CN117233123A - Large-scale remote sensing monitoring method and device for bacterial leaf blight of rice based on sentinel No. 2 - Google Patents
Large-scale remote sensing monitoring method and device for bacterial leaf blight of rice based on sentinel No. 2 Download PDFInfo
- Publication number
- CN117233123A CN117233123A CN202311178155.XA CN202311178155A CN117233123A CN 117233123 A CN117233123 A CN 117233123A CN 202311178155 A CN202311178155 A CN 202311178155A CN 117233123 A CN117233123 A CN 117233123A
- Authority
- CN
- China
- Prior art keywords
- rice
- leaf blight
- bacterial leaf
- sentinel
- bacterial
- 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.)
- Pending
Links
- 235000007164 Oryza sativa Nutrition 0.000 title claims abstract description 122
- 235000009566 rice Nutrition 0.000 title claims abstract description 122
- 230000001580 bacterial effect Effects 0.000 title claims abstract description 98
- 238000012544 monitoring process Methods 0.000 title claims abstract description 31
- 238000000034 method Methods 0.000 title claims abstract description 30
- 240000007594 Oryza sativa Species 0.000 title 1
- 241000209094 Oryza Species 0.000 claims abstract description 121
- 238000000605 extraction Methods 0.000 claims abstract description 39
- 238000001228 spectrum Methods 0.000 claims abstract description 31
- 238000010586 diagram Methods 0.000 claims abstract description 16
- 238000003066 decision tree Methods 0.000 claims abstract description 11
- 238000007781 pre-processing Methods 0.000 claims abstract description 11
- 238000011160 research Methods 0.000 claims abstract description 10
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 7
- 230000003595 spectral effect Effects 0.000 claims description 5
- 238000003860 storage Methods 0.000 claims description 3
- 241001272684 Xanthomonas campestris pv. oryzae Species 0.000 claims description 2
- 238000012806 monitoring device Methods 0.000 claims description 2
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 238000009826 distribution Methods 0.000 abstract description 2
- 238000012545 processing Methods 0.000 abstract description 2
- 201000010099 disease Diseases 0.000 description 8
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 8
- 238000012952 Resampling Methods 0.000 description 3
- 235000013339 cereals Nutrition 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 238000011835 investigation Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 241001530056 Athelia rolfsii Species 0.000 description 1
- 208000035143 Bacterial infection Diseases 0.000 description 1
- 235000008331 Pinus X rigitaeda Nutrition 0.000 description 1
- 235000011613 Pinus brutia Nutrition 0.000 description 1
- 241000018646 Pinus brutia Species 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000003902 lesion Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000002310 reflectometry Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Landscapes
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The application relates to a large-scale remote sensing monitoring method and device for bacterial leaf blight of a sentinel No. 2 rice, comprising the following steps: acquiring and preprocessing a sentinel No. 2 multispectral image; selecting ground object sample points of a research area, and calculating normalized difference vegetation indexes NDVI in the area; determining an optimal band of spectrum difference to construct a rice bacterial leaf blight index; calculating bacterial leaf blight indexes, determining an optimal threshold range through a sample box diagram, and constructing a decision tree to extract the area of rice damaged by bacterial leaf blight; and checking the combination of the extraction result and the ground object sample point. The beneficial effects of the application are as follows: the method has the advantages that the number of the required samples is small, the time for processing hyperspectral data is reduced, the difference maximization index is constructed, the rice bacterial leaf blight area can be rapidly and accurately extracted, the spatial distribution range of the rice bacterial leaf blight is accurately reflected, and compared with a method based on classification ideas, the method has practical application significance.
Description
Technical Field
The application relates to the technical field of rice bacterial leaf blight monitoring, in particular to a large-scale remote sensing monitoring method and device for rice bacterial leaf blight based on sentinel No. 2.
Background
The rice is an important grain crop in China, and the stable yield of the rice is important to guaranteeing the national grain safety. However, the yield of rice due to disease is reduced by up to 30% of the total yield worldwide. Bacterial leaf blight is one of the most serious bacterial diseases in rice production in China, and is also called three major diseases of rice together with rice blast and banded sclerotial blight.
The traditional bacterial leaf blight disease monitoring method mainly uses manual investigation, and cannot realize large-scale accurate disease monitoring. The remote sensing technology has been applied to crop disease monitoring research due to the advantages of large monitoring range, strong periodicity and the like. However, the existing remote sensing monitoring of the bacterial leaf blight in a large range still has the following two outstanding problems: (a) The early remote sensing monitoring of the bacterial leaf blight of the large-scale rice is difficult. (b) The inversion model of the bacterial leaf blight hazard degree of large-area rice is not high in precision.
Disclosure of Invention
The application aims at overcoming the defects of the prior art, and provides a large-scale remote sensing monitoring method and device for bacterial leaf blight of rice based on a sentinel No. 2.
In a first aspect, a large-scale remote sensing monitoring method for bacterial leaf blight of rice based on sentinel No. 2 is provided, which comprises the following steps:
s1, acquiring a sentinel No. 2 multispectral image, and preprocessing the sentinel No. 2 multispectral image;
s2, selecting a ground object sample point of a research area, calculating a normalized difference vegetation index NDVI in the area, and setting a threshold value to remove non-vegetation ground objects, wherein the non-vegetation ground objects comprise roads, water bodies and buildings;
s3, calculating the average value of spectrum curves of healthy rice, rice with bacterial blight and woodland, analyzing the difference of spectrum curves of the rice with bacterial blight and the healthy rice and woodland, and determining the optimal wave band of the spectrum difference to construct a rice bacterial blight index;
s4, calculating bacterial leaf blight indexes, determining an optimal threshold range through a sample box diagram, and constructing a decision tree to extract the area of rice with bacterial leaf blight hazard;
s5, checking the combination of the extraction result of the S4 and the ground object sample point, and taking the extraction result passing the checking as a final rice extraction result of bacterial leaf blight hazard.
Preferably, in S1, the preprocessing includes: image mosaicing and spectral index calculation.
Preferably, in S2, the calculation formula of NDVI is:
wherein B is 8 Represents a near infrared band value of 835.1nm in center wavelength, B 4 The red band value with a center wavelength of 664.5nm is shown.
Preferably, in S3, the rice bacterial leaf blight extraction index is expressed by the following formula:
wherein BIB is rice bacterial leaf blight index, B 8 Represents a near infrared band value of 835.1nm in center wavelength, B 12 Representing the value of the short wave infrared band with the central wavelength of 2202.4nm, B 4 The red band value with a center wavelength of 664.5nm is shown.
Preferably, in S4, a sample box diagram is constructed based on ground object sample points, an optimal threshold range is determined, the bacterial leaf blight of rice is extracted through a decision tree, and the following formula is satisfied:
BIB<threshold
wherein BIB is the extraction index of bacterial leaf blight of rice, and threshold is the threshold of BIB.
In a second aspect, a device for monitoring bacterial leaf blight of rice based on sentinel No. 2 multispectral remote sensing is provided, and the method for monitoring bacterial leaf blight of rice based on sentinel No. 2 multispectral remote sensing in the first aspect is executed, and the method comprises the following steps:
the acquisition module is used for acquiring a sentinel No. 2 multispectral image and preprocessing the sentinel No. 2 multispectral image;
the computing module is used for selecting ground object sample points of a research area, computing a normalized difference vegetation index NDVI in the area, and setting a threshold value to remove non-vegetation ground objects, wherein the non-vegetation ground objects comprise roads, water bodies and buildings;
the determining module is used for calculating the average value of spectrum curves of healthy rice, white leaf blight harmful rice and woodland, analyzing the difference of spectrum curves of white leaf blight harmful rice, healthy rice and woodland, and determining the optimal wave band of the spectrum difference to construct a rice white leaf blight index;
the extraction module is used for calculating bacterial leaf blight indexes, determining an optimal threshold range through a sample box diagram, and constructing a decision tree to extract the area of rice damaged by bacterial leaf blight;
and the checking module is used for checking the combination of the extraction result of the extraction module and the ground object sample point, and taking the checked extraction result as a final rice extraction result of bacterial leaf blight hazard.
In a third aspect, a computer storage medium having a computer program stored therein is provided; when the computer program runs on a computer, the computer is enabled to execute the method for monitoring the bacterial leaf blight of the rice based on the sentinel No. 2 multispectral remote sensing according to any one of the first aspect.
In a fourth aspect, a computer program product is provided, characterized in that the computer program product, when run on a computer, causes the computer to perform the method for monitoring bacterial blight of rice based on sentinel No. 2 multispectral remote sensing according to any one of the first aspects.
The beneficial effects of the application are as follows: the application fully excavates the spectral characteristics of the bacterial leaf blight of the rice based on the sentinel No. 2 multispectral remote sensing, has fewer required samples, reduces the time for processing hyperspectral data, constructs the difference maximization index, can rapidly and accurately extract the bacterial leaf blight region of the rice, accurately reflects the spatial distribution range of the bacterial leaf blight of the rice, and has practical application significance compared with a method based on classification ideas.
Drawings
FIG. 1 is a flow chart of a method for monitoring bacterial leaf blight of rice based on sentinel No. 2 multispectral remote sensing;
FIG. 2 is a box diagram of NDVI for feature sample point statistics;
FIG. 3 is a graph showing the mean value of the statistical spectrum curve of the ground object sample points;
FIG. 4 is a box line schematic diagram of bacterial blight indexes counted by ground object sample points;
FIG. 5 is a schematic diagram showing the extraction result of bacterial leaf blight of rice.
Detailed Description
The application is further described below with reference to examples. The following examples are presented only to aid in the understanding of the application. It should be noted that it will be apparent to those skilled in the art that modifications can be made to the present application without departing from the principles of the application, and such modifications and adaptations are intended to be within the scope of the application as defined in the following claims.
Example 1:
the existing disease remote sensing monitoring method based on the remote sensing of the disease mostly takes the field as the object, and cannot realize the monitoring of the disease in a large range. Therefore, the application aims at meeting the early and accurate monitoring of the bacterial leaf blight of the large-area rice, and utilizes the data of the sentinel No. 2 multispectral satellite to construct a spectrum index so as to realize the large-range and high-precision extraction of the bacterial leaf blight of the rice.
The embodiment of the application provides a large-scale remote sensing monitoring method for bacterial leaf blight of rice based on a sentinel No. 2, which is shown in fig. 1 and comprises the following steps:
s1, acquiring a sentinel No. 2 multispectral image, and preprocessing the sentinel No. 2 multispectral image.
Specifically, sentinel number 2L 2A data is obtained from google earth cloud platform (Google earth engine, GEE). Firstly, uploading Ningbo market vector data into an asset (Aesset) of a GEE account, and then setting the cloud cover to be not more than 35% and screening date to obtain an image available in Ningbo market. Because the Javascript of the GEE website cannot directly download the image to the local, the image is first saved in the Gu Geyun disc, and then downloaded from the cloud disc to the local. The L2A product has been subjected to radiometric calibration, atmospheric correction and geometric correction, so that preprocessing operations such as image stitching and clipping, spectral index calculation and the like are directly performed locally.
S2, selecting ground object sample points of a research area, calculating a normalized difference vegetation index NDVI in the area, and setting a threshold (for example, NDVI is less than 0.2) to remove non-vegetation ground objects, wherein the non-vegetation ground objects comprise roads, water bodies and buildings.
In S2, the ground object sample points in the research area include sample points of severe bacterial leaf blight damage rice, mild bacterial leaf blight damage rice, healthy rice, water, road and the like, which may also be referred to as sample data, NDVI of each type of sample point is calculated, and a threshold is set to remove non-vegetation ground objects (as shown in fig. 2).
The calculation formula of NDVI is:
wherein B is 8 Represents a near infrared band value of 835.1nm in center wavelength, B 4 The red band value with a center wavelength of 664.5nm is shown.
S3, calculating the average value of spectrum curves of healthy rice, rice with bacterial leaf blight and woodland (shown in figure 3), analyzing the difference of the spectrum curves of the rice with bacterial leaf blight, healthy rice and woodland, and determining the optimal wave band of the spectrum difference to construct the rice bacterial leaf blight index.
S3 comprises the following steps:
s301, analyzing the spectrum curves can find that the rice with bacterial blight is greatly different from healthy rice and woodland in red wave bands (B4), near infrared (B8) and short wave infrared wave bands (B12), but the change trend of the spectrum curves of the three is similar, so that the wave band combination is required to be determined in an experimental mode to extract the rice with bacterial blight;
s302, finding B based on a spectrum graph and experimental verification mode 8 、B 12 And B 4 The spectral values of the wave bands in the bacterial leaf blight which endangers rice, woodland and healthy rice are greatly different, and in B 8 The healthy rice with wave band is higher than the rice with bacterial leaf blight, and the healthy rice with wave band is in B 12 And B 4 Rice with bacterial leaf blight at the position has higher reflectivity than healthy riceIndicating that healthy rice and bacterial leaf blight of rice are in B 4 、B 12 And B 8 The method is sensitive, so that the three wave bands are selected to construct the rice bacterial leaf blight extraction index:
wherein BIB is rice bacterial leaf blight index, B 8 Represents a near infrared band value of 835.1nm in center wavelength, B 12 Representing the value of the short wave infrared band with the central wavelength of 2202.4nm, B 4 The red band value with a center wavelength of 664.5nm is shown.
S4, calculating bacterial leaf blight indexes, determining an optimal threshold range (shown in fig. 4) through a sample box diagram, and constructing a decision tree to extract the area of the bacterial leaf blight damage rice.
Specifically, S4 includes:
s401, calculating a BIB index through a Band Math tool, superposing the interested areas of the three ground features in S2 on an index result, calculating the value range of the selected sample pixels, and constructing a sample box diagram to determine an optimal threshold range (shown in FIG. 4);
s402, constructing a decision tree based on an index result and a sample box diagram to extract bacterial leaf blight of rice, and determining that the threshold satisfies the following formula to obtain a pine lesion area:
BIB<threshold
wherein BIB is the extraction index of bacterial leaf blight of rice, and threshold is the threshold of BIB. The threshold is set to 3, for example.
S5, checking the combination of the extraction result of the S4 and the ground object sample point, and taking the extraction result after checking as a final rice bacterial leaf blight result. For example, fig. 5 is a graph showing the extraction result of bacterial leaf blight of rice in this example.
S5, checking the combination of the extraction result of the S4 and the ground object sample point, and taking the extraction result passing the checking as a final rice extraction result of bacterial leaf blight hazard.
Example 2:
on the basis of the embodiment 1, the embodiment 2 of the application provides a more specific large-scale remote sensing monitoring method for the bacterial leaf blight of the rice based on the sentinel No. 2, which comprises the following steps:
s1, acquiring a sentinel No. 2 multispectral image, and preprocessing the sentinel No. 2 multispectral image.
Specifically, the method comprises the following steps:
s101, image screening and downloading are carried out in the GEE platform according to the uploaded Ningbo city vector data, date and cloud cover. Spatial resolution of the sentinel No. 2 image among wave bands is divided into differences, wherein the wave bands B1 and B9 are 60m; the resolution of the wave bands B2, B3, B4 and B8 is 10m; the spatial resolution of B5, B6, B7, B8a, B11, B12 is 20m.
S102, image stitching: splicing and embedding the images by using ENVI software to finally obtain spliced images;
s103, spectrum resampling and index calculation: and (3) performing spectrum resampling by using ENVI5.3 software, resampling a wave band with resolution of not 10m to obtain a preprocessed sentinel No. 2 multispectral image, and calculating a spectrum index by using a Bandmath function of ENVI.
S2, selecting ground object sample points of a research area, calculating a normalized difference vegetation index NDVI in the area, and setting a threshold value to remove non-vegetation ground objects, wherein the non-vegetation ground objects comprise roads, water bodies and buildings.
Specifically, S2 includes:
s201, selecting interesting areas with different ground object types on a sentinel No. 2 image by combining field investigation data and visual interpretation; it should be noted that, the bacterial leaf blight of rice generally presents yellow brown on true color images, and healthy rice generally presents green;
s202, selecting 501, 506, 500, 536, 661, 700 and 610 samples of severe rice bacterial leaf blight, mild bacterial leaf blight, healthy rice, roads, water bodies, buildings and woodlands through selecting an interested region, and calculating spectrum curve average values of different ground object types through selected sample pixels so as to extract the bacterial leaf blight rice through indexes in the later period.
S203, calculating NDVI values of various sample points, and setting NDVI <0.3 through an NDVI box diagram to remove non-vegetation ground objects such as roads, water bodies, buildings and the like.
S3, calculating the average value of spectrum curves of healthy rice, rice with bacterial blight and woodland, analyzing the difference of spectrum curves of the rice with bacterial blight and the healthy rice and woodland, and determining the optimal wave band of the spectrum difference to construct the rice bacterial blight index.
S4, calculating bacterial leaf blight indexes, determining an optimal threshold range through a sample box diagram, and constructing a decision tree to extract the rice area of bacterial leaf blight hazard.
S5, checking the combination of the extraction result of the S4 and the ground object sample point, and taking the extraction result passing the checking as a final rice extraction result of bacterial leaf blight hazard.
In this embodiment, the same or similar parts as those in embodiment 1 may be referred to each other, and will not be described in detail in the present disclosure.
Example 3:
on the basis of the embodiments 1 and 2, the embodiment 3 of the application provides a rice bacterial leaf blight monitoring device based on sentinel No. 2 multispectral remote sensing, which comprises:
the acquisition module is used for acquiring a sentinel No. 2 multispectral image and preprocessing the sentinel No. 2 multispectral image;
the computing module is used for selecting ground object sample points of a research area, computing a normalized difference vegetation index NDVI in the area, and setting a threshold value to remove non-vegetation ground objects, wherein the non-vegetation ground objects comprise roads, water bodies and buildings;
the determining module is used for calculating the average value of spectrum curves of healthy rice, white leaf blight harmful rice and woodland, analyzing the difference of spectrum curves of white leaf blight harmful rice, healthy rice and woodland, and determining the optimal wave band of the spectrum difference to construct a rice white leaf blight index;
the extraction module is used for calculating bacterial leaf blight indexes, determining an optimal threshold range through a sample box diagram, and constructing a decision tree to extract the area of rice damaged by bacterial leaf blight;
and the checking module is used for checking the combination of the extraction result of the extraction module and the ground object sample point, and taking the checked extraction result as a final rice extraction result of bacterial leaf blight hazard.
Specifically, the apparatus provided in this embodiment is an apparatus corresponding to the method provided in embodiments 1 and 2, so that the parts in this embodiment that are the same as or similar to those in embodiments 1 and 2 may be referred to each other, and will not be described in detail in this disclosure.
Claims (8)
1. A large-scale remote sensing monitoring method for bacterial leaf blight of rice based on a sentinel No. 2 is characterized by comprising the following steps:
s1, acquiring a sentinel No. 2 multispectral image, and preprocessing the sentinel No. 2 multispectral image;
s2, selecting ground object sample points of a research area, calculating a normalized difference vegetation index NDVI in the area, and setting a threshold value to remove non-vegetation ground objects, wherein the non-vegetation ground objects comprise roads, water bodies and buildings;
s3, calculating the average value of spectrum curves of healthy rice, rice with bacterial blight and woodland, analyzing the difference of spectrum curves of the rice with bacterial blight and the healthy rice and woodland, and determining the optimal wave band of the spectrum difference to construct a rice bacterial blight index;
s4, calculating bacterial leaf blight indexes, determining an optimal threshold range through a sample box diagram, and constructing a decision tree to extract the area of rice with bacterial leaf blight hazard;
s5, checking the combination of the extraction result of the S4 and the ground object sample point, and taking the extraction result passing the checking as a final rice extraction result of bacterial leaf blight hazard.
2. The method for remotely sensing and monitoring bacterial leaf blight of rice in a large range based on sentinel No. 2 of claim 1, wherein in S1, the preprocessing comprises: image mosaicing and spectral index calculation.
3. The large-scale remote sensing monitoring method for bacterial leaf blight of rice based on sentinel No. 2 of claim 2, wherein in S2, the calculation formula of NDVI is:
wherein B is 8 Represents a near infrared band value of 835.1nm in center wavelength, B 4 The red band value with a center wavelength of 664.5nm is shown.
4. The large-scale remote sensing monitoring method for bacterial leaf blight of rice based on sentinel No. 2 of claim 3, wherein in S3, the bacterial leaf blight extraction index of rice is expressed by the following formula:
wherein BIB is rice bacterial leaf blight index, B 8 Represents a near infrared band value of 835.1nm in center wavelength, B 12 Representing the value of the short wave infrared band with the central wavelength of 2202.4nm, B 4 The red band value with a center wavelength of 664.5nm is shown.
5. The large-scale remote sensing monitoring method for bacterial leaf blight of paddy rice based on sentinel No. 2 of claim 4, wherein in S4, a sample box diagram is constructed based on ground feature sample points, an optimal threshold range is determined, the bacterial leaf blight of paddy rice is extracted through a decision tree, and the following formula is satisfied:
BIB<threshold
wherein BIB is the extraction index of bacterial leaf blight of rice, and threshold is the threshold of BIB.
6. The utility model provides a rice bacterial leaf blight monitoring devices based on sentinel No. 2 multispectral remote sensing which is characterized in that is used for carrying out the rice bacterial leaf blight monitoring method based on sentinel No. 2 multispectral remote sensing of any one of claims 1 to 5, includes:
the acquisition module is used for acquiring a sentinel No. 2 multispectral image and preprocessing the sentinel No. 2 multispectral image;
the computing module is used for selecting ground object sample points of a research area, computing a normalized difference vegetation index NDVI in the area, and setting a threshold value to remove non-vegetation ground objects, wherein the non-vegetation ground objects comprise roads, water bodies and buildings;
the determining module is used for calculating the average value of spectrum curves of healthy rice, white leaf blight harmful rice and woodland, analyzing the difference of spectrum curves of white leaf blight harmful rice, healthy rice and woodland, and determining the optimal wave band of the spectrum difference to construct a rice white leaf blight index;
the extraction module is used for calculating bacterial leaf blight indexes, determining an optimal threshold range through a sample box diagram, and constructing a decision tree to extract the area of rice damaged by bacterial leaf blight;
and the checking module is used for checking the combination of the extraction result of the extraction module and the ground object sample point, and taking the checked extraction result as a final rice extraction result of bacterial leaf blight hazard.
7. A computer storage medium, wherein a computer program is stored in the computer storage medium; when the computer program runs on a computer, the computer is caused to execute the method for monitoring the bacterial blight of the rice based on the sentinel No. 2 multispectral remote sensing according to any one of claims 1 to 5.
8. A computer program product, characterized in that the computer program product, when run on a computer, causes the computer to perform the method for monitoring bacterial blight of rice based on the multi-spectral remote sensing of sentinel No. 2 according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311178155.XA CN117233123A (en) | 2023-09-13 | 2023-09-13 | Large-scale remote sensing monitoring method and device for bacterial leaf blight of rice based on sentinel No. 2 |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311178155.XA CN117233123A (en) | 2023-09-13 | 2023-09-13 | Large-scale remote sensing monitoring method and device for bacterial leaf blight of rice based on sentinel No. 2 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117233123A true CN117233123A (en) | 2023-12-15 |
Family
ID=89090512
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311178155.XA Pending CN117233123A (en) | 2023-09-13 | 2023-09-13 | Large-scale remote sensing monitoring method and device for bacterial leaf blight of rice based on sentinel No. 2 |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117233123A (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112766036A (en) * | 2020-12-08 | 2021-05-07 | 中科禾信遥感科技（苏州）有限公司 | Remote sensing extraction method and device for lodging corn |
| CN113033670A (en) * | 2021-03-29 | 2021-06-25 | 华南农业大学 | Method for extracting rice planting area based on Sentinel-2A/B data |
| CN113283281A (en) * | 2021-02-26 | 2021-08-20 | 中科禾信遥感科技（苏州）有限公司 | Zizania latifolia planting area extraction method based on multi-temporal remote sensing image |
| CN114519821A (en) * | 2022-02-21 | 2022-05-20 | 河南大学 | Construction method of remote sensing identification index of green and healthy vegetation |
| CN115115955A (en) * | 2022-07-08 | 2022-09-27 | 宁波大学 | Pine wood nematode disease tree monitoring method and device based on unmanned aerial vehicle hyperspectral remote sensing |
| WO2022252799A1 (en) * | 2021-06-04 | 2022-12-08 | 成都数之联科技股份有限公司 | Model training method, woodland change detection method, system, and apparatus, and medium |
| CN115690580A (en) * | 2022-10-27 | 2023-02-03 | 吉林大学 | Corn lodging remote sensing monitoring method and system, electronic device and medium |
-
2023
- 2023-09-13 CN CN202311178155.XA patent/CN117233123A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112766036A (en) * | 2020-12-08 | 2021-05-07 | 中科禾信遥感科技（苏州）有限公司 | Remote sensing extraction method and device for lodging corn |
| CN113283281A (en) * | 2021-02-26 | 2021-08-20 | 中科禾信遥感科技（苏州）有限公司 | Zizania latifolia planting area extraction method based on multi-temporal remote sensing image |
| CN113033670A (en) * | 2021-03-29 | 2021-06-25 | 华南农业大学 | Method for extracting rice planting area based on Sentinel-2A/B data |
| WO2022252799A1 (en) * | 2021-06-04 | 2022-12-08 | 成都数之联科技股份有限公司 | Model training method, woodland change detection method, system, and apparatus, and medium |
| CN114519821A (en) * | 2022-02-21 | 2022-05-20 | 河南大学 | Construction method of remote sensing identification index of green and healthy vegetation |
| CN115115955A (en) * | 2022-07-08 | 2022-09-27 | 宁波大学 | Pine wood nematode disease tree monitoring method and device based on unmanned aerial vehicle hyperspectral remote sensing |
| CN115690580A (en) * | 2022-10-27 | 2023-02-03 | 吉林大学 | Corn lodging remote sensing monitoring method and system, electronic device and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111461052B (en) | Wheat lodging area identification method for multiple growth periods based on transfer learning | |
| CN111461053B (en) | Multi-growth-period wheat lodging area identification system based on transfer learning | |
| CN111795936B (en) | Multispectral remote sensing image atmospheric correction system and method based on lookup table and storage medium | |
| Peña-Barragán et al. | Object-based crop identification using multiple vegetation indices, textural features and crop phenology | |
| CN112183209B (en) | Regional crop classification method and system based on multidimensional feature fusion | |
| Harris et al. | Hyperspectral remote sensing of peatland floristic gradients | |
| CN110796001B (en) | Satellite image film-covering farmland identification and extraction method and system | |
| CN107067334B (en) | Farmland fire and straw burning monitoring method and system based on multi-source remote sensing data | |
| CN111582194B (en) | Multi-temporal high-resolution remote sensing image building extraction method based on multi-feature LSTM network | |
| Gutiérrez et al. | On‐the‐go hyperspectral imaging for the in‐field estimation of grape berry soluble solids and anthocyanin concentration | |
| Chen et al. | The application of the tasseled cap transformation and feature knowledge for the extraction of coastline information from remote sensing images | |
| CN112712038B (en) | Method and system for monitoring wheat lodging condition based on multispectral satellite image | |
| CN110991430B (en) | Ground feature identification and coverage rate calculation method and system based on remote sensing image | |
| Zhang et al. | Extraction of tree crowns damaged by Dendrolimus tabulaeformis Tsai et Liu via spectral-spatial classification using UAV-based hyperspectral images | |
| Wang et al. | Unsupervised discrimination between lodged and non-lodged winter wheat: A case study using a low-cost unmanned aerial vehicle | |
| CN115372282B (en) | Farmland soil water content monitoring method based on hyperspectral image of unmanned aerial vehicle | |
| CN109959621B (en) | Water environment analysis method based on medium-high resolution time series remote sensing data | |
| CN114778483A (en) | Method for correcting terrain shadow of remote sensing image near-infrared wave band for monitoring mountainous region | |
| CN111474117A (en) | Method and device for monitoring crop diseases | |
| CN114199800A (en) | Method, system, equipment and medium for identifying rice sheath blight | |
| Chen et al. | Effect of crop spectra purification on plant nitrogen concentration estimations performed using high-spatial-resolution images obtained with unmanned aerial vehicles | |
| CN117557897A (en) | Lodging monitoring method and device for target crops, electronic equipment and storage medium | |
| Liu et al. | Comprehensive growth index monitoring of desert steppe grassland vegetation based on UAV hyperspectral | |
| CN117233123A (en) | Large-scale remote sensing monitoring method and device for bacterial leaf blight of rice based on sentinel No. 2 | |
| Danoedoro et al. | Combining pan-sharpening and forest cover density transformation methods for vegetation mapping using Landsat-8 Satellite Imagery |
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 |