reflectivity that reflected light is formed increases, and curve difference is obvious.
2, portable non-contact laser detects the method for the leaf blight of corn fast, easy to operate, accurately and reliably, detects the incidence of disease of the leaf blight of corn in time, for field control provides reliable theoretical foundation.
, data-analyzing machine adopts computing machine and analysis software composition, by the reflectivity of comparison curves, analyzes incidence of disease degree.
accompanying drawing explanation.
Fig. 1 is the main TV structure schematic diagram of the embodiment of the present invention.
wherein in figure:1, optical data acquisition device 2, laser irradiation device 3, laser generator controller 4, adjusting knob 5, data-analyzing machine.
Embodiment.
A kind of portable non-contact laser detects the method for the leaf blight of corn fast, comprise optical data acquisition device 1, laser irradiation device 2, laser generator controller 3, data-analyzing machine 5 forms, optical data acquisition device 1 is by optical lens and charge-coupled image sensor, it can be converted to digital signal optical image, laser generator controller 3 can produce wavelength 500 ~ 760nm visible ray, laser generator controller 3 is provided with wavelength regulation knob 4, laser generator controller 3 is launched and determines visible light wavelengths, data-analyzing machine 5 is made up of computing machine and analysis software, the data that data-analyzing machine 5 collects optical data acquisition device 1, through wavelet transformation and matrixing, least square method is adopted to set up the Quantitative Analysis Model of leaf blight of corn laser spectrum reflectivity and optical maser wavelength, calculate synthetic image on screen by analysis and store, horizontal ordinate is laser wave long value, ordinate is laser spectrum reflected value, calculate leaf blight of corn reflectance value and corn not fall ill related coefficient between value, calculate the incidence of disease.
In the breeding time of corn, represent after field determines, can according to the characteristic of the leaf blight of corn and kind carry out field reconnaissance sampling detect, checkerboard type can be adopted, double diagonal line formula, Z-shaped, take out the form samplings such as row type, the corn of every sampling detects, in, the blade at lower three positions, the light of a certain wavelength that laser generator controller 3 produces is irradiated to by laser irradiation device 2 on the appointed area of tested maize leaf, the milpa blade that selection will detect, the different position of laser irradiation device 2 at the same blade of corn is moved, irradiate by different visible light within the scope of wavelength 400nm ~ 650nm and 700nm ~ 760nm, the reflectivity of the curve of the visible ray of maize leaf is observed at data-analyzing machine 5, the reflectivity of site of pathological change is higher than position, check plot, calculate the incidence of disease, when the ratio of site of pathological change and check plot is about 5% time, the initial phase of the leaf blight of corn, for optimum control period, 5 ~ 10% time, the epizootic modeling of the leaf blight of corn, it is the large emergence period during more than 20%, Pyatyi can be divided into, one-level is less than 5%, control optimal period, prevention effect is obvious, secondary 5 ~ 10%, morbidity is light, little to yield effect, three grade 10 ~ 20%, fall ill medium, large to yield effect, level Four 20 ~ 40%, morbidity is heavy, larger to yield effect, Pyatyi 40 ~ 50%, morbidity is serious, large especially to yield effect, judge the incidence of disease degree of the leaf blight of corn.
The blade of the milpa selected with the laser of 500nm wavelength, at the diverse location sampled point of same blade, observe the value of curve of reflected light, system-computed goes out a class value of the curve of reflected light and the ratio of another class value, within the scope of which rank of in Pyatyi.
The blade of the milpa selected with the laser of 530nm wavelength, at the diverse location sampled point of same blade, observe the value of curve of reflected light, system-computed goes out a class value of the curve of reflected light and the ratio of another class value, within the scope of which rank of in Pyatyi.
The blade of the milpa selected with the laser of 550nm wavelength, at the diverse location sampled point of same blade, observe the value of curve of reflected light, system-computed goes out a class value of the curve of reflected light and the ratio of another class value, within the scope of which rank of in Pyatyi.
The blade of the milpa selected with the laser of 600nm wavelength, at the diverse location sampled point of same blade, observe the value of curve of reflected light, system-computed goes out a class value of the curve of reflected light and the ratio of another class value, within the scope of which rank of in Pyatyi.
The blade of the milpa selected with the laser of 650nm wavelength, at the diverse location sampled point of same blade, observe the value of curve of reflected light, system-computed goes out a class value of the curve of reflected light and the ratio of another class value, within the scope of which rank of in Pyatyi.
The blade of the milpa selected with the laser of 680nm wavelength, at the diverse location sampled point of same blade, observe the value of curve of reflected light, system-computed goes out a class value of the curve of reflected light and the ratio of another class value, within the scope of which rank of in Pyatyi.
The blade of the milpa selected with the laser of 700nm wavelength, at the diverse location sampled point of same blade, observe the value of curve of reflected light, system-computed goes out a class value of the curve of reflected light and the ratio of another class value, within the scope of which rank of in Pyatyi.
The blade of the milpa selected with the laser of 720nm wavelength, at the diverse location sampled point of same blade, observe the value of curve of reflected light, system-computed goes out a class value of the curve of reflected light and the ratio of another class value, within the scope of which rank of in Pyatyi.
The blade of the milpa selected with the laser of 750nm wavelength, at the diverse location sampled point of same blade, observe the value of curve of reflected light, system-computed goes out a class value of the curve of reflected light and the ratio of another class value, within the scope of which rank of in Pyatyi.
The observation ratio value of wavelength of 600nm and 750nm sensitizing range and the ratio value of the wavelength in non-sensitive district contrast.
The above; it is only preferred embodiment of the present invention; it is not restriction the present invention being made to other form; any those skilled in the art may utilize the technology contents of above-mentioned announcement to be changed or be modified as the Equivalent embodiments of equivalent variations; everyly do not depart from technical solution of the present invention content; according to any simple modification, equivalent variations and remodeling that the technical spirit of type of the present invention is done above embodiment, still belong to the protection domain of type technical scheme of the present invention.
Claims (4)
1. a portable non-contact laser detects the method for the leaf blight of corn fast, comprise optical data acquisition device (1), laser irradiation device (2), laser generator controller (3), data-analyzing machine (5), it is characterized in that: optical data acquisition device (1) is by optical lens and charge-coupled image sensor, it can be converted to digital signal optical image, laser generator controller (3) is provided with wavelength regulation knob (4), data-analyzing machine (5) calculates the data of optical data acquisition device (1) by analysis and on screen, to generate figure automatic analysis relatively and store, the light of a certain wavelength that laser generator controller (3) produces is irradiated on the appointed area of tested maize leaf by laser irradiation device (2).
2. a kind of portable non-contact laser as claimed in claim 1 detects the method for the leaf blight of corn fast, it is characterized in that: the wavelength regulation knob (4) regulating laser generator controller (3), makes the wavelength of visible ray between 500nm ~ 650nm and 700nm ~ 760nm.
3. a kind of portable non-contact laser as claimed in claim 1 detects the method for the leaf blight of corn fast, it is characterized in that: select milpa blade, the different position of laser irradiation device (2) at the same blade of corn is moved, irradiate by different visible light within the scope of wavelength 500nm ~ 650nm and 700nm ~ 760nm, the reflectivity of the curve of the visible ray of maize leaf is observed at data-analyzing machine (5), the reflectivity of site of pathological change is higher than position, check plot, when the ratio of site of pathological change and check plot is about 5% time, the initial phase of the leaf blight of corn, for optimum control period, 5 ~ 10% time, the epizootic modeling of the leaf blight of corn, it is the large emergence period during more than 20%.
4. a kind of portable non-contact laser as claimed in claim 1 detects the method for the leaf blight of corn fast, it is characterized in that: sensitizing range 600nm and 750nm of different visible light within the scope of wavelength 500nm ~ 650nm and 700nm ~ 760nm.
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Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105866336A (en) * | 2016-04-18 | 2016-08-17 | 王立云 | Handheld plant detector |
CN112098369A (en) * | 2020-08-18 | 2020-12-18 | 杭州电子科技大学 | Apple flower detection method and device for mould core disease based on diffuse reflection light |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPS6244651A (en) * | 1985-08-22 | 1987-02-26 | Canon Inc | Particle analyzing device |
EP0434644A2 (en) * | 1989-12-22 | 1991-06-26 | Consiglio Nazionale Delle Ricerche | Instrument for the two-channel measurement of the fluorescence of chlorophyll |
CN201298021Y (en) * | 2008-11-27 | 2009-08-26 | 吉林大学 | Plant chlorophyll-measuring instrument |
CN102628800A (en) * | 2012-03-26 | 2012-08-08 | 北京农业智能装备技术研究中心 | Method and system for measuring plant leaf chlorophyll content |
-
2014
- 2014-05-14 CN CN201410202429.9A patent/CN105092532A/en active Pending
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPS6244651A (en) * | 1985-08-22 | 1987-02-26 | Canon Inc | Particle analyzing device |
EP0434644A2 (en) * | 1989-12-22 | 1991-06-26 | Consiglio Nazionale Delle Ricerche | Instrument for the two-channel measurement of the fluorescence of chlorophyll |
CN201298021Y (en) * | 2008-11-27 | 2009-08-26 | 吉林大学 | Plant chlorophyll-measuring instrument |
CN102628800A (en) * | 2012-03-26 | 2012-08-08 | 北京农业智能装备技术研究中心 | Method and system for measuring plant leaf chlorophyll content |
Non-Patent Citations (2)
Title |
---|
JAN U.H. EITEL ET AL: "Early season remote sensing of wheat nitrogen status using a green scanning laser", 《AGRICULTURAL AND FOREST METEOROLOGY》 * |
陈兵等: "病害胁迫对棉叶光谱反射率和叶绿素荧光特性的影响", 《农业工程学报》 * |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105866336A (en) * | 2016-04-18 | 2016-08-17 | 王立云 | Handheld plant detector |
CN112098369A (en) * | 2020-08-18 | 2020-12-18 | 杭州电子科技大学 | Apple flower detection method and device for mould core disease based on diffuse reflection light |
CN112098369B (en) * | 2020-08-18 | 2023-08-29 | 杭州电子科技大学 | Method and device for detecting apple flowers with moldy heart disease based on diffuse reflection light |
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