CN111832441A - Spodoptera frugiperda prevention and control system and method - Google Patents
Spodoptera frugiperda prevention and control system and method Download PDFInfo
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Abstract
The invention belongs to the technical field of intelligent insect killing, and discloses a system and a method for preventing and controlling Spodoptera frugiperda, wherein the system for preventing and controlling Spodoptera frugiperda comprises the following components: illumination module, image acquisition module, image processing module, central control module, whole image generation module, image segmentation module, image analysis module, Spodoptera frugiperda detection module, Spodoptera frugiperda activity information confirm module, Spodoptera frugiperda prevention and cure module. According to the method, the activity information of spodoptera frugiperda is obtained by collecting the field images, and the collected images are processed and analyzed, so that the image definition can be improved, the frequent activity time and the intensive activity area of spodoptera frugiperda can be more accurately determined, and the spodoptera frugiperda can be effectively prevented and treated. Meanwhile, the invention can avoid the problems of pesticide residue, food safety, soil environment pollution, drug resistance and natural enemy damage caused by long-term large-scale unreasonable use of the pesticide.
Description
Technical Field
The invention belongs to the technical field of intelligent insect killing, and particularly relates to a system and a method for preventing and controlling Spodoptera frugiperda.
Background
At present, Spodoptera frugiperda, commonly known as a comamonas, originates from tropical and subtropical regions in America, has the characteristics of strong migratory flight capability, wide host range, serious pest damage and high prevention and control difficulty, and is an important agricultural pest for global early warning of Food and Agricultural Organization (FAO) in United nations. 13 days 1 month in 2019, the insect is introduced into Yunnan province in China, and after 4 months, the insect is rapidly diffused in corn regions in south China; and 5, 9 days, the department of agricultural rural plantation management will have the same tendency as the national agricultural technology popularization service center, so that the harm and the occurrence situation of spodoptera frugiperda are notified, and the monitoring, prevention and control work of the spodoptera frugiperda is promoted to be done all over the country. Until now, Spodoptera frugiperda has invaded 1540 counties in 26 provinces of China, the corn emergence area reaches 1672 ten thousand acres, the subsequent re-emergence situation is obvious, the emergence area is wider, the damage degree is more serious, and more harmful crops are produced. At present, chemical pesticides are mainly applied to a method for preventing and controlling Spodoptera frugiperda, but the current field harm investigation needs manual investigation and is easy to investigate and not prepared, so that the reasonable use of the chemical pesticides is influenced, and the long-term large-scale unreasonable use of the chemical pesticides can cause the problems of pesticide residue, food safety, soil environment pollution, drug resistance and damage to natural enemies.
Through the above analysis, the problems and defects of the prior art are as follows: the judgment of field damage of Spodoptera frugiperda is inaccurate, and long-term large-scale unreasonable use of the pesticide can cause the problems of pesticide residue, food safety, soil environmental pollution, pesticide resistance and damage of natural enemies.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a system and a method for preventing and controlling Spodoptera frugiperda.
The invention is realized in such a way that a method for preventing spodoptera frugiperda comprises the following steps:
step one, the illumination module carries out the field illumination through the light, and in the field illumination time quantum, the image acquisition module carries the insect attitude image on the camera collection field plant through unmanned aerial vehicle, and the insect attitude includes: ova, larvae, pupae, adults;
acquiring pest state image data on the field plants according to the image acquisition module, and denoising and normalizing the image by the image processing module through an image processing program;
thirdly, according to the processed image data, the central control module is respectively connected with the illumination module, the image acquisition module, the image processing module, the whole image generation module, the image segmentation module, the image analysis module, the spodoptera frugiperda detection module, the spodoptera frugiperda activity information determination module and the spodoptera frugiperda control module, and the master control machine is used for controlling the normal operation of each module;
fourthly, the central control module controls the whole image generation module, and the collected images are synthesized through an image generation program to generate a field whole image; the image segmentation module segments the whole field image through an image segmentation program to obtain a crop image and an image of an area to be analyzed;
after the image segmentation is finished, the image analysis module analyzes the image of the area to be analyzed through an image analysis program; the Spodoptera frugiperda detection module detects Spodoptera frugiperda through a Spodoptera frugiperda detection program and determines the position of the Spodoptera frugiperda; the spodoptera frugiperda activity information determining module determines the number of spodoptera frugiperda in different time periods and different areas through a spodoptera frugiperda activity information determining program to obtain time and position information of frequent spodoptera frugiperda activity;
step six, according to the time and position information of frequent spodoptera frugiperda activity, the spodoptera frugiperda control module sprays spodoptera frugiperda insecticide in a determined time and region through a spodoptera frugiperda control program so as to achieve the control indexes that the seedling stage of crops reaches 5%, and the ear stage of the crops reaches 10% and 15%; in the spraying process of the spodoptera frugiperda insecticide, the spraying process is reasonably planned according to the collected data;
the synthesis of the collected images and the generation of the field integral image are carried out in an imaging overlapping area of the imaging module with adjacent field angles, and the synthesis method specifically comprises the following steps:
(1) matching the number of pixel points in the imaging overlapping region of the imaging module;
(2) replacing pixel points within the imaging overlap region of the imaging module having a large field angle with pixel points within the imaging overlap region of the imaging module having a small field angle when the magnification of the imaging overlap region is enlarged;
(3) forming a first reference area by taking a central pixel point as a center in the imaging overlapping area of the imaging module with a large field angle; forming a second reference area by taking a central pixel point as a center in the imaging overlapping area of the imaging module with a small field angle and taking each pixel point as a center in the range of adding or subtracting n pixel points respectively in the axial direction and the radial direction;
(4) and respectively obtaining the difference value of the first reference area and each second reference area, wherein when the difference value of the first reference area and the second reference area is the minimum, the pixel point of the second reference area at the center is the mapping of the central pixel point of the first reference area on the imaging area of the imaging module with a small field angle.
Further, the denoising and normalization processing of the image comprises the following steps:
(1) acquiring a filtering central point of an image to be processed;
(2) acquiring a filtering window and a plurality of filtering directions corresponding to the filtering central point;
(3) acquiring a filtering pixel point in the filtering direction;
(4) acquiring the normalized filtering weight of the filtering pixel point;
(5) acquiring a filtering value of the filtering central point according to the filtering pixel point and the normalized filtering weight;
(6) and (5) sequentially taking each pixel point of the image to be processed as a filtering central point, and repeating the steps (2) to (5) to finish image denoising.
Further, the obtaining the normalized filtering weight of the filtering pixel point includes:
obtaining the average value of the filtering pixel points corresponding to each filtering direction; obtaining the filtering direction variance according to the filtering pixel points and the mean value of the filtering pixel points;
acquiring a filtering direction weight according to the filtering direction variance; acquiring the filtering weight of the filtering pixel point according to the filtering direction weight; and acquiring the normalized filtering weight according to the filtering weight.
Further, the segmentation of the field overall image comprises the following steps:
selecting effective segmentation results from the multi-scale segmentation result set;
constructing a multi-scale hierarchical region merging model, and merging hierarchical regions in a global hierarchical range;
and extracting energy and the minimum segmentation region combination to determine a set label of a suitable segmentation region, and generating an optimal segmentation result of the image according to the label.
Further, the constructing a multi-scale hierarchical region merging model, and the merging hierarchical regions in the global hierarchical scope specifically includes:
traversing all the areas of the upper level hierarchy of each partition area in the set T, acquiring the intersection ratio of each partition area and all other areas, selecting the area with the maximum intersection ratio as a father node area, and further acquiring the father-son relationship between each partition area and other partition areas in the set T;
taking the division region combination with the minimum threshold as an initial hierarchy, establishing a hierarchical region merging tree for multi-scale image division according to the parent-child relationship of the hierarchical regions, wherein each node in the tree represents one division region;
calculating an internal color consistency feature fintra _ color, an internal texture consistency feature fintra _ texture and a geometric feature fgeo _ info of each segmented region vi; the smaller the values of the fintra _ color and fintra _ texture are, the more uniform the color and texture inside the region are, the better the quality of region segmentation is, and the fgeo _ for avoids over-segmentation of the segmentation region; adding the three characteristic values to obtain the segmentation quality fraction of each segmentation area in the set T;
constructing a multi-scale hierarchical region merging model, and performing segmentation region combination on a hierarchical region merging tree from bottom to top by using a dynamic programming method; in the hierarchical region merged tree structure, a sub-tree taking a node (a partition region) vi as a root, and compared with the whole hierarchical partition tree, the optimal partition region can be the node vi itself, or a union of all child nodes corresponding to the node vi, and the selection depends on who the energy between the union of the child nodes and a parent node is lower; a framework suitable for dynamic programming is used for searching global optimum through a dynamic programming algorithm;
further, the analyzing the image of the region to be analyzed is:
1) aiming at the collected image information, identifying a smooth area of the image surface;
2) if the flat area is identified, the identified flat area is sketched; if no flat area is identified, returning to '1');
3) calculating the gray value of the delineated flat area;
4) carrying out scratching treatment on the drawn smooth area aiming at the acquired image;
5) return "1)"; and (4) until the calculated gray value of the flat area is maximum, namely the position of the spodoptera frugiperda.
Further, during the spraying of the spodoptera frugiperda insecticide, the spraying process is planned reasonably;
the spodoptera frugiperda insecticide consists of emamectin benzoate, indoxacarb, an emulsifier, an organic solvent, a dispersant and water.
Further, the process of preventing and controlling according to the insect state image on the field plant acquired by the image acquisition module comprises the following steps:
adult insects and cocoon pupas reach the level of medication control through lamp attraction, sex attraction and food attraction technologies;
the larvae, 1-3 instar larvae can be sprayed with the above agents for whole plant, the middle-upper part of the corn plant is sprayed with emphasis on the egg laying of the immigration type adults, and the lower part of the corn plant is sprayed with emphasis on the egg laying of the adults; 4-6 instar larvae can be sprayed with 10% chlorfenapyr suspending agent, 20% chlorantraniliprole suspending agent and 60 g/L spinetoram suspending agent;
laying eggs, isolating crops, and spraying corresponding pesticides.
Another object of the present invention is to provide a spodoptera frugiperda control system for implementing the spodoptera frugiperda control method, the spodoptera frugiperda control system comprising:
the illumination module is connected with the central control module and is used for field illumination through an illuminating lamp;
image acquisition module is connected with central control module for carry the insect attitude image on the camera gathers field plant through unmanned aerial vehicle, the insect attitude includes: ova, larvae, pupae, adults;
the image processing module is connected with the central control module and is used for carrying out denoising and normalization processing on the image through an image processing program; the image denoising and normalization processing method comprises the following steps: acquiring a filtering central point of an image to be processed, and acquiring a filtering window and a plurality of filtering directions corresponding to the filtering central point; acquiring a filtering pixel point in the filtering direction, and acquiring a normalized filtering weight of the filtering pixel point; acquiring a filtering value of the filtering central point according to the filtering pixel point and the normalized filtering weight; sequentially taking each pixel point of the image to be processed as a filtering central point, and repeating the steps (2) to (5) to finish image denoising;
the central control module is connected with the illumination module, the image acquisition module, the image processing module, the whole image generation module, the image segmentation module, the image analysis module, the spodoptera frugiperda detection module, the spodoptera frugiperda activity information determination module and the spodoptera frugiperda control module and is used for controlling the normal operation of each module through a master control machine;
the integral image generation module is connected with the central control module and used for synthesizing the collected images through an image generation program to generate a field integral image;
the image segmentation module is connected with the central control module and used for segmenting the field integral image through an image segmentation program to obtain a crop image and an image of an area to be analyzed; selecting effective segmentation results from the multi-scale segmentation result set; constructing a multi-scale hierarchical region merging model, and merging hierarchical regions in a global hierarchical range; extracting energy and the minimum segmentation area combination to determine a set label of a suitable segmentation area, and generating an optimal segmentation result of the image according to the label;
the image analysis module is connected with the central control module and used for analyzing the image of the area to be analyzed through an image analysis program;
further, control system of Spodoptera frugiperda still includes:
the Spodoptera frugiperda detection module is connected with the central control module and used for detecting Spodoptera frugiperda through a Spodoptera frugiperda detection program and determining the position of the Spodoptera frugiperda; aiming at the collected image information, identifying a smooth area of the image surface; if the flat area is identified, the identified flat area is sketched; if no flat area is identified, returning to '1'); calculating the gray value of the delineated smooth area, and carrying out scratching treatment on the delineated smooth area aiming at the acquired image; return "1)"; until the calculated gray value of the flat area is maximum, the gray value is the position of the spodoptera frugiperda;
the Spodoptera frugiperda activity information determining module is connected with the central control module and used for determining the number of Spodoptera frugiperda in different time periods and different areas through a Spodoptera frugiperda activity information determining program to obtain time and position information of frequent Spodoptera frugiperda activity;
spodoptera frugiperda prevention and control module is connected with central control module for through Spodoptera frugiperda prevention and control procedure at definite time and regional spraying of carrying out Spodoptera frugiperda insecticide, reach 5% in order to reach crops seedling stage, the prevention and control index that 10% ear stage reached 15% in the loudspeaker mouth stage.
By combining all the technical schemes, the invention has the advantages and positive effects that: the field illumination is carried out through the illumination module through the illuminating lamp, and the field image is acquired through the unmanned aerial vehicle carrying the camera shooting image by the image acquisition module; the image processing module carries out denoising and normalization processing on the images through an image processing program, and the whole image generation module carries out synthesis on the collected images through an image generation program to generate a field whole image; the image segmentation module segments the whole field image through an image segmentation program to obtain a crop image and an image of an area to be analyzed; the image analysis module analyzes the image of the area to be analyzed through an image analysis program, and the spodoptera frugiperda detection module detects the spodoptera frugiperda through a spodoptera frugiperda detection program and determines the position of the spodoptera frugiperda; the spodoptera frugiperda activity information determining module determines the number of spodoptera frugiperda in different time periods and different areas through a spodoptera frugiperda activity information determining program to obtain time and position information of frequent spodoptera frugiperda activity; and the Spodoptera frugiperda control module sprays Spodoptera frugiperda insecticide in a determined time and region through a Spodoptera frugiperda control program. According to the method, the activity information of spodoptera frugiperda is obtained by collecting the field images, and the collected images are processed and analyzed, so that the image definition can be improved, the frequent activity time and the intensive activity area of spodoptera frugiperda can be more accurately determined, and the spodoptera frugiperda can be effectively prevented and treated. Meanwhile, the invention can avoid the problems of pesticide residue, food safety, soil environment pollution, drug resistance and natural enemy damage caused by long-term large-scale unreasonable use of the pesticide.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained from the drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a spodoptera frugiperda control system provided in an embodiment of the present invention.
Fig. 2 is a flowchart of a method for controlling spodoptera frugiperda according to an embodiment of the present invention.
Fig. 3 is a flowchart of a method for denoising and normalizing an image according to an embodiment of the present invention.
Fig. 4 is a flowchart of a method for synthesizing the collected images to generate the field overall image according to the embodiment of the present invention.
Fig. 5 is a flowchart of a method for analyzing an image of an area to be analyzed according to an embodiment of the present invention.
In the figure: 1. an illumination module; 2. an image acquisition module; 3. an image processing module; 4. a central control module; 5. an overall image generation module; 6. an image segmentation module; 7. an image analysis module; 8. a Spodoptera frugiperda detection module; 9. a Spodoptera frugiperda activity information determining module; 10. spodoptera frugiperda control module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Aiming at the problems in the prior art, the invention provides a system and a method for preventing and controlling Spodoptera frugiperda, and the invention is described in detail with reference to the attached drawings.
As shown in fig. 1, the system for controlling spodoptera frugiperda provided in the embodiment of the present invention includes: illumination module 1, image acquisition module 2, image processing module 3, central control module 4, whole image generation module 5, image segmentation module 6, image analysis module 7, Spodoptera frugiperda detection module 8, Spodoptera frugiperda activity information confirm module 9, Spodoptera frugiperda prevention and cure module 10.
And the illumination module 1 is connected with the central control module 4 and is used for field illumination through an illuminating lamp.
The image acquisition module 2 is connected with the central control module 4 and used for carrying a camera image through the unmanned aerial vehicle to acquire field images.
And the image processing module 3 is connected with the central control module 4 and is used for carrying out denoising and normalization processing on the image through an image processing program.
And the central control module 4 is connected with the illumination module 1, the image acquisition module 2, the image processing module 3, the whole image generation module 5, the image segmentation module 6, the image analysis module 7, the spodoptera frugiperda detection module 8, the spodoptera frugiperda activity information determination module 9 and the spodoptera frugiperda prevention and control module 10 and is used for controlling the normal operation of each module through a master control machine.
And the whole image generation module 5 is connected with the central control module 4 and is used for synthesizing the collected images through an image generation program to generate a field whole image.
And the image segmentation module 6 is connected with the central control module 4 and is used for segmenting the whole field image through an image segmentation program to obtain a crop image and an image of an area to be analyzed.
And the image analysis module 7 is connected with the central control module 4 and is used for analyzing the image of the area to be analyzed through an image analysis program.
Spodoptera frugiperda detection module 8 is connected with central control module 4 for detect Spodoptera frugiperda through Spodoptera frugiperda detection program, confirm Spodoptera frugiperda's position.
And the spodoptera frugiperda activity information determining module 9 is connected with the central control module 4 and is used for determining the number of spodoptera frugiperda in different time periods and different regions through a spodoptera frugiperda activity information determining program and obtaining the frequent time and position information of spodoptera frugiperda activity.
Spodoptera frugiperda prevention and control module 10 is connected with central control module 4 for through Spodoptera frugiperda prevention and control procedure the spraying of Spodoptera frugiperda insecticide is carried out with the region in definite time.
As shown in fig. 2, the method for controlling spodoptera frugiperda provided by the embodiment of the present invention includes the following steps:
s101: illumination module carries out the field illumination through the light, and in the field illumination time quantum, the image acquisition module carries the insect attitude image on the camera gathers the field plant through unmanned aerial vehicle, and the insect attitude includes: ova, larvae, pupae, adults.
S102: according to the insect state image data on the field plants collected by the image collecting module, the image processing module carries out denoising and normalization processing on the images through an image processing program.
S103: according to the image data who handles the completion, central control module is connected with illumination module, image acquisition module, image processing module, whole image generation module, image segmentation module, image analysis module, spodoptera frugiperda detection module, spodoptera frugiperda activity information confirm module, spodoptera frugiperda prevention and cure module respectively, controls each module normal operating through the master control machine.
S104: the central control module controls the whole image generation module, and the collected images are synthesized through an image generation program to generate a field whole image; and the image segmentation module segments the whole field image through an image segmentation program to obtain a crop image and an image of an area to be analyzed.
S105: after the image segmentation is finished, the image analysis module analyzes the image of the area to be analyzed through an image analysis program; the Spodoptera frugiperda detection module detects Spodoptera frugiperda through a Spodoptera frugiperda detection program and determines the position of the Spodoptera frugiperda; and the spodoptera frugiperda activity information determining module determines the quantity of spodoptera frugiperda in different time periods and different areas through a spodoptera frugiperda activity information determining program to obtain the time and position information of frequent spodoptera frugiperda activity.
S106: according to the frequent time and position information of spodoptera frugiperda activity, the spodoptera frugiperda control module sprays spodoptera frugiperda insecticide in a determined time and region through a spodoptera frugiperda control program so as to achieve the control indexes that the seedling stage of crops reaches 5%, and the bell mouth stage reaches 10% and the ear stage reaches 15%; in the spraying process of the spodoptera frugiperda insecticide, the spraying process is reasonably planned according to the collected data.
The image denoising and normalization processing method provided by the invention comprises the following steps:
s201: acquiring a filtering central point of an image to be processed;
s202: acquiring a filtering window and a plurality of filtering directions corresponding to the filtering central point;
s203: acquiring a filtering pixel point in the filtering direction;
s204: acquiring the normalized filtering weight of the filtering pixel point;
s205: acquiring a filtering value of the filtering central point according to the filtering pixel point and the normalized filtering weight;
s206: and (4) taking each pixel point of the image to be processed as a filtering central point in sequence, and repeating the steps S202-S205 to finish image denoising.
The invention provides a method for synthesizing collected images to generate field integral images in an imaging overlapping area of imaging areas of imaging modules with adjacent large and small field angles, which comprises the following steps:
s301: matching the number of pixel points in the imaging overlapping region of the imaging module;
s302: replacing pixel points within the imaging overlap region of the imaging module having a large field angle with pixel points within the imaging overlap region of the imaging module having a small field angle when the magnification of the imaging overlap region is enlarged;
s303: forming a first reference area by taking a central pixel point as a center in the imaging overlapping area of the imaging module with a large field angle; forming a second reference area by taking a central pixel point as a center in the imaging overlapping area of the imaging module with a small field angle and taking each pixel point as a center in the range of adding or subtracting n pixel points respectively in the axial direction and the radial direction;
s304: and respectively obtaining the difference value of the first reference area and each second reference area, wherein when the difference value of the first reference area and the second reference area is the minimum, the pixel point of the second reference area at the center is the mapping of the central pixel point of the first reference area on the imaging area of the imaging module with a small field angle.
The method for acquiring the normalized filtering weight of the filtering pixel point comprises the following steps:
obtaining the average value of the filtering pixel points corresponding to each filtering direction; obtaining the filtering direction variance according to the filtering pixel points and the mean value of the filtering pixel points;
acquiring a filtering direction weight according to the filtering direction variance; acquiring the filtering weight of the filtering pixel point according to the filtering direction weight; and acquiring the normalized filtering weight according to the filtering weight.
The invention provides a method for segmenting a field integral image, which comprises the following steps:
selecting effective segmentation results from the multi-scale segmentation result set;
constructing a multi-scale hierarchical region merging model, and merging hierarchical regions in a global hierarchical range;
and extracting energy and the minimum segmentation region combination to determine a set label of a suitable segmentation region, and generating an optimal segmentation result of the image according to the label.
The invention provides a method for constructing a multi-scale hierarchical region merging model, which specifically comprises the following steps of:
traversing all the areas of the upper level hierarchy of each partition area in the set T, acquiring the intersection ratio of each partition area and all other areas, selecting the area with the maximum intersection ratio as a father node area, and further acquiring the father-son relationship between each partition area and other partition areas in the set T;
taking the division region combination with the minimum threshold as an initial hierarchy, establishing a hierarchical region merging tree for multi-scale image division according to the parent-child relationship of the hierarchical regions, wherein each node in the tree represents one division region;
calculating an internal color consistency feature fintra _ color, an internal texture consistency feature fintra _ texture and a geometric feature fgeo _ info of each segmented region vi; the smaller the values of the fintra _ color and fintra _ texture are, the more uniform the color and texture inside the region are, the better the quality of region segmentation is, and the fgeo _ for avoids over-segmentation of the segmentation region; adding the three characteristic values to obtain the segmentation quality fraction of each segmentation area in the set T;
constructing a multi-scale hierarchical region merging model, and performing segmentation region combination on a hierarchical region merging tree from bottom to top by using a dynamic programming method; in the hierarchical region merged tree structure, a sub-tree taking a node (a partition region) vi as a root, and compared with the whole hierarchical partition tree, the optimal partition region can be the node vi itself, or a union of all child nodes corresponding to the node vi, and the selection depends on who the energy between the union of the child nodes and a parent node is lower; and the dynamic programming framework is suitable for searching global optimum through a dynamic programming algorithm.
The invention provides a method for analyzing an image of a region to be analyzed, which comprises the following steps:
s401: aiming at the collected image information, identifying a smooth area of the image surface;
s402: if the flat area is identified, the identified flat area is sketched; if the flat area is not identified, returning to the step S401;
s403: calculating the gray value of the delineated flat area;
s404: carrying out scratching treatment on the drawn smooth area aiming at the acquired image;
s405: returning to 'S401'; and (4) until the calculated gray value of the flat area is maximum, namely the position of the spodoptera frugiperda.
In the spraying of the spodoptera frugiperda insecticide, the spraying process is planned reasonably;
the spodoptera frugiperda insecticide consists of emamectin benzoate, indoxacarb, an emulsifier, an organic solvent, a dispersant and water.
The process for preventing and controlling the insect state image on the field plant acquired by the image acquisition module provided by the invention comprises the following steps:
adult insects and cocoon pupas reach the level of medication control through lamp attraction, sex attraction and food attraction technologies;
the larvae, 1-3 instar larvae can be sprayed with the above agents for whole plant, the middle-upper part of the corn plant is sprayed with emphasis on the egg laying of the immigration type adults, and the lower part of the corn plant is sprayed with emphasis on the egg laying of the adults; 4-6 instar larvae can be sprayed with 10% chlorfenapyr suspending agent, 20% chlorantraniliprole suspending agent and 60 g/L spinetoram suspending agent;
laying eggs, isolating crops, and spraying corresponding pesticides.
The working principle of the invention is as follows: illumination module 1 carries out the field illumination through the light, and in the field illumination time quantum, image acquisition module 2 carries the insect attitude image on the camera gathers field plant through unmanned aerial vehicle, and the insect attitude includes: ova, larvae, pupae, adults. According to the insect state image data on the field plants collected by the image collecting module 2, the image processing module 3 carries out denoising and normalization processing on the images through an image processing program.
According to the image data that the processing was accomplished, central control module 4 is connected with illumination module 1, image acquisition module 2, image processing module 3, whole image generation module 5, image segmentation module 6, image analysis module 7, spodoptera frugiperda detection module 8, spodoptera frugiperda activity information confirm module 9, spodoptera frugiperda prevents and treats module 10, controls each module normal operating through the master control machine respectively. The central control module 4 controls the whole image generation module, synthesizes the collected images through an image generation program and generates a field whole image; the image segmentation module 6 segments the field integral image through an image segmentation program to obtain a crop image and an image of an area to be analyzed.
After the image segmentation is completed, the image analysis module 7 analyzes the image of the area to be analyzed through an image analysis program; the spodoptera frugiperda detection module 8 detects spodoptera frugiperda through a spodoptera frugiperda detection program and determines the position of the spodoptera frugiperda; and the spodoptera frugiperda activity information determining module 9 determines the number of spodoptera frugiperda in different time periods and different regions through a spodoptera frugiperda activity information determining program to obtain time and position information of frequent spodoptera frugiperda activity. According to the frequent time and position information of spodoptera frugiperda activity, the spodoptera frugiperda control module 10 sprays spodoptera frugiperda insecticide in a determined time and region through a spodoptera frugiperda control program so as to achieve the control indexes that the seedling stage of crops reaches 5%, and the outter stage reaches 10% and the heading stage reaches 15%; in the spraying process of the spodoptera frugiperda insecticide, the spraying process is reasonably planned according to the collected data.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, and any modification, equivalent replacement, and improvement made by those skilled in the art within the technical scope of the present invention disclosed herein, which is within the spirit and principle of the present invention, should be covered by the present invention.
Claims (10)
1. A method for controlling Spodoptera frugiperda, comprising:
step one, the illumination module carries out the field illumination through the light, and in the field illumination time quantum, the image acquisition module carries the insect attitude image on the camera collection field plant through unmanned aerial vehicle, and the insect attitude includes: ova, larvae, pupae, adults;
acquiring pest state image data on the field plants according to the image acquisition module, and denoising and normalizing the image by the image processing module through an image processing program;
thirdly, according to the processed image data, the central control module is respectively connected with the illumination module, the image acquisition module, the image processing module, the whole image generation module, the image segmentation module, the image analysis module, the spodoptera frugiperda detection module, the spodoptera frugiperda activity information determination module and the spodoptera frugiperda control module, and the master control machine is used for controlling the normal operation of each module;
fourthly, the central control module controls the whole image generation module, and the collected images are synthesized through an image generation program to generate a field whole image; the image segmentation module segments the whole field image through an image segmentation program to obtain a crop image and an image of an area to be analyzed;
after the image segmentation is finished, the image analysis module analyzes the image of the area to be analyzed through an image analysis program; the Spodoptera frugiperda detection module detects Spodoptera frugiperda through a Spodoptera frugiperda detection program and determines the position of the Spodoptera frugiperda; the spodoptera frugiperda activity information determining module determines the number of spodoptera frugiperda in different time periods and different areas through a spodoptera frugiperda activity information determining program to obtain time and position information of frequent spodoptera frugiperda activity;
step six, according to the time and position information of frequent spodoptera frugiperda activity, the spodoptera frugiperda control module sprays spodoptera frugiperda insecticide in a determined time and region through a spodoptera frugiperda control program so as to achieve the control indexes that the seedling stage of crops reaches 5%, and the ear stage of the crops reaches 10% and 15%; in the spraying process of the spodoptera frugiperda insecticide, the spraying process is reasonably planned according to the collected data;
the synthesis of the collected images and the generation of the field integral image are carried out in an imaging overlapping area of the imaging module with adjacent field angles, and the synthesis method specifically comprises the following steps:
(1) matching the number of pixel points in the imaging overlapping region of the imaging module;
(2) replacing pixel points within the imaging overlap region of the imaging module having a large field angle with pixel points within the imaging overlap region of the imaging module having a small field angle when the magnification of the imaging overlap region is enlarged;
(3) forming a first reference area by taking a central pixel point as a center in the imaging overlapping area of the imaging module with a large field angle; forming a second reference area by taking a central pixel point as a center in the imaging overlapping area of the imaging module with a small field angle and taking each pixel point as a center in the range of adding or subtracting n pixel points respectively in the axial direction and the radial direction;
(4) and respectively obtaining the difference value of the first reference area and each second reference area, wherein when the difference value of the first reference area and the second reference area is the minimum, the pixel point of the second reference area at the center is the mapping of the central pixel point of the first reference area on the imaging area of the imaging module with a small field angle.
2. The method for controlling spodoptera frugiperda as claimed in claim 1, wherein said denoising and normalizing the image comprises the steps of:
(1) acquiring a filtering central point of an image to be processed;
(2) acquiring a filtering window and a plurality of filtering directions corresponding to the filtering central point;
(3) acquiring a filtering pixel point in the filtering direction;
(4) acquiring the normalized filtering weight of the filtering pixel point;
(5) acquiring a filtering value of the filtering central point according to the filtering pixel point and the normalized filtering weight;
(6) and (5) sequentially taking each pixel point of the image to be processed as a filtering central point, and repeating the steps (2) to (5) to finish image denoising.
3. The method for preventing and controlling spodoptera frugiperda as claimed in claim 2, wherein said obtaining normalized filtering weights of said filtering pixel points comprises:
obtaining the average value of the filtering pixel points corresponding to each filtering direction; obtaining the filtering direction variance according to the filtering pixel points and the mean value of the filtering pixel points;
acquiring a filtering direction weight according to the filtering direction variance; acquiring the filtering weight of the filtering pixel point according to the filtering direction weight; and acquiring the normalized filtering weight according to the filtering weight.
4. The method for controlling spodoptera frugiperda as claimed in claim 1, wherein said segmenting the field global image comprises the steps of:
selecting effective segmentation results from the multi-scale segmentation result set;
constructing a multi-scale hierarchical region merging model, and merging hierarchical regions in a global hierarchical range;
and extracting energy and the minimum segmentation region combination to determine a set label of a suitable segmentation region, and generating an optimal segmentation result of the image according to the label.
5. The method for controlling spodoptera frugiperda as claimed in claim 4, wherein said constructing a multi-scale hierarchical region merging model, and said performing hierarchical region merging in a global hierarchical scope specifically comprises:
traversing all the areas of the upper level hierarchy of each partition area in the set T, acquiring the intersection ratio of each partition area and all other areas, selecting the area with the maximum intersection ratio as a father node area, and further acquiring the father-son relationship between each partition area and other partition areas in the set T;
taking the division region combination with the minimum threshold as an initial hierarchy, establishing a hierarchical region merging tree for multi-scale image division according to the parent-child relationship of the hierarchical regions, wherein each node in the tree represents one division region;
calculating an internal color consistency feature fintra _ color, an internal texture consistency feature fintra _ texture and a geometric feature fgeo _ info of each segmented region vi; the smaller the values of the fintra _ color and fintra _ texture are, the more uniform the color and texture inside the region are, the better the quality of region segmentation is, and the fgeo _ for avoids over-segmentation of the segmentation region; adding the three characteristic values to obtain the segmentation quality fraction of each segmentation area in the set T;
constructing a multi-scale hierarchical region merging model, and performing segmentation region combination on a hierarchical region merging tree from bottom to top by using a dynamic programming method; in the hierarchical region merged tree structure, a sub-tree taking a node (a partition region) vi as a root, and compared with the whole hierarchical partition tree, the optimal partition region can be the node vi itself, or a union of all child nodes corresponding to the node vi, and the selection depends on who the energy between the union of the child nodes and a parent node is lower; and the dynamic programming framework is suitable for searching global optimum through a dynamic programming algorithm.
6. The method for controlling spodoptera frugiperda as claimed in claim 1, wherein said analyzing the image of the area to be analyzed is:
1) aiming at the collected image information, identifying a smooth area of the image surface;
2) if the flat area is identified, the identified flat area is sketched; if no flat area is identified, returning to '1');
3) calculating the gray value of the delineated flat area;
4) carrying out scratching treatment on the drawn smooth area aiming at the acquired image;
5) return "1)"; and (4) until the calculated gray value of the flat area is maximum, namely the position of the spodoptera frugiperda.
7. The method for controlling spodoptera frugiperda as claimed in claim 1, wherein spraying of spodoptera frugiperda insecticide is carried out, and the spraying process is planned reasonably;
the spodoptera frugiperda insecticide consists of emamectin benzoate, indoxacarb, an emulsifier, an organic solvent, a dispersant and water.
8. The method for controlling spodoptera frugiperda as claimed in claim 1, wherein the process of controlling according to the insect-state image on the field plants collected by the image collection module comprises:
adult insects and cocoon pupas reach the level of medication control through lamp attraction, sex attraction and food attraction technologies;
the larvae, 1-3 instar larvae can be sprayed with the above agents for whole plant, the middle-upper part of the corn plant is sprayed with emphasis on the egg laying of the immigration type adults, and the lower part of the corn plant is sprayed with emphasis on the egg laying of the adults; 4-6 instar larvae can be sprayed with 10% chlorfenapyr suspending agent, 20% chlorantraniliprole suspending agent and 60 g/L spinetoram suspending agent;
laying eggs, isolating crops, and spraying corresponding pesticides.
9. A spodoptera frugiperda control system for carrying out the method for controlling spodoptera frugiperda as described in claims 1 to 8, characterized in that the spodoptera frugiperda control system comprises:
the illumination module is connected with the central control module and is used for field illumination through an illuminating lamp;
image acquisition module is connected with central control module for carry the insect attitude image on the camera gathers field plant through unmanned aerial vehicle, the insect attitude includes: ova, larvae, pupae, adults;
the image processing module is connected with the central control module and is used for carrying out denoising and normalization processing on the image through an image processing program; the image denoising and normalization processing method comprises the following steps: acquiring a filtering central point of an image to be processed, and acquiring a filtering window and a plurality of filtering directions corresponding to the filtering central point; acquiring a filtering pixel point in the filtering direction, and acquiring a normalized filtering weight of the filtering pixel point; acquiring a filtering value of the filtering central point according to the filtering pixel point and the normalized filtering weight; sequentially taking each pixel point of the image to be processed as a filtering central point, and repeating the steps (2) to (5) to finish image denoising;
the central control module is connected with the illumination module, the image acquisition module, the image processing module, the whole image generation module, the image segmentation module, the image analysis module, the spodoptera frugiperda detection module, the spodoptera frugiperda activity information determination module and the spodoptera frugiperda control module and is used for controlling the normal operation of each module through a master control machine;
the integral image generation module is connected with the central control module and used for synthesizing the collected images through an image generation program to generate a field integral image;
the image segmentation module is connected with the central control module and used for segmenting the field integral image through an image segmentation program to obtain a crop image and an image of an area to be analyzed; selecting effective segmentation results from the multi-scale segmentation result set; constructing a multi-scale hierarchical region merging model, and merging hierarchical regions in a global hierarchical range; extracting energy and the minimum segmentation area combination to determine a set label of a suitable segmentation area, and generating an optimal segmentation result of the image according to the label;
and the image analysis module is connected with the central control module and is used for analyzing the image of the area to be analyzed through an image analysis program.
10. The spodoptera frugiperda control system of claim 9, further comprising:
the Spodoptera frugiperda detection module is connected with the central control module and used for detecting Spodoptera frugiperda through a Spodoptera frugiperda detection program and determining the position of the Spodoptera frugiperda; aiming at the collected image information, identifying a smooth area of the image surface; if the flat area is identified, the identified flat area is sketched; if no flat area is identified, returning to '1'); calculating the gray value of the delineated smooth area, and carrying out scratching treatment on the delineated smooth area aiming at the acquired image; return "1)"; until the calculated gray value of the flat area is maximum, the gray value is the position of the spodoptera frugiperda;
the Spodoptera frugiperda activity information determining module is connected with the central control module and used for determining the number of Spodoptera frugiperda in different time periods and different areas through a Spodoptera frugiperda activity information determining program to obtain time and position information of frequent Spodoptera frugiperda activity;
spodoptera frugiperda prevention and control module is connected with central control module for through Spodoptera frugiperda prevention and control procedure at definite time and regional spraying of carrying out Spodoptera frugiperda insecticide, reach 5% in order to reach crops seedling stage, the prevention and control index that 10% ear stage reached 15% in the loudspeaker mouth stage.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113689034A (en) * | 2021-08-19 | 2021-11-23 | 云南省气候中心(云南省生态气象和卫星遥感中心) | Method for comprehensively predicting growth suitability of spodoptera frugiperda |
| CN114431207A (en) * | 2022-03-04 | 2022-05-06 | 湖南农业大学 | Control method for spodoptera frugiperda |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110414615A (en) * | 2019-08-02 | 2019-11-05 | 中国科学院合肥物质科学研究院 | Image is repaired based on interim depth and improves the corn Spodopterafrugiperda detection method of Double-DQN technology |
| CN110428413A (en) * | 2019-08-02 | 2019-11-08 | 中国科学院合肥物质科学研究院 | A kind of Spodopterafrugiperda adult image detecting method lured for lamp under equipment |
| CN110622932A (en) * | 2019-10-13 | 2019-12-31 | 北京中捷四方生物科技股份有限公司 | Sex attractant for spodoptera frugiperda, matched trapping device and using method |
| CN111235286A (en) * | 2020-03-12 | 2020-06-05 | 中国农业科学院植物保护研究所 | Spodoptera frugiperda specific SS-CO I primer and application thereof |
| CN111269968A (en) * | 2020-03-13 | 2020-06-12 | 华南师范大学 | PCR-RFLP-based method for rapidly identifying Spodoptera frugiperda |
-
2020
- 2020-06-28 CN CN202010599386.8A patent/CN111832441A/en not_active Withdrawn
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110414615A (en) * | 2019-08-02 | 2019-11-05 | 中国科学院合肥物质科学研究院 | Image is repaired based on interim depth and improves the corn Spodopterafrugiperda detection method of Double-DQN technology |
| CN110428413A (en) * | 2019-08-02 | 2019-11-08 | 中国科学院合肥物质科学研究院 | A kind of Spodopterafrugiperda adult image detecting method lured for lamp under equipment |
| CN110622932A (en) * | 2019-10-13 | 2019-12-31 | 北京中捷四方生物科技股份有限公司 | Sex attractant for spodoptera frugiperda, matched trapping device and using method |
| CN111235286A (en) * | 2020-03-12 | 2020-06-05 | 中国农业科学院植物保护研究所 | Spodoptera frugiperda specific SS-CO I primer and application thereof |
| CN111269968A (en) * | 2020-03-13 | 2020-06-12 | 华南师范大学 | PCR-RFLP-based method for rapidly identifying Spodoptera frugiperda |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113689034A (en) * | 2021-08-19 | 2021-11-23 | 云南省气候中心(云南省生态气象和卫星遥感中心) | Method for comprehensively predicting growth suitability of spodoptera frugiperda |
| CN114431207A (en) * | 2022-03-04 | 2022-05-06 | 湖南农业大学 | Control method for spodoptera frugiperda |
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Application publication date: 20201027 |
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| WW01 | Invention patent application withdrawn after publication |