CN110567891B - Winter wheat canopy chlorophyll estimation system and method - Google Patents
Winter wheat canopy chlorophyll estimation system and method Download PDFInfo
- Publication number
- CN110567891B CN110567891B CN201910869692.6A CN201910869692A CN110567891B CN 110567891 B CN110567891 B CN 110567891B CN 201910869692 A CN201910869692 A CN 201910869692A CN 110567891 B CN110567891 B CN 110567891B
- Authority
- CN
- China
- Prior art keywords
- green
- red
- spectral reflectance
- blue
- spectral
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/27—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection ; circuits for computing concentration
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/27—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection ; circuits for computing concentration
- G01N21/274—Calibration, base line adjustment, drift correction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Theoretical Computer Science (AREA)
- Immunology (AREA)
- Mathematical Optimization (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Pathology (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Data Mining & Analysis (AREA)
- Pure & Applied Mathematics (AREA)
- Algebra (AREA)
- Operations Research (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Image Processing (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention discloses a winter wheat canopy chlorophyll estimation system and method, wherein a six-axis rotor unmanned aerial vehicle with flexibility, high efficiency and high speed is used for carrying a multispectral camera containing five kinds of spectral information to acquire winter wheat spectral images with high precision and high space-time resolution, accurate spectral reflectivity data is obtained after gray plate reflectivity is calibrated, various vegetation indexes are obtained through spectral vegetation coefficient equation set operation, an optimal model for estimating the critical growth period of the winter wheat canopy chlorophyll content is established through a stepwise regression analysis method, and finally a low-error estimation value of the winter wheat canopy chlorophyll content is obtained, so that an effective technical means is provided for accurate fertilization management of farmlands.
Description
Technical Field
The invention relates to the technical field of agricultural remote sensing, in particular to a system and a method for estimating chlorophyll of a winter wheat canopy.
Background
Wheat is the grain crop with the largest sowing area and the largest yield in the world, and the growth condition and the yield condition influence the grain safety in the world. The diagnosis of the growth condition of the crops is linked by the difference between nutrient elements and spectra, chlorophyll is an important indicator factor of plant photosynthesis and nitrogen condition, and the research on the change characteristics of the chlorophyll content of the crops in different growth periods provides a basis for rapid diagnosis of fertilization. Currently, the estimation of the chlorophyll content of crops by using a remote sensing technology becomes a research hotspot in the field of remote sensing, and the change characteristic of the chlorophyll content is mainly predicted by measuring the spectral characteristics of the crops by using various spectrometers and sensors in different scales.
In ground measurement, a surface feature spectrometer is commonly used for measuring the spectrum of the crop canopy, but the monitoring range is very limited, and only the spectrum data of a certain point or a sample point in a very small range can be obtained. The satellite remote sensing can acquire spectral data of crop images in a large range, but due to the limitation of time and spatial resolution, the crop key phenological period data in a research area are often lacked, the flexibility, the high efficiency and the high cost are lacked, so that the precision aim of precision agriculture is difficult to achieve, and the requirement of field nitrogen fertilizer management is met.
Disclosure of Invention
Aiming at the defects in the prior art, the system and the method for estimating the chlorophyll of the winter wheat canopy provided by the invention solve the problem that the prior remote sensing technology cannot give consideration to the monitoring range and the accuracy so as not to accurately diagnose the chlorophyll content of the wheat canopy.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: a winter wheat canopy chlorophyll estimation system comprises an unmanned aerial vehicle, a multispectral camera, a gray board and a light intensity sensor;
the multispectral camera is carried on the unmanned aerial vehicle and used for collecting the reflectivity image of winter wheat;
the light intensity sensor is fixedly connected with the multispectral camera and used for calibrating the reflectivity of the multispectral camera;
the gray board is used for performing reflectivity calibration of the multispectral camera in cooperation with the light intensity sensor;
the unmanned aerial vehicle is used for bearing the multispectral camera and flying off the ground.
Further: unmanned aerial vehicle is six rotor unmanned aerial vehicle.
Further: the multispectral camera is provided with five spectral channels with central wavelengths of 475nm, 560nm, 668nm, 717nm and 840nm respectively.
A winter wheat canopy chlorophyll estimation method comprises the following steps:
s1, selecting clear and cloudless weather, and carrying out ground-off flight by adopting an unmanned aerial vehicle to carry the multispectral camera;
s2, simultaneously collecting spectrum data of the winter wheat canopy by adopting five spectrum channels of the multispectral camera to obtain uncalibrated blue light spectrum reflectivity R'blueUncorrected green spectral reflectance R'greenUncorrected red spectral reflectance R'redUncalibrated red-edge spectral reflectance R'rededgeAnd uncalibrated near infrared spectral reflectance R'nir；
S3, performing photometry on the gray board by using a light intensity sensor to obtain a light intensity value, and correcting the uncalibrated blue light spectrum reflectivity R 'according to the light intensity value'blueUncorrected green spectral reflectance R'greenACalibrated Red spectral reflectance R'redUncalibrated red-edge spectral reflectance R'rededgeAnd uncalibrated near infrared spectral reflectance R'nirObtaining the reflectivity R of the blue light spectrumblueGreen spectral reflectance RgreenRed spectral reflectance RredRed edge spectral reflectance RrededgeAnd near infrared spectral reflectance Rnir；
S4, according to the spectral vegetation coefficient equation set and according to the blue spectral reflectivity RblueGreen spectral reflectance RgreenRed spectral reflectance RredRed edge spectral reflectance RrededgeAnd near infrared spectral reflectance RnirCalculating to obtain a vegetation coefficient set;
and S5, calculating according to the growth season of the winter wheat according to the vegetation coefficient set by utilizing a stepwise regression chlorophyll model to obtain an estimated value of the winter wheat canopy chlorophyll content SPAD.
Further: in step S1, the time period for the unmanned aerial vehicle to fly is: a time period between 10 and 12 am.
Further: in the step S1, the flying height of the unmanned aerial vehicle flying off the ground is 30-80 m, and the flying speed is 1-6 m/S.
Further: step S2 includes the following steps:
s21, periodically photographing the wheat field by adopting five spectral channels of the multispectral camera to obtain 5 XN image pictures, wherein N is the number of the image pictures of a single channel;
s22, splicing the respective N image maps of each spectral channel to obtain the wheat field reflectivity images of the five spectral channels with the central wavelengths of 475nm, 560nm, 668nm, 717nm and 840nm respectively;
s23, extracting uncalibrated blue spectral reflectance R 'from wheat field reflectance images of five spectral channels with center wavelengths of 475nm, 560nm, 668nm, 717nm and 840nm respectively'blueUncorrected green spectral reflectance R'greenUncorrected red spectral reflectance R'redUncalibrated red-edge spectral reflectance R'rededgeAnd uncalibrated near infrared spectral reflectance R'nir。
Further: the vegetation coefficient set in step S4 includes: chlorophyll absorption ratio index CARI, normalized blue-green difference index NGBDI, triangular vegetation index TVI, improved simple ratio vegetation index MSR, green band optimized soil adjusted vegetation index GOSAVI, green normalized vegetation index GNDVI and red band atmospheric impedance vegetation index VARIred.
Further: the system of spectral vegetation coefficient equations in step S4 includes the following equations:
CARI＝(Rrededge-Rred)-0.2×(Rrededge+Rred) (1)
NGBDI＝(Rgreen-Rblue)/(Rgreen+Rblue) (2)
TVI＝0.5×[120×(Rnir-Rgreen)-200×(Rred-Rgreen)] (3)
GOSAVI＝1.16×[(Rnir-Rgreen)/(Rnir+Rgreen+0.16)] (5)
GNDVI＝(Rnir-Rgreen)/(Rnir+Rgreen) (6)
VARIred＝(Rrededge-1.7×Rred+0.7×Rblue)/(Rrededge+2.3×Rred-1.3×Rblue) (7)。
further: the stepwise regression chlorophyll model in step S5 is:
the invention has the beneficial effects that: the method comprises the steps of carrying out high-precision and high-time-space-resolution winter wheat spectral image acquisition by utilizing a six-axis rotor unmanned aerial vehicle which is flexible, efficient and fast to move and is provided with a multispectral camera containing five kinds of spectral information, obtaining accurate spectral reflectivity data after gray plate reflectivity is calibrated, obtaining various vegetation indexes through spectral vegetation coefficient equation set calculation, constructing an optimal model for estimating a key growth period of the chlorophyll content of the winter wheat canopy through a stepwise regression analysis method, and finally obtaining a low-error estimation value of the chlorophyll content of the winter wheat canopy of the wheat field, so that an effective technical means is provided for accurate fertilization management of the farmland.
Drawings
FIG. 1 is a block diagram of a winter wheat canopy chlorophyll estimation system
FIG. 2 is a schematic flow chart of a winter wheat canopy chlorophyll estimation method
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
In one embodiment of the present invention, as shown in fig. 1, a winter wheat canopy chlorophyll estimation system includes an unmanned aerial vehicle, a multispectral camera, a gray board, and a light intensity sensor;
the multispectral camera is of a RedEdge-M model, is provided with five spectral channels with central wavelengths of 475nm, 560nm, 668nm, 717nm and 840nm respectively, is used for collecting reflectivity images of winter wheat, and is fixedly connected with the unmanned aerial vehicle; the light intensity sensor is used for calibrating the reflectivity of the multispectral camera and is fixedly connected with the multispectral camera; the gray board is used for performing reflectivity calibration of the multispectral camera in cooperation with the light intensity sensor, and the preferred size is 30cm multiplied by 30 cm; the unmanned aerial vehicle is a six-shaft rotor unmanned aerial vehicle and is used for bearing a multispectral camera and flying off the ground.
As shown in fig. 2, a method for estimating chlorophyll in canopy of winter wheat corresponding to the system comprises the following steps:
s1, selecting clear and cloudless weather, and carrying out off-ground flight by adopting an unmanned aerial vehicle to carry the multispectral camera in a time period between 10 and 12 am;
in the step S1, the flying height of the unmanned aerial vehicle flying off the ground is 30-80 meters, and preferably 60 m; the flying speed is 1-6 m/s, preferably 3 m/s.
S2, simultaneously collecting spectrum data of the winter wheat canopy by adopting five spectrum channels of the multispectral camera to obtain uncalibrated blue light spectrum reflectivity R'blueUncorrected green spectral reflectance R'greenUncorrected red spectral reflectance R'redUncalibrated red-edge spectral reflectance R'rededgeAnd uncalibrated near infrared spectral reflectance R'nir；
The step S2 includes the steps of:
s21, periodically photographing the wheat field by adopting five spectral channels of the multispectral camera to obtain 5 multiplied by N image maps, wherein N is the number of the image maps of a single channel, and preferably 5 multiplied by 335 image maps;
s22, splicing the respective N image maps of each spectral channel by adopting Pix4Dmap image processing software to obtain the wheat field reflectivity images of the spectral channels with the central wavelengths of 475nm, 560nm, 668nm, 717nm and 840nm respectively;
s23, extracting uncalibrated blue spectral reflectance R 'from wheat field reflectance images of spectral channels with center wavelengths of 475nm, 560nm, 668nm, 717nm and 840nm respectively'blueUncorrected green spectral reflectance R'greenUncorrected red spectral reflectance R'redUncalibrated red-edge spectral reflectance R'rededgeAnd uncalibrated near infrared spectral reflectance R'nir。
S3, performing photometry on the gray board by using a light intensity sensor to obtain a light intensity value, and correcting the uncalibrated blue light spectrum reflectivity R 'according to the light intensity value'blueUncorrected green spectral reflectance R'greenUncorrected red spectral reflectance R'redUncalibrated red-edge spectral reflectance R'rededgeAnd uncalibrated near infrared spectral reflectance R'nirObtaining the reflectivity R of the blue light spectrumblueGreen spectral reflectance RgreenRed spectral reflectance RredRed edge spectral reflectance RrededgeAnd near infrared spectral reflectance Rnir；
S4, using the spectral vegetation coefficient equation set to reflect the R according to the blue spectrumblueGreen spectral reflectance RgreenRed spectral reflectance RredRed edge spectral reflectance RrededgeAnd near infrared spectral reflectance RnirCalculating to obtain a vegetation coefficient set;
the vegetation coefficient set includes: chlorophyll absorption ratio index CARI, normalized blue-green difference index NGBDI, triangular vegetation index TVI, improved simple ratio vegetation index MSR, green band optimized soil adjusted vegetation index GOSAVI, green normalized vegetation index GNDVI and red band atmospheric impedance vegetation index VARIred.
The system of spectral vegetation coefficient equations in step S4 includes the following equations:
CARI＝(Rrededge-Rred)-0.2×(Rrededge+Rred) (1)
NGBDI＝(Rgreen-Rblue)/(Rgreen+Rblue) (2)
TVI＝0.5×[120×(Rnir-Rgreen)-200×(Rred-Rgreen)] (3)
GOSAVI＝1.16×[(Rnir-Rgreen)/(Rnir+Rgreen+0.16)] (5)
GNDVI＝(Rnir-Rgreen)/(Rnir+Rgreen) (6)
VARIred＝(Rrededge-1.7×Rred+0.7×Rblue)/(Rrededge+2.3×Rred-1.3×Rblue) (7)
and S5, calculating according to the growth season of the winter wheat according to the vegetation coefficient set by utilizing a stepwise regression chlorophyll model to obtain an estimated value of the winter wheat canopy chlorophyll content SPAD.
The stepwise regression chlorophyll model in step S5 is:
according to the method, a six-axis rotor unmanned aerial vehicle with flexibility, high efficiency and high speed is used for carrying a multi-spectral camera containing five kinds of spectral information to carry out high-precision and high-space-time resolution winter wheat spectral image acquisition, accurate spectral reflectivity data is obtained after gray plate reflectivity is calibrated, various vegetation indexes are obtained through spectral vegetation coefficient equation set calculation, an optimal model for estimating the key growth period of the chlorophyll content of the winter wheat canopy is constructed through a stepwise regression analysis method, and finally a low-error estimation value of the chlorophyll content of the winter wheat canopy of the wheat field is obtained, so that an effective technical means is provided for accurate fertilization management of the farmland.
TABLE 1 chlorophyll estimation test results table
The system and the method disclosed by the invention are applied to the wheat fields of the great-rise test base (39 degrees, 37.25 'N and 116 degrees, 25.51' E) of the Chinese institute of Water conservancy and hydropower science, the estimated value of the chlorophyll content of the canopy of the winter wheat is obtained, the estimated value is compared with the actual value of the chlorophyll content of the canopy of the winter wheat measured by a SPAD-502 type chlorophyll meter, the table 1 is obtained, wherein the coefficient R is determined2The root mean square error RMSE and the relative error RE are taken as indexes of the error between the estimated value and the actual value, R2The closer to 1, the smaller the RMSE and the smaller the RE, the better the estimation. As can be seen from Table 1, the present invention has excellent effects in the evaluation of wheat in various growth periods.
Claims (6)
1. The estimation method of chlorophyll of the winter wheat canopy is characterized by comprising the following steps:
s1, selecting clear and cloudless weather, and carrying out ground-off flight by adopting an unmanned aerial vehicle to carry the multispectral camera;
s2, simultaneously collecting spectrum data of the winter wheat canopy by adopting five spectrum channels of the multispectral camera to obtain uncalibrated blue light spectrum reflectivity R'blueUncorrected green spectral reflectance R'greenUncorrected red spectral reflectance R'redUncalibrated red-edge spectral reflectance R'rededgeAnd uncalibrated near infrared spectral reflectance R'nir；
The step S2 includes the steps of:
s21, periodically photographing the wheat field by adopting five spectral channels of the multispectral camera to obtain 5 XN image pictures, wherein N is the number of the image pictures of a single channel;
s22, splicing the respective N image maps of each spectral channel to obtain the wheat field reflectivity images of the five spectral channels with the central wavelengths of 475nm, 560nm, 668nm, 717nm and 840nm respectively;
s23, extracting uncalibrated blue spectral reflectance R 'from wheat field reflectance images of five spectral channels with center wavelengths of 475nm, 560nm, 668nm, 717nm and 840nm respectively'blueUncorrected green spectral reflectance R'greenUncorrected red spectral reflectance R'redUncalibrated red-edge spectral reflectance R'rededgeAnd uncalibrated near infrared spectral reflectance R'nir；
S3, performing photometry on the gray board by using a light intensity sensor to obtain a light intensity value, and correcting the uncalibrated blue light spectrum reflectivity R 'according to the light intensity value'blueUncorrected green spectral reflectance R'greenUncorrected red spectral reflectance R'redUncalibrated red-edge spectral reflectance R'rededgeAnd uncalibrated near infrared spectral reflectance R'nirObtaining the reflectivity R of the blue light spectrumblueGreen spectral reflectance RgreenRed spectral reflectance RredRed edge spectral reflectance RrededgeAnd near infrared spectral reflectance Rnir；
S4, according to the spectral vegetation coefficient equation set and according to the blue spectral reflectivity RblueGreen spectral reflectance RgreenRed spectral reflectance RredRed edge spectral reflectance RrededgeAnd near infrared spectral reflectance RnirCalculating to obtain a vegetation coefficient set;
the vegetation coefficient set in step S4 includes: chlorophyll absorption ratio index CARI, normalized blue-green difference index NGBDI, triangular vegetation index TVI, improved simple ratio vegetation index MSR, green band optimized soil adjusted vegetation index GOSAVI, green normalized vegetation index GNDVI and red band atmospheric impedance vegetation index VARIred;
the system of spectral vegetation coefficient equations in step S4 includes the following equations:
CARI＝(Rrededge-Rred)-0.2×(Rrededge+Rred) (1)
NGBDI＝(Rgreen-Rblue)/(Rgreen+Rblue) (2)
TVI＝0.5×[120×(Rnir-Rgreen)-200×(Rred-Rgreen)] (3)
GOSAVI＝1.16×[(Rnir-Rgreen)/(Rnir+Rgreen+0.16)] (5)
GNDVI＝(Rnir-Rgreen)/(Rnir+Rgreen) (6)
VARIred＝(Rrededge-1.7×Rred+0.7×Rblue)/(Rrededge+2.3×Rred-1.3×Rblue) (7)；
s5, calculating according to the growth season of the winter wheat according to the vegetation coefficient set by utilizing a stepwise regression chlorophyll model to obtain an estimated value of the winter wheat canopy chlorophyll content SPAD;
the stepwise regression chlorophyll model in step S5 is:
2. the winter wheat canopy chlorophyll estimation method of claim 1, wherein the time period of unmanned aerial vehicle flight in step S1 is: a time period between 10 and 12 am.
3. The method for estimating chlorophyll in canopy of winter wheat according to claim 1, wherein in step S1, the flying height of unmanned aerial vehicle flying off the ground is 30-80 m, and the flying speed is 1-6 m/S.
4. A system of the winter wheat canopy chlorophyll estimation method of claim 1, comprising an unmanned aerial vehicle, a multispectral camera, a gray plate and a light intensity sensor;
the multispectral camera is carried on the unmanned aerial vehicle and used for collecting the reflectivity image of winter wheat;
the light intensity sensor is fixedly connected with the multispectral camera and used for calibrating the reflectivity of the multispectral camera;
the gray board is used for performing reflectivity calibration of the multispectral camera in cooperation with the light intensity sensor;
the unmanned aerial vehicle is used for bearing the multispectral camera and flying off the ground.
5. The system for winter wheat canopy chlorophyll estimation method according to claim 4, wherein said drone is a six-axis rotor drone.
6. The system for winter wheat canopy chlorophyll estimation method according to claim 4, wherein said multispectral camera is provided with five spectral channels with central wavelengths of 475nm, 560nm, 668nm, 717nm and 840nm, respectively.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910869692.6A CN110567891B (en) | 2019-09-16 | 2019-09-16 | Winter wheat canopy chlorophyll estimation system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910869692.6A CN110567891B (en) | 2019-09-16 | 2019-09-16 | Winter wheat canopy chlorophyll estimation system and method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110567891A CN110567891A (en) | 2019-12-13 |
| CN110567891B true CN110567891B (en) | 2021-09-07 |
Family
ID=68780128
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910869692.6A Active CN110567891B (en) | 2019-09-16 | 2019-09-16 | Winter wheat canopy chlorophyll estimation system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110567891B (en) |
Families Citing this family (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110987183A (en) * | 2019-12-27 | 2020-04-10 | 广州极飞科技有限公司 | Multispectral imaging system and method |
| CN111175782A (en) * | 2019-12-31 | 2020-05-19 | 塔里木大学 | Satellite remote sensing monitoring method for chlorophyll content of cotton canopy |
| CN111175783A (en) * | 2019-12-31 | 2020-05-19 | 塔里木大学 | Satellite remote sensing monitoring method for cotton canopy chlorophyll b content |
| CN111398178B (en) * | 2020-04-07 | 2023-05-16 | 中国科学院空天信息创新研究院 | Leaf chlorophyll content inversion method and device, electronic equipment and storage medium |
| WO2021207977A1 (en) * | 2020-04-15 | 2021-10-21 | 深圳市大疆创新科技有限公司 | Movable platform operation method, movable platform and electronic device |
| CN112634213A (en) * | 2020-12-15 | 2021-04-09 | 安阳工学院 | System and method for predicting winter wheat canopy leaf area index by unmanned aerial vehicle |
| CN113514402B (en) * | 2021-04-12 | 2023-03-07 | 新疆农业大学 | System and method for predicting chlorophyll content of winter wheat |
| CN114373516A (en) * | 2021-11-30 | 2022-04-19 | 中国科学院空天信息创新研究院 | Chlorophyll content remote sensing inversion method, device, electronic equipment, medium and product |
| CN115950838B (en) * | 2022-09-20 | 2023-08-01 | 中国水利水电科学研究院 | Summer corn drought unmanned aerial vehicle rapid monitoring and distinguishing method based on chlorophyll content |
| CN117115687B (en) * | 2023-08-02 | 2024-04-09 | 江苏商贸职业学院 | Unmanned aerial vehicle accurate fertilization method and system based on artificial intelligence technology |
| CN117760984A (en) * | 2023-12-25 | 2024-03-26 | 安徽科技学院 | Winter wheat SPAD space-time change monitoring method |
| CN117746168B (en) * | 2024-02-21 | 2024-05-07 | 深圳块织类脑智能科技有限公司 | Intelligent pine forest nematode disease detection method based on unmanned aerial vehicle multispectral |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101074926A (en) * | 2007-06-12 | 2007-11-21 | 浙江大学 | Method and system for diagnosing plant-leaf or crown botrytis of visible and near-infrared spectral |
| CN101403689A (en) * | 2008-11-20 | 2009-04-08 | 北京航空航天大学 | Nondestructive detection method for physiological index of plant leaf |
| CN101762463A (en) * | 2009-12-16 | 2010-06-30 | 中国烟草总公司郑州烟草研究院 | Method for measuring chlorophyll content of fresh tobacco leaf of flue-cured tobacco based on canopy multi-spectra |
| CN101839979A (en) * | 2010-04-22 | 2010-09-22 | 中国农业大学 | Method and device for measuring index number of canopy vegetation of crops |
| CN102878957A (en) * | 2012-09-26 | 2013-01-16 | 安徽大学 | Leaf area index and chlorophyll content inversion method based on remote sensing image optimization PROSAIL model parameters |
| CN110082309A (en) * | 2019-05-20 | 2019-08-02 | 中国水利水电科学研究院 | Canopy of winter wheat SPAD value integrated spectral monitoring model method for building up |
| US10386296B1 (en) * | 2017-12-06 | 2019-08-20 | Arable Labs, Inc. | Systems and methods for determination and application of nitrogen fertilizer using crop canopy measurements |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103175789B (en) * | 2013-03-05 | 2016-07-06 | 中国科学院南海海洋研究所 | Water quality hyperspectral air remote sensing system based on many rotor wing unmanned aerial vehicles |
| CN103278460B (en) * | 2013-05-30 | 2015-07-29 | 华南农业大学 | A kind of mandarin tree red spider herbivore stress situation method for testing and analyzing |
| CN107024439A (en) * | 2017-03-23 | 2017-08-08 | 西北农林科技大学 | A kind of paddy rice different growing chlorophyll content EO-1 hyperion estimating and measuring method |
| CN107807125B (en) * | 2017-10-12 | 2020-08-11 | 中国农业大学 | Plant information calculation system and method based on unmanned aerial vehicle-mounted multispectral sensor |
| CN109030378A (en) * | 2018-06-04 | 2018-12-18 | 沈阳农业大学 | Japonica rice canopy chlorophyll content inverse model approach based on PSO-ELM |
| CN109060676A (en) * | 2018-07-05 | 2018-12-21 | 中国水利水电科学研究院 | Based on the determination method of the Summer Corn Canopy SPAD value appraising model of EO-1 hyperion |
| CN108760660A (en) * | 2018-08-17 | 2018-11-06 | 山东农业大学 | A kind of period of seedling establishment leaves of winter wheat chlorophyll contents evaluation method |
| CN109187441B (en) * | 2018-08-27 | 2022-06-10 | 中国水利水电科学研究院 | Method for constructing summer corn nitrogen content monitoring model based on canopy spectral information |
| CN109459392B (en) * | 2018-11-06 | 2019-06-14 | 南京农业大学 | A kind of rice the upperground part biomass estimating and measuring method based on unmanned plane multispectral image |
| CN110222475B (en) * | 2019-07-03 | 2021-09-07 | 中国水利水电科学研究院 | Method for inverting moisture content of winter wheat plants based on multispectral remote sensing of unmanned aerial vehicle |
-
2019
- 2019-09-16 CN CN201910869692.6A patent/CN110567891B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101074926A (en) * | 2007-06-12 | 2007-11-21 | 浙江大学 | Method and system for diagnosing plant-leaf or crown botrytis of visible and near-infrared spectral |
| CN101403689A (en) * | 2008-11-20 | 2009-04-08 | 北京航空航天大学 | Nondestructive detection method for physiological index of plant leaf |
| CN101762463A (en) * | 2009-12-16 | 2010-06-30 | 中国烟草总公司郑州烟草研究院 | Method for measuring chlorophyll content of fresh tobacco leaf of flue-cured tobacco based on canopy multi-spectra |
| CN101839979A (en) * | 2010-04-22 | 2010-09-22 | 中国农业大学 | Method and device for measuring index number of canopy vegetation of crops |
| CN102878957A (en) * | 2012-09-26 | 2013-01-16 | 安徽大学 | Leaf area index and chlorophyll content inversion method based on remote sensing image optimization PROSAIL model parameters |
| US10386296B1 (en) * | 2017-12-06 | 2019-08-20 | Arable Labs, Inc. | Systems and methods for determination and application of nitrogen fertilizer using crop canopy measurements |
| CN110082309A (en) * | 2019-05-20 | 2019-08-02 | 中国水利水电科学研究院 | Canopy of winter wheat SPAD value integrated spectral monitoring model method for building up |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110567891A (en) | 2019-12-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110567891B (en) | Winter wheat canopy chlorophyll estimation system and method | |
| CN107807125B (en) | Plant information calculation system and method based on unmanned aerial vehicle-mounted multispectral sensor | |
| Qiu et al. | Estimation of nitrogen nutrition index in rice from UAV RGB images coupled with machine learning algorithms | |
| CN106372592B (en) | A kind of winter wheat planting area calculation method based on winter wheat area index | |
| CN108169138B (en) | Rice lodging monitoring method utilizing thermal infrared image | |
| US10585210B2 (en) | Apparatus for radiometric correction and orthorectification of aerial imagery | |
| Dobrowski et al. | Grapevine dormant pruning weight prediction using remotely sensed data | |
| Zarco-Tejada et al. | Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera | |
| CN112051222A (en) | River and lake water quality monitoring method based on high-resolution satellite image | |
| Moriya et al. | Mapping mosaic virus in sugarcane based on hyperspectral images | |
| CN111241912A (en) | Multi-vegetation index rice yield estimation method based on machine learning algorithm | |
| CN111028096A (en) | System and method for integrating space, air and ground data | |
| Saberioon et al. | A review of optical methods for assessing nitrogen contents during rice growth | |
| CN108760660A (en) | A kind of period of seedling establishment leaves of winter wheat chlorophyll contents evaluation method | |
| CN105758806B (en) | Remote sensing monitoring method for mulching film farmland based on spectral characteristics | |
| Warren et al. | Agricultural applications of high-resolution digital multispectral imagery | |
| CN116308866B (en) | Rice ear biomass estimation method and system based on canopy reflection spectrum | |
| Saberioon et al. | Novel approach for estimating nitrogen content in paddy fields using low altitude remote sensing system | |
| Jeong et al. | Application of an unmanned aerial system for monitoring paddy productivity using the GRAMI-rice model | |
| Yuhao et al. | Rice Chlorophyll Content Monitoring using Vegetation Indices from Multispectral Aerial Imagery. | |
| CN114140695B (en) | Prediction method and system for tea tree nitrogen diagnosis and quality index determination based on unmanned aerial vehicle multispectral remote sensing | |
| CN112345467B (en) | Model for estimating physiological parameters of rice by using remote sensing technology and application thereof | |
| CN113063740A (en) | Wheat canopy nitrogen content monitoring method based on multi-source remote sensing data | |
| CN112504972B (en) | Method for rapidly monitoring nitrogen content of tobacco | |
| CN114778476A (en) | Alfalfa cotton field soil water content monitoring model based on unmanned aerial vehicle remote sensing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |