CN112753456B - Accurate prevention and control method and system for pine wood nematode disease based on space-time law - Google Patents
Accurate prevention and control method and system for pine wood nematode disease based on space-time law Download PDFInfo
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Abstract
The invention provides a method and a system for accurately preventing and controlling pine wilt disease based on a space-time law, which comprises the following steps: analyzing the collected pine forest image by using a trained deep learning model and a space-time law analysis model; feeding back the original image and the analysis result to a matched database for storage; and thirdly, uploading the value data to a private block chain network established among all forest zones through a block chain technology, and sharing the information. And step four, accurately preventing and controlling the pine wood nematode according to the long-term space-time record. The method has the advantages that the deep learning technology is used, the database technology is used as assistance, the pine wood nematode evolution rule is combined, and the method can be used for more accurately, intelligently and quickly controlling the pine wood nematode.
Description
Technical Field
The invention relates to a forest region disease control method, in particular to a method and a system for accurately controlling pine wood nematode diseases based on a space-time law.
Background
The information in this background section is only for enhancement of understanding of the general background of the invention and is not necessarily to be construed as an admission or any form of suggestion that this information forms the prior art that is already known to a person of ordinary skill in the art.
The pine forest area coverage area in China is large, and the pine wood nematode disease is a destructive disaster for pine trees, is listed as a major external invasive species at present, and can rapidly die once infected, even threatens a large amount of pine forests. At present, most of the existing prevention and control measures for the pine wood nematode disease in China are drug control and forest disease dead trees treatment, the former needs to spray a medicament on a large area of forest, and causes great pollution to the environment while protecting, and the latter is an inexhaustible measure, so that how to effectively, timely and accurately prevent and control the pine wood nematode disease becomes a problem to be solved urgently.
However, the inventor finds that the existing detecting and positioning method for detecting and positioning the dead pine tree caused by the pine wilt disease based on deep learning can quickly, efficiently and accurately detect the diseased pine and judge the position of the diseased pine in time, but cannot be used for early accurate prevention and control. In the prior art, no method for early and accurate prevention and control of the pine wilt disease exists.
Disclosure of Invention
In order to solve the defects of the prior art, the purpose of the disclosure is to provide a method and a system for accurately preventing and controlling the pine wilt disease based on a space-time rule.
Specifically, the technical scheme of the present disclosure is as follows:
in a first aspect of the disclosure, a method for accurately preventing and controlling pine wilt disease based on space-time law comprises the following steps:
analyzing the collected pine forest image by using a trained deep learning model and a space-time law analysis model;
feeding back the original image and the analysis result to a matched database for storage;
and thirdly, uploading the value data to a private block chain network established among all forest zones through a block chain technology, and sharing the information.
And step four, accurately preventing and controlling the pine wood nematode according to the long-term space-time record.
In a second aspect of the disclosure, a space-time law-based precise pine wood nematode prevention and control system comprises a pre-training deep learning model, a data analysis module, a database and a block chain.
In a third aspect of the disclosure, a method and/or a system for accurately controlling pine wilt disease based on space-time law is applied to the accurate control of pine wilt disease.
One or more technical schemes in the disclosure have the following beneficial effects:
(1) through the combination of the deep learning model and the space-time law analysis model, the disease condition can be found at the early stage of the pine wood nematode disease, and the fluctuation of the disease trend of the pine wood nematode disease along with the seasonal variation can be predicted, so that the accurate prevention and control of the pine wood nematode disease become possible.
(2) The information sharing of different forest zones can be realized by establishing the private block chain network among the forest zones, and the whole forest zone control diagram is established.
(3) The method has the characteristics of rapidness, high efficiency and accurate prevention and control, can accurately analyze the development trend of the nematode disease, realizes early prevention of the nematode disease, and has very important application to prevention and control of diseases and insect pests in the pine forest region.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
Embodiments of the present disclosure are described in detail below with reference to the attached drawing figures, wherein:
FIG. 1 is a schematic diagram of the precise control system for bursaphelenchus xylophilus disease based on the space-time law in example 1;
FIG. 2 is a block chain structure diagram exclusive to forest zones in example 1;
FIG. 3 is an example of classification of a space control zone in embodiment 1;
fig. 4 is an example of classification of the time prevention zone of embodiment 1.
Detailed Description
The disclosure is further illustrated with reference to specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. The experimental procedures, in which specific conditions are not noted in the following examples, are generally carried out according to conventional conditions or according to conditions recommended by the manufacturers.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. The reagents or starting materials used in the present invention can be purchased from conventional sources, and unless otherwise specified, the reagents or starting materials used in the present invention can be used in a conventional manner in the art or in accordance with the product specifications. In addition, any methods and materials similar or equivalent to those described herein can be used in the methods of the present invention. The preferred embodiments and materials described herein are intended to be exemplary only.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of the stated features, steps, operations, and/or combinations thereof, unless the context clearly indicates otherwise.
As introduced by the background art, at present, there is no effective method for accurately preventing and controlling forest area nematode diseases, most of prevention and control measures for nematode diseases are drug control and forest disease dead trees treatment, and early accurate prevention and control cannot be realized.
In one embodiment of the present disclosure, a method for accurately preventing and controlling pine wilt disease based on space-time law includes the following steps:
analyzing the collected pine forest image by using a trained deep learning model and a space-time law analysis model;
feeding back the original image and the analysis result to a matched database for storage;
and thirdly, uploading the value data to a private block chain network established among all forest zones through a block chain technology, and sharing the information.
And step four, accurately preventing and controlling the pine wood nematode according to the long-term space-time record.
Further, in the first step, the pine forest image is acquired by shooting the pine forest image at high altitude by using an unmanned aerial vehicle, wherein the shot image is an original image; specifically, the unmanned aerial vehicle is positioned 10-50 meters above the top of the pine tree.
Further, in the first step, the deep learning model is trained by using the previous year pine wood nematode data.
Further, in the first step, the deep learning model is divided into two parts, wherein one part is used for target detection and is used for framing each tree top; the other part is used to identify whether the tree has bursaphelenchus xylophilus disease by the respective tree tops.
Further, in the first step, when recognizing that a certain pine tree is attacked, the deep learning model correspondingly calculates the proportion of the attack area to the whole pine tree and the attack degree, and finally calculates the proportion of all the attack pine trees in the whole shot forest area.
Further, in the step one, the time law analysis model predicts the fluctuation of the incidence trend of the pine wood nematode disease along with the seasonal variation by adopting a time sequence analysis method.
Further, in the first step, the spatial law adopts a spatial scanning statistical method, and different areas are scanned by using irregular windows with dynamically changed sizes and positions, so as to determine the positions and sizes of the pathogenic accumulation areas.
Further, in the first step, the method for calculating the average propagation velocity by the analysis model is an accumulation method, and the formula is as follows:
wherein, C is a high-order equation constant term, different values a represent trees infected with the pine wood nematode disease in each year, an initial value X0 is determined during calculation, and the calculation is continuously repeated after the initial value X0 is substituted into a formula until adjacent calculation results are basically similar, namely the average propagation speed is obtained.
Further, in the second step, after the database acquires the model analysis data, the relative coordinates and the original image, further analysis, visualization and other operations can be performed.
Further, in the fourth step, the concrete steps of controlling the pine wood nematode disease according to the long-term space-time record are as follows:
the space law is as follows: determining a warning area of a control area, and respectively representing the warning area by three colors of green, yellow and red;
according to the propagation speed of the disease trees in the database and the distance between the disease trees, a red prevention and control area warning area (a disease tree key prevention and control area), a yellow prevention and control area warning area (a disease tree monitoring and control area) and a green prevention and control area warning area (a disease tree safety area) are determined in a statistical mode; aiming at a warning area of a red prevention and control area, preventing and controlling accurately;
the time law is as follows: determining a prevention and treatment time warning area, and respectively representing by three colors of green, yellow and red;
according to the propagation speed of the disease tree in the database, a green prevention and treatment time warning area (prevention and treatment time safety area), a yellow prevention and treatment time warning area (prevention and treatment time urgent period) and a red prevention and treatment time warning area (prevention and treatment time urgent period) which are found for the first time from the disease tree are determined.
In one embodiment of the disclosure, the system for accurately preventing and controlling the pine wood nematode disease based on the space-time law comprises a pre-training deep learning model, a data analysis module, a database and a block chain.
Further, the data analysis module comprises a disease area proportion analysis aiming at a single pine tree, a tree proportion analysis aiming at all diseases in a forest area and a relative coordinate analysis aiming at the pine trees with the diseases.
Further, the system also comprises an unmanned aerial vehicle shooting module used for acquiring the original image.
In one embodiment of the disclosure, the application of the method and/or system for accurately preventing and controlling the pine wilt disease based on the space-time law in the accurate prevention and control of the pine wilt disease is provided.
In order to make the technical solutions of the present disclosure more clearly understood by those skilled in the art, the technical solutions of the present disclosure will be described in detail below with reference to specific embodiments.
Example 1
An accurate prevention and control method for pine wilt disease based on space-time law (as shown in figure 1):
step 1: placing the unmanned aerial vehicle at a position 40 meters above the top of the pine tree for taking the pine image, and taking the pine image at high altitude by the unmanned aerial vehicle;
step 2: performing target detection processing on the image, framing a pixel block at the top of each pine tree, identifying each tree top, judging whether the pine wood nematode disease exists, marking the relative coordinates of the diseased pine trees, and calculating the proportion of the diseased area to the whole pine tree and the proportion of all the diseased pine trees to the whole forest area;
predicting the fluctuation of the disease trend of the pine wood nematode disease along with the seasonal variation according to a time sequence analysis method, determining a control time warning area (green, yellow and red), and determining a control time safety area (green), a control time urgent period (yellow) and a control time urgent period (red) which are firstly found away from a disease tree according to the propagation speed of the disease tree in a database;
according to a space scanning statistical method, scanning different areas by using irregular windows with dynamically changed sizes and positions, determining the positions and sizes of the disease accumulation areas, and finally determining the alert areas (green, yellow and red) of the control areas; according to the propagation speed of the disease trees in the database and the distance between the disease trees and the disease trees, a key control area (red) of the disease trees, a monitoring control area (yellow) of the disease trees and a safety area (green) of the disease trees are determined in a statistical manner; aiming at the key prevention and treatment area (red), the accurate prevention and treatment is carried out.
Wherein, the average propagation velocity is calculated by using an accumulation method, and the formula is as follows:
wherein, C is a high-order equation constant term, different values a represent pine nematode disease trees infected in each year, an initial value X0 is determined during calculation, and the calculation is continuously repeated after the initial value X0 is substituted into a formula until adjacent calculation results are basically similar, namely the average propagation speed is obtained.
And finally, transmitting the analysis result and the original image to a matched database.
And step 3: the database acquires the information to further analyze, mine, visualize and the like.
And 4, step 4: and uploading the value data to a private block chain network established among all forest zones by using a block chain technology to share information (as shown in figure 2).
And 5: and (3) accurately controlling pine nematode according to long-term space-time records:
the space law is as follows: and (4) determining a warning area (green, yellow and red) of the control area. According to the propagation speed of the disease trees in the database and the distance between the disease trees, a key control area (red) of the disease trees, a monitoring control area (yellow) of the disease trees and a safety area (green) of the disease trees are determined in a statistical mode. Aiming at the key control area (red), the control is accurate (as shown in figure 3).
The time law is as follows: and determining the prevention and treatment time alert zones (green, yellow and red). According to the propagation speed of the disease tree in the database, a prevention and treatment time safety zone (green), a prevention and treatment time urgent period (yellow) and a prevention and treatment time urgent period (red) which are found for the first time from the disease tree are determined (as shown in figure 4).
Although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A pine wood nematode accurate prevention and control method based on a space-time law is characterized by comprising the following steps:
analyzing the collected pine forest image by using a trained deep learning model and a space-time law analysis model;
feeding back the original image and the analysis result to a matched database for storage;
uploading the value data to a private block chain network established among all forest zones through a block chain technology, and sharing information;
step four, accurately preventing and controlling pine nematode according to long-term space-time records;
in the first step, a time law analysis model predicts the fluctuation of the incidence trend of the pine wood nematode disease along with seasonal changes by adopting a time sequence analysis method;
in the first step, the spatial law adopts a spatial scanning statistical method, and irregular windows with dynamically changed sizes and positions are used for scanning different areas to determine the positions and the sizes of the pathogenic accumulation areas;
in the first step, the method for calculating the average propagation velocity by the analysis model is an accumulation method, and the formula is as follows:
wherein C is a high-order equation constant term, different values a represent trees infected with the pine wilt disease in each year, an initial value X0 is determined during calculation, the calculation is substituted into a formula and repeated continuously until adjacent calculation results are basically similar, and the calculation results are the average propagation speed;
in the fourth step, the concrete steps of controlling the pine wilt disease according to the long-term space-time record are as follows:
the space law is as follows: determining a warning area of a control area, and respectively representing the warning area by three colors of green, yellow and red;
according to the propagation speed of the disease trees in the database and the distance between the disease trees, a red prevention and control area warning area, a yellow prevention and control area warning area and a green prevention and control area warning area are determined in a statistical mode; aiming at a warning area of a red prevention and control area, preventing and controlling accurately;
the time law is as follows: determining a prevention and treatment time warning area, and respectively representing by three colors of green, yellow and red;
and determining a green prevention and treatment time warning area, a yellow prevention and treatment time warning area and a red prevention and treatment time warning area which are found for the first time away from the diseased tree according to the propagation speed of the diseased tree in the database.
2. The method for accurately preventing and controlling the pine wilt disease based on space-time law as claimed in claim 1, wherein in the first step, the method for collecting the image of the pine forest is to use an unmanned aerial vehicle to shoot the image of the pine forest at high altitude.
3. The method for accurately controlling pine wilt disease based on spatiotemporal rules according to claim 1, wherein in the first step, the deep learning model is trained by using the previous year pine wilt disease data.
4. The method for accurately preventing and controlling the pine wilt disease based on space-time law as claimed in claim 1, wherein in the first step, the deep learning model is divided into two parts, one part is used for target detection and is used for framing each tree top; the other part is used to identify whether the tree has bursaphelenchus xylophilus disease by the respective tree tops.
5. The method for accurately preventing and controlling the pine wilt disease based on the spatio-temporal rules according to claim 1, wherein in the first step, the deep learning model calculates the proportion of the diseased area to the whole pine and the degree of disease when recognizing that a certain pine is diseased, and finally calculates the proportion of all the diseased pines in the whole photographed forest area.
6. The method for accurately preventing and controlling the pine wilt disease based on spatiotemporal rules according to claim 1, wherein in the second step, the database acquires model analysis data, relative coordinates and an original image and then carries out further analysis and visualization operation.
7. An accurate prevention and control system for bursaphelenchus xylophilus disease based on spatiotemporal laws, which is used for the method of any one of claims 1 to 6, and is characterized by comprising a pre-trained deep learning model, a data analysis module, a database and a block chain;
the data analysis module comprises the steps of analyzing the proportion of the diseased area in a single pine tree, analyzing the proportion of all diseased pine trees in a forest area and analyzing the relative coordinates of the diseased pine trees;
the system also comprises an unmanned aerial vehicle shooting module used for acquiring the original image.
8. The use of the precise pine wilt disease prevention and control system based on spatiotemporal rules according to claim 7 in the precise prevention and control of pine wilt disease.
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