CN112364691B - Tobacco pest identification method - Google Patents
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- 235000002637 Nicotiana tabacum Nutrition 0.000 title claims abstract description 67
- 241000607479 Yersinia pestis Species 0.000 title claims abstract description 53
- 238000000034 method Methods 0.000 title claims abstract description 26
- 244000061176 Nicotiana tabacum Species 0.000 title abstract description 9
- 241000208125 Nicotiana Species 0.000 claims abstract description 58
- 201000010099 disease Diseases 0.000 claims abstract description 39
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims abstract description 39
- 241000238631 Hexapoda Species 0.000 claims abstract description 30
- 241000196324 Embryophyta Species 0.000 claims abstract description 14
- 238000013528 artificial neural network Methods 0.000 claims abstract description 12
- 238000005070 sampling Methods 0.000 claims abstract description 6
- 230000005855 radiation Effects 0.000 claims abstract description 5
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- 238000003786 synthesis reaction Methods 0.000 claims abstract description 4
- 230000003595 spectral effect Effects 0.000 claims description 4
- 238000011160 research Methods 0.000 claims description 3
- 238000004088 simulation Methods 0.000 claims description 2
- 239000002689 soil Substances 0.000 claims description 2
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- 238000012549 training Methods 0.000 abstract description 3
- 238000004364 calculation method Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 101100322243 Caenorhabditis elegans deg-3 gene Proteins 0.000 description 1
- 244000025254 Cannabis sativa Species 0.000 description 1
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- 230000008020 evaporation Effects 0.000 description 1
- 238000001704 evaporation Methods 0.000 description 1
- 238000003306 harvesting Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000001556 precipitation Methods 0.000 description 1
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Abstract
The invention discloses a tobacco plant disease and insect pest identification method, which comprises the steps of obtaining surface information data of a tobacco planting area, wherein the surface information data is obtained by a multispectral sensor carried by an unmanned aerial vehicle; obtaining Landsat data of a tobacco planting area, carrying out radiation and geometric calibration on the obtained data, and calculating biological physical parameters; acquiring meteorological data of a tobacco planting area; acquiring plant disease and insect pest data of a tobacco planting area, wherein the plant disease and insect pest data comprises spectrum and growth environment information of tobacco in a sampling area with and without insect pest; carrying out multiband fusion on the obtained earth surface information data, landsat data and meteorological data of the tobacco planting area to obtain a band synthesis data set; and training the neural network by taking the synthesized wave band synthesized data set as an input variable and the plant disease and insect pest data as an output layer by adopting the BP neural network to construct the tobacco plant disease and insect pest monitoring characteristic parameters. The method combines the climate condition and the plant diseases and insect conditions of the area where the tobacco belongs to, and can realize the monitoring of the plant diseases and insect pests of the tobacco in a large range at the same time.
Description
Technical Field
The invention relates to the technical field of identification, in particular to a tobacco plant diseases and insect pests identification method.
Background
The identification of tobacco diseases and insect pests is an important component for tobacco growth monitoring, and the grasp of the disease and insect pest states and the optimal harvesting period of tobacco has important significance for tobacco estimated production. At present, the plant diseases and insect pests of tobacco are monitored mainly by means of manual ground investigation, the plant diseases and insect pests information of tobacco is difficult to collect in a large area, and quantitative tobacco growth assessment means are lacked.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a tobacco plant diseases and insect pests identification method so as to realize large-scale tobacco plant diseases and insect pests monitoring and provide technical support for realizing real-time monitoring of tobacco plant diseases and insect pests.
In order to achieve the above purpose, the technical scheme of the invention is as follows:
a method of identifying tobacco pests, comprising:
acquiring surface information data of a tobacco planting area, wherein the surface information data is acquired by a multispectral sensor carried by an unmanned aerial vehicle;
obtaining Landsat data of a tobacco planting area, carrying out radiation and geometric calibration on the obtained data, and calculating biological physical parameters;
acquiring meteorological data of a tobacco planting area;
acquiring disease and pest data of a tobacco planting area, wherein the disease and pest data comprises spectrum and growth environment information of tobacco in a sampling area with and without insect pests;
carrying out multiband fusion on the obtained earth surface information data, landsat data and meteorological data of the tobacco planting area to obtain a band synthesis data set;
and training the neural network by taking the synthesized wave band synthesized data set as an input variable and the plant disease and insect pest data as an output layer by adopting the BP neural network to construct the characteristic parameters of monitoring the plant disease and insect pest of the tobacco.
Further, the biophysical parameters include an enhanced vegetation index and a normalized vegetation index.
Further, the enhanced vegetation index EVI is calculated as follows:
wherein ρ is NIR 、ρ RED And ρ BLUE Near infrared, red and blue wave bands respectively representing remote sensing images and unmanned aerial vehicle images, C 1 And C 2 Are constant parameters.
Further, the calculation mode of the normalized vegetation index NDVI is as follows:
wherein ρ is NIR 、ρ RED And ρ BLUE Representing near infrared, red and blue bands of the remote sensing image and the unmanned aerial vehicle image, respectively.
Further, the meteorological data of the tobacco planting area are obtained through the ground climate data month value data of the China meteorological office data center, are discrete point data, and are converted into surface data by adopting the Kriging interpolation.
Further, the growth environment information includes surface temperature and soil moisture content.
Compared with the prior art, the invention has the beneficial effects that:
the method is based on satellite-borne and airborne multispectral remote sensing images to acquire high-time and spatial resolution remote sensing images, and combines the climate conditions and the disease and pest states of the areas where the tobacco belongs to provide a tobacco disease and pest identification method by utilizing the BP neural network, so that the simultaneous and large-scale tobacco disease and pest monitoring is realized, and theoretical basis and technical support are provided for realizing the real-time monitoring of the tobacco disease and pest
Drawings
Fig. 1 is a flowchart of a method for identifying tobacco plant diseases and insect pests provided by an embodiment of the present invention;
FIG. 2 is a Landsat image;
FIG. 3 is an image of a drone;
FIG. 4 is a graph of spectra;
fig. 5 is a plant disease and insect pest grading diagram.
Detailed Description
Examples:
the technical scheme of the invention is further described below with reference to the accompanying drawings and examples.
Referring to fig. 1, the method for identifying tobacco plant diseases and insect pests provided in this embodiment includes the following steps:
and acquiring surface information data of the tobacco planting area, wherein the surface information data is acquired by a multispectral sensor carried by the unmanned aerial vehicle. The unmanned aerial vehicle performs radiation and geometric calibration on the load and the image before and after running, and the acquired data are spliced to obtain the unmanned aerial vehicle image in a large area. The unmanned aerial vehicle image comprises a high-spatial resolution ground snapshot and ground physiological and biochemical parameters extracted based on multi-band images.
The land-based Landsat8 satellite OLI land imager comprises 15m full-color wave bands, 30m spatial resolution data are obtained in 1-7 wave bands and 9-11 wave bands, radiation and geometric calibration are carried out on tobacco planting area data, biophysical parameters such as EVI (enhanced vegetation index) and NDVI (normalized vegetation index) are calculated, and therefore the growth condition of tobacco is described based on the extracted biophysical parameters, and therefore the range of the area potentially affected by diseases and insect pests is identified.
Acquiring meteorological data of a tobacco planting area; specifically, the data of the ground climate data month value (V3.0) passes through the data center of the China weather office. The meteorological data in the dataset are discrete point data, and in order to acquire a meteorological grid covering the whole research area, the point data are converted into surface data by adopting Kerling interpolation
Acquiring disease and pest data of a tobacco planting area, wherein the disease and pest data comprises spectrum and growth environment information of tobacco in a sampling area with and without insect pests;
carrying out multiband fusion on the obtained earth surface information data, landsat data and meteorological data of the tobacco planting area to obtain a band synthesis data set;
and training the neural network by taking the synthesized wave band synthesized data set as an input variable and the plant disease and insect pest data as an output layer by adopting the BP neural network to construct the characteristic parameters of monitoring the plant disease and insect pest of the tobacco. That is, the spectral variation characteristics of the tobacco pest information are obtained by normalizing and spatially and geometrically calibrating the physiological and biochemical parameter images of each surface and sampling in a research area, and the variation information is used as an input parameter to be imported into the BP neural network. And taking the biophysical information of the ground verification point as an output result at the output layer so as to train the neural network, and constructing a simulation model suitable for large-area implementation.
Therefore, the method is based on satellite-borne and airborne multispectral remote sensing images to acquire high-time and spatial resolution remote sensing images, and combines the climate conditions and the disease and pest states of the areas where the tobacco belongs to provide the tobacco disease and pest identification method by utilizing the BP neural network, so that the tobacco disease and pest monitoring is realized in a large range at the same time, and theoretical basis and technical support are provided for realizing the real-time monitoring of the tobacco disease and pest.
Specifically, the calculation method of the enhanced vegetation index EVI is as follows:
the calculation mode of the normalized vegetation index NDVI is as follows:
in ρ NIR 、ρ RED And ρ BLUE Near infrared, red and blue wave bands respectively representing remote sensing images and unmanned aerial vehicle images, C 1 And C 2 Is a constant parameter.
Thus, the enhanced vegetation index and the normalized vegetation index can be accurately calculated through the above formula.
The method is described in further detail below in connection with an application scenario example:
(1) Region selection: the method selects special high-quality tobacco production demonstration sheets (114 DEG 11 '44' E,25 DEG 3 '10' N) in northeast of Guangdong North Yuejungxing county as an experimental area, and the tobacco field period is 3-6 months per year.
(2) And (3) data collection: weather dataset: air temperature, precipitation, evaporation, relative humidity and solar hours; landsat data: the remote sensing image shared by the geospatial data cloud is adopted, so that the data can be subjected to radiometric calibration and atmospheric correction, and accurate geometric calibration can be realized. Calculating physiological and biochemical parameters such as NDVI, EVI and the like by using Landsat data; unmanned aerial vehicle data: 4-band images acquired by unmanned aerial vehicle.
(3) And (3) collecting ground pest and disease data: healthy and unhealthy tobacco leaves are collected in the sample area, different types of spectral information are measured, and spectral characteristics are shown in fig. 4.
And (3) monitoring grass growth vigor: and constructing tobacco pest monitoring characteristic parameters by taking the multiband data and the pest data fused by different data as an input layer and an output layer respectively, and obtaining a pest grading diagram quickly as shown in figure 5.
The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement the same, and are not intended to limit the scope of the present invention. All equivalent changes or modifications made in accordance with the essence of the present invention are intended to be included within the scope of the present invention.
Claims (6)
1. A method for identifying tobacco pests, comprising:
acquiring surface information data of a tobacco planting area, wherein the surface information data is acquired by a multispectral sensor carried by an unmanned aerial vehicle;
obtaining Landsat data of a tobacco planting area, carrying out radiation and geometric calibration on the obtained data, and calculating biological physical parameters;
acquiring meteorological data of a tobacco planting area;
acquiring plant disease and insect pest data of a tobacco planting area, wherein the plant disease and insect pest data comprises spectrum and growth environment information of tobacco in insect pest and insect pest-free sampling areas;
carrying out multiband fusion on the obtained earth surface information data, landsat data and meteorological data of the tobacco planting area to obtain a band synthesis data set;
the method comprises the steps of carrying out normalization and space geometric calibration on each surface physiological and biochemical parameter image, sampling in a research area, obtaining spectral change characteristics of tobacco pest and disease information, and introducing the change information into a BP neural network as an input parameter; and taking the biophysical information of the ground verification point as an output result at the output layer so as to train the neural network, and constructing a simulation model suitable for large-area implementation.
2. The tobacco pest identification method of claim 1, wherein the biophysical parameters include an enhanced vegetation index and a normalized vegetation index.
3. The tobacco pest identification method of claim 2, wherein the enhanced vegetation index EVI is calculated as follows:
in the method, in the process of the invention,、/>and->Near infrared, red and blue wave bands respectively representing remote sensing images and unmanned aerial vehicle images, C 1 And C 2 Are constant parameters.
4. The tobacco pest identification method of claim 2, wherein the normalized vegetation index NDVI is calculated as follows:
in the method, in the process of the invention,、/>and respectively representing near infrared and red light of the remote sensing image and the unmanned aerial vehicle image.
5. The method of claim 1, wherein the weather data of the tobacco planting area is obtained by ground weather data month value data of a data center of the chinese weather bureau, is discrete point data, and the point data is converted into the face data by using kriging interpolation.
6. The method of claim 1, wherein the growth environment information comprises surface temperature and soil moisture content.
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| CN113239825B (en) * | 2021-05-19 | 2022-08-19 | 四川中烟工业有限责任公司 | High-precision tobacco beetle detection method in complex scene |
| CN113435252B (en) * | 2021-05-27 | 2023-09-29 | 广西壮族自治区烟草公司百色市公司 | Remote sensing-based tobacco pest monitoring and early warning method and system |
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