Bursaphelenchus xylophilus disease intelligent identification control method and system based on artificial intelligence

Abstract

The invention discloses a pine wood nematode disease intelligent identification management and control method and system based on artificial intelligence, and relates to the technical field of forest management and control, and the system discloses a pine forest region division management and control module, a pine wood nematode disease identification module, a pine wood nematode disease treatment analysis module, and a region management and control sorting module. A pine forest region division control module and a pine wood nematode disease identification module are arranged, reasonable region control is performed on a pine forest region, pine wood nematode diseases in the pine forest region are accurately identified through deep analysis of multi-dimensional disease features, and a pine wood nematode disease treatment analysis module and a region control sorting module are arranged. The pine forest simulation management and control model of the pine wood nematode disease recognition area is constructed, the actual situation of the pine wood nematode disease recognition area in the subsequent management and control process is accurately analyzed, and the current disease situation, the subsequent disease control situation and the disease influence situation of the pine wood nematode disease recognition area are quantitatively analyzed.

Description

Technical Field

[0001] The present invention relates to the field of forest management technology, and more specifically, to an artificial intelligence-based intelligent identification and management method and system for pine wood nematode disease. Background Art

[0002] Pine wilt disease is a deadly disease that damages pine trees. The pathogenic nematodes are spread by pine vector insects (such as Monochamus alternatus) and invade the pine tree, causing the pine tree to wither and die rapidly. The disease poses a serious threat to forest resources worldwide, especially in areas where pine is the main tree species or economic forest. The impact of pine wilt disease is particularly significant.

[0003] Traditional methods of pine wilt disease identification and control mainly rely on manual inspections and pathological analysis, which is not only time-consuming and labor-intensive, but also easily affected by human factors and environmental conditions, resulting in low identification accuracy and control efficiency. With the continuous expansion of forest area and the increasing complexity of the disease situation, traditional methods can no longer meet the actual needs of current pine wilt disease prevention and control work.

[0004] In recent years, the rapid development of artificial intelligence technology has provided new possibilities for the intelligent identification and control of pine wilt disease. By using advanced technologies such as computer vision, it is possible to efficiently and accurately extract and classify features of pine disease images, thereby realizing the rapid identification of pine wilt disease. However, relying solely on intelligent recognition technology is not enough to achieve effective control of pine wilt disease. Therefore, how to combine artificial intelligence technology to build a complete set of intelligent identification and control methods and systems for pine wilt disease, and realize accurate identification, scientific management and efficient control of pine wilt disease in pine forest areas, has become a problem that needs to be solved urgently. Summary of the invention

[0005] In view of the deficiencies in the prior art, the purpose of the present invention is to provide an intelligent identification and control method and system for pine wood nematode disease based on artificial intelligence.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The artificial intelligence-based intelligent identification and control system for pine wood nematode disease includes a pine forest area division and control module, a pine wood nematode disease identification module, a pine wood nematode disease treatment and analysis module, and a regional control and sorting module;

[0008] The pine forest area division and control module is used to divide the target pine forest area into multiple pine forest control areas;

[0009] The pine wood nematode disease identification module is used to set a pine forest management cycle. After each pine forest management cycle, an aerial image of the pine forest area of ​​each pine forest management area is obtained, and then the pine wood nematode disease index of each pine forest management area is obtained. Based on the comparison result of the pine wood nematode disease index and the pine wood nematode disease standard index, it is determined whether to mark the pine forest management area as a pine wood nematode disease identification area;

[0010] The pine wood nematode disease control analysis module is used to construct a pine forest simulation control model for the pine wood nematode disease identification area, and the pine forest simulation control model performs simulation control to obtain the pine wood nematode disease control index for the pine wood nematode disease identification area;

[0011] The regional control sorting module is used to obtain the control emergency value of each pine wilt disease identification area, sort all pine wilt disease identification areas in descending order according to the control emergency value, and control the pine wilt disease identification areas in the sorted order.

[0012] Furthermore, the pine wood nematode disease index of the pine forest control area is obtained as follows: determine the prominent disease impact features and the disease-irrelevant features in the pine forest control area, mark the total number of prominent disease impact features as Lgst, mark the total number of disease-irrelevant features as Kdtp, match all prominent disease impact features into a disease impact group, sum the pine wood nematode disease identification feature values ​​of the two prominent disease impact features in the disease impact group, obtain the disease impact added value of the disease impact group, set the disease impact added limit, and when the disease impact added value of the disease impact group is greater than or equal to the disease impact added limit, mark the disease image group as the disease impact added group, mark the total number of the disease impact added group as Zdes, and use the formula The pine wood nematode disease index Dise of the pine forest control area was obtained, where e1 is the disease impact prominence coefficient, e2 is the disease impact irrelevant coefficient, and e3 is the disease impact additional coefficient.

[0013] Furthermore, the prominent features of disease impact and the features unrelated to disease impact in the pine forest control area are determined, specifically: various pine wilt disease identification features are extracted from the regional aerial images of the pine forest control area, and the real feature data of various pine wilt disease identification features are extracted, and the disease feature analysis models of various pine wilt disease identification features are obtained, and the real feature data of various pine wilt disease identification features are respectively input into the corresponding disease feature analysis models, and the pine wilt disease identification feature values ​​of various pine wilt disease identification features are output, and a pine wilt disease identification feature threshold is set. When the pine wilt disease identification feature value of the pine wilt disease identification feature is greater than or equal to the pine wilt disease identification feature threshold, the pine wilt disease identification feature is marked as a prominent feature of disease impact; when the pine wilt disease identification feature value of the pine wilt disease identification feature is less than the pine wilt disease identification feature threshold, the pine wilt disease identification feature is marked as a feature unrelated to disease impact.

[0014] Furthermore, the pine wood nematode control index Zaw in the pine wood nematode disease identification area is obtained as follows: set the simulation interval time to T, and during the simulation control process, obtain the pine wood nematode disease index of the pine forest simulation control model after each simulation interval time T until the pine forest simulation control model completes the simulation control, mark the total number of pine wood nematode disease indices as Nukp, obtain the number of disease durations Bzdg, and use the formula Zaw = Nukp*ma+Bzdg*mb to obtain the pine wood nematode control index Zaw in the pine wood nematode disease identification area, where ma is the disease control coefficient and mb is the disease duration coefficient.

[0015] Furthermore, the disease duration times Bzdg are obtained as follows: all pine wood nematode disease indices are sorted in order according to the order of acquisition time, and the two adjacent pine wood nematode disease indices after sorting are summed to obtain the pine wood nematode disease duration value, and a pine wood nematode disease duration threshold is set. When the pine wood nematode disease duration value is greater than or equal to the pine wood nematode disease duration threshold, the disease duration times are increased by one, and the disease duration times are marked as Bzdg.

[0016] Furthermore, the control emergency value of the pine wood nematode disease identification area is obtained as follows: obtain the pine wood nematode disease index Dise and the pine wood nematode control index Zaw of a pine wood nematode disease identification area, and simultaneously obtain the potential impact quantity Rek, and use the formula EMbt = 2.6 (Dise + Zaw) Rek+1.38 The control emergency value EMbt of the pine wood nematode disease identification area is obtained.

[0017] Furthermore, the potential impact quantity Rek is obtained as follows: mark the remaining pine wilt disease identification areas as potential impact identification areas, measure the distance between the pine wilt disease identification areas and each potential impact identification area, obtain the regional distance between the pine wilt disease identification areas and each potential impact identification area, set the regional standard distance, and when the regional distance is less than the regional standard distance, increase the potential impact quantity by one and mark the potential impact quantity as Rek.

[0018] Furthermore, the artificial intelligence-based intelligent identification and control method for pine wood nematode disease includes the following steps:

[0019] Step 1: Divide the target pine forest area into multiple pine forest control areas;

[0020] Step 2: After each pine forest control cycle, obtain aerial images of the pine forest areas of each pine forest control area, and then obtain the pine wood nematode disease index of each pine forest control area;

[0021] Step 3: Based on the comparison results of the pine wood nematode disease index and the pine wood nematode disease standard index, determine whether to mark the pine forest control area as a pine wood nematode disease identification area;

[0022] Step 4: Construct a pine forest simulation control model in the pine wood nematode disease identification area, and use the pine forest simulation control model to simulate control, thereby obtaining the pine wood nematode control index in the pine wood nematode disease identification area;

[0023] Step 5: Obtain the control emergency value of each pine wilt disease identification area, and sort all pine wilt disease identification areas in descending order according to the control emergency value;

[0024] Step 6: Control the pine wood nematode disease identification areas in sorted order.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. The method of the present invention ensures the reasonable control of pine wood nematode disease in pine forest areas by quantitatively analyzing the current disease situation, subsequent disease control situation and disease impact situation in the pine wood nematode disease identification area;

[0027] 2. Set up a pine forest area division and control module and a pine wilt disease identification module to carry out reasonable regional control of the pine forest area. Through in-depth analysis of disease characteristics in multiple dimensions, accurately identify pine wilt disease in the pine forest area. Set up a pine wilt disease treatment analysis module and a regional control ranking module. By constructing a pine forest simulation control model for the pine wilt disease identification area, accurately analyze the actual situation of the pine wilt disease identification area in the subsequent control and treatment process, and then give priority to the control of the pine wilt disease identification areas with high control needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart of the method for intelligent identification and control of pine wood nematode disease based on artificial intelligence;

[0029] Figure 2 This is the system module diagram of the intelligent identification and control system for pine wood nematode disease based on artificial intelligence;

[0030] Figure 3 Flowchart for marking features that are prominently affected by disease and features that are not affected by disease. DETAILED DESCRIPTION

[0031] Example 1: Reference Figure 1 The intelligent identification and control method of pine wood nematode disease based on artificial intelligence includes the following steps:

[0032] Step 1: Divide the target pine forest area into multiple pine forest control areas;

[0033] Step 2: After each pine forest control cycle, obtain aerial images of the pine forest areas of each pine forest control area, and then obtain the pine wood nematode disease index of each pine forest control area;

[0034] Step 3: Based on the comparison results of the pine wood nematode disease index and the pine wood nematode disease standard index, determine whether to mark the pine forest control area as a pine wood nematode disease identification area;

[0035] Step 4: Construct a pine forest simulation control model in the pine wood nematode disease identification area, and use the pine forest simulation control model to simulate control, thereby obtaining the pine wood nematode control index in the pine wood nematode disease identification area;

[0036] Step 5: Obtain the control emergency value of each pine wilt disease identification area, and sort all pine wilt disease identification areas in descending order according to the control emergency value;

[0037] Step 6: Control the pine wood nematode disease identification areas in sorted order.

[0038] The above method quantitatively analyzes the current disease situation, subsequent disease control and disease impact in the pine wood nematode disease identification area to ensure the reasonable control of pine wood nematode disease in pine forest areas.

[0039] Example 2: Reference Figures 2 to 3 The artificial intelligence-based intelligent identification and control system for pine wood nematode disease includes a pine forest area division and control module, a pine wood nematode disease identification module, a pine wood nematode disease management and analysis module, and a regional control sorting module.

[0040] Pine forest area division and control module: divide the target pine forest area into regions (the regions are divided according to the distribution of the pine forest and the characteristics of the topography and landforms. The specific division method is not the research and development focus of this application, so it will not be elaborated), and divide it into multiple pine forest control areas.

[0041] Pine wood nematode disease identification module: set the pine forest control cycle, and after each pine forest control cycle, obtain aerial images of the pine forest areas of each pine forest control area (the aerial images of the pine forest areas of the pine forest control areas can be obtained using drone remote sensing monitoring technology), and then obtain the pine wood nematode disease index of each pine forest control area, and set the pine wood nematode disease standard index (the pine wood nematode disease standard index is the system preset value). When the pine wood nematode disease index of the pine forest control area is greater than or equal to the pine wood nematode disease standard index, the pine forest control area is marked as a pine wood nematode disease identification area. When the pine wood nematode disease index of the pine forest control area is less than the pine wood nematode disease standard index, no processing is performed.

[0042] The method for obtaining the pine wood nematode disease index in the pine forest control area is as follows: various pine wood nematode disease identification features are extracted from the regional aerial images of the pine forest control area (the types of pine wood nematode disease identification features include resin secretion features, needle leaf color features, vector insect trace features, etc.), the real feature data of various pine wood nematode disease identification features are extracted, and the disease feature analysis model of various pine wood nematode disease identification features is obtained, and the real feature data of various pine wood nematode disease identification features are respectively input into the corresponding disease feature analysis model, and the pine wood nematode disease identification features of various pine wood nematode disease identification features are output. Characteristic value, set the pine wilt disease identification feature threshold (the pine wilt disease identification feature threshold is the system preset value), when the pine wilt disease identification feature value of the pine wilt disease identification feature is greater than or equal to the pine wilt disease identification feature threshold, the pine wilt disease identification feature is marked as a prominent disease-affected feature, when the pine wilt disease identification feature value of the pine wilt disease identification feature is less than the pine wilt disease identification feature threshold, the pine wilt disease identification feature is marked as a disease-irrelevant feature, the total number of disease-irrelevant features is marked as Lgst, and the total number of disease-irrelevant features is marked as Kdtp;

[0043] Match all prominent features of disease impact into a disease impact group, sum the pine wood nematode disease identification feature values ​​of the two prominent features of disease impact in the disease impact group, and obtain the disease impact additional value of the disease impact group. Set the disease impact additional limit (the disease impact additional limit is the system preset value). When the disease impact additional value of the disease impact group is greater than or equal to the disease impact additional limit, mark the disease image group as the disease impact additional group. When the disease impact additional value of the disease impact group is less than the disease impact additional limit, no processing is done. The total number of disease impact additional groups is marked as Zdes. Use the formula The pine wood nematode disease index Dise of the pine forest control area was obtained, where e1 is the disease impact prominence coefficient, e2 is the disease impact irrelevant coefficient, and e3 is the disease impact additional coefficient. The value of e1 is 1.41, the value of e2 is 0.97, and the value of e3 is 1.25.

[0044] Different types of pine wood nematode disease identification features correspond to different disease feature analysis models. For example, resin secretion features and needle color features correspond to two different disease feature analysis models respectively. The disease feature analysis models are all constructed based on neural network models. The difference in the construction of different disease feature analysis models lies in the difference in training data. This embodiment will disclose the construction process of the disease feature analysis model of resin secretion features: collect a group of real feature data of resin secretion features (if it is a disease feature analysis model of needle color features, collect a group of real feature data of needle color features), construct a neural network model, use the real feature data of resin secretion features as training data of the neural network model, assign pine wood nematode disease identification feature values ​​to each training data, and pine wood nematode The value range of the disease identification characteristic value is (2.1-5.9). The larger the pine wilt disease identification characteristic value, the more abnormal the resin secretion of the pine trees in the pine forest control area, and the smaller the pine wilt disease identification characteristic value, the more normal the resin secretion of the pine trees in the pine forest control area (if it is a disease characteristic analysis model of needle color characteristics, the larger the pine wilt disease identification characteristic value, the more abnormal the needle color of the pine trees in the pine forest control area, and the smaller the pine wilt disease identification characteristic value, the more normal the needle color of the pine trees in the pine forest control area). The training data is divided into 60% training set, 20% validation set and 20% test set. The neural network is iteratively trained on the training set, validation set and test set. After the training is completed, a disease characteristic analysis model of resin secretion characteristics is constructed.

[0045] Set up a pine forest area division and control module and a pine wood nematode disease identification module to carry out reasonable regional control of the pine forest area, and accurately identify the pine wood nematode disease in the pine forest area through in-depth analysis of disease characteristics in multiple dimensions.

[0046] Pine wilt disease control analysis module: Build a pine forest simulation control model for the pine wilt disease identification area, and use the pine forest simulation control model to perform simulated control to obtain the pine wilt disease control index Zaw in the pine wilt disease identification area.

[0047] The pine wood nematode control index Zaw in the pine wood nematode disease identification area is obtained as follows: set the simulation interval time to T (the length of T is set according to actual needs). During the simulation control process, obtain the pine wood nematode disease index of the pine forest simulation control model after each simulation interval T until the pine forest simulation control model completes the simulation control. Mark the total number of pine wood nematode disease indexes as Nukp, sort all pine wood nematode disease indexes in the order of acquisition time, sum the two adjacent pine wood nematode disease indexes after sorting, and obtain the pine wood nematode disease duration value. Set Set the pine wood nematode disease persistence threshold (the pine wood nematode disease persistence threshold is the system preset value). When the pine wood nematode disease persistence value is greater than or equal to the pine wood nematode disease persistence threshold, the disease duration times are increased once. When the pine wood nematode disease persistence value is less than the pine wood nematode disease persistence threshold, no processing is done. The disease duration times are marked as Bzdg. The formula Zaw=Nukp*ma+Bzdg*mb is used to obtain the pine wood nematode control index Zaw in the pine wood nematode disease identification area, where ma is the disease control coefficient, mb is the disease persistence coefficient, the value of ma is 0.43, and the value of mb is 0.35.

[0048] A pine forest simulation management and control model for the pine wilt disease identification area is constructed, specifically: the real feature data of various pine wilt disease identification characteristics in the pine wilt disease identification area are obtained, the expected feature data of various pine wilt disease identification characteristics are set simultaneously, the historical environmental data of the pine wilt disease identification area are obtained, simulation software such as SpeedTree and Unreal Engine are selected, a pine forest model is created in the simulation software based on the real feature data of various pine wilt disease identification characteristics in the pine wilt disease identification area, and the growth status, leaf color, branch shape and other parameters of each pine tree in the pine forest model are set, the corresponding environmental parameters are set in the pine forest model based on the historical environmental data of the pine wilt disease identification area, the management and control direction of the pine forest model is determined based on the expected feature data of various pine wilt disease identification characteristics, and then the pine forest simulation management and control model for the pine wilt disease identification area is constructed, and the pine forest simulation management and control model for the pine wilt disease identification area will perform simulation management and control based on the management and control direction.

[0049] Regional control sorting module: obtain the control emergency value of each pine wilt disease identification area, sort all pine wilt disease identification areas in descending order according to the control emergency value, and control the pine wilt disease identification areas in the sorted order (prioritize control of pine wilt disease identification areas with higher rankings, and refer to the simulation control method of the pine forest simulation control model for the specific control method).

[0050] The method for obtaining the control emergency value of the pine wilt disease identification area is as follows: obtain the pine wilt disease index Dise and the pine wilt management index Zaw of a pine wilt disease identification area, mark the remaining pine wilt disease identification areas (except the pine wilt disease identification area) as potential impact identification areas, measure the distance between the pine wilt disease identification area and each potential impact identification area (GIS measurement method can be used, using geographic information system software, the boundary data of the pine wilt disease identification area and the potential impact identification area are imported into the software, and the spatial analysis tool of the software is used to calculate the shortest distance between the pine wilt disease identification area and the potential impact identification area), obtain the regional distance between the pine wilt disease identification area and each potential impact identification area, set the regional standard distance (the regional standard distance is the system preset distance), when the regional distance is greater than or equal to the regional standard distance, no processing is performed, when the regional distance is less than the regional standard distance, the potential impact number is increased by one, and the potential impact number is marked as Rek, and the formula EMbt=2.6(Dise+Zaw) is used. Rek+1.38 The control emergency value EMbt of the pine wood nematode disease identification area is obtained.

[0051] A pine wilt disease control analysis module and a regional control ranking module are set up. By constructing a pine forest simulation control model for the pine wilt disease identification area, the actual situation of the pine wilt disease identification area in the subsequent control and management process is accurately analyzed. The current disease situation, subsequent disease control situation and disease impact of the pine wilt disease identification area are quantitatively analyzed, and the control of the pine wilt disease identification areas with high control needs is given priority to ensure the reasonable control of pine wilt disease in the pine forest area.

[0052] The above formulas are all dimensionless and numerical calculations are performed, and the preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0053] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0054] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0055] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0056] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0057] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0058] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0059] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. The intelligent identification and control system of pine wood nematode disease based on artificial intelligence is characterized by: It includes pine forest area division and control module, pine wood nematode disease identification module, pine wood nematode disease treatment and analysis module, and regional control and sorting module; The pine forest area division and control module is used to divide the target pine forest area into multiple pine forest control areas; The pine wood nematode disease identification module is used to set a pine forest management cycle. After each pine forest management cycle, an aerial image of the pine forest area of ​​each pine forest management area is obtained, and then the pine wood nematode disease index of each pine forest management area is obtained. Based on the comparison result of the pine wood nematode disease index and the pine wood nematode disease standard index, it is determined whether to mark the pine forest management area as a pine wood nematode disease identification area; The pine wood nematode disease control analysis module is used to construct a pine forest simulation control model for the pine wood nematode disease identification area, and the pine forest simulation control model performs simulation control to obtain the pine wood nematode disease control index for the pine wood nematode disease identification area; The regional control sorting module is used to obtain the control emergency value of each pine wilt disease identification area, sort all pine wilt disease identification areas in descending order according to the control emergency value, and control the pine wilt disease identification areas in the sorted order.

2. The artificial intelligence-based pine wood nematode disease intelligent identification and control system according to claim 1 is characterized in that: The pine wood nematode disease index in the pine forest control area is obtained as follows: determine the prominent disease impact features and the disease-irrelevant features in the pine forest control area, mark the total number of prominent disease impact features as Lgst, mark the total number of disease-irrelevant features as Kdtp, match all prominent disease impact features into a disease impact group, sum the pine wood nematode disease identification feature values ​​of the two prominent disease impact features in the disease impact group, obtain the disease impact added value of the disease impact group, set the disease impact added limit, and when the disease impact added value of the disease impact group is greater than or equal to the disease impact added limit, mark the disease image group as the disease impact added group, mark the total number of the disease impact added group as Zdes, and use the formula The pine wood nematode disease index Dise of the pine forest control area was obtained, where e1 is the disease impact prominence coefficient, e2 is the disease impact irrelevant coefficient, and e3 is the disease impact additional coefficient.

3. The artificial intelligence-based pine wood nematode disease intelligent identification and control system according to claim 2 is characterized in that: Determine the prominent features of disease impact and the features unrelated to disease impact in the pine forest control area, specifically: extract various pine wilt disease identification features from the regional aerial images of the pine forest control area, extract the real feature data of various pine wilt disease identification features, obtain the disease feature analysis model of various pine wilt disease identification features, input the real feature data of various pine wilt disease identification features into the corresponding disease feature analysis model respectively, output the pine wilt disease identification feature values ​​of various pine wilt disease identification features, set the pine wilt disease identification feature threshold, when the pine wilt disease identification feature value of the pine wilt disease identification feature is greater than or equal to the pine wilt disease identification feature threshold, mark the pine wilt disease identification feature as a prominent feature of disease impact, when the pine wilt disease identification feature value of the pine wilt disease identification feature is less than the pine wilt disease identification feature threshold, mark the pine wilt disease identification feature as a feature unrelated to disease impact.

4. The artificial intelligence-based pine wood nematode disease intelligent identification and control system according to claim 1, characterized in that: The method for obtaining the pine wood nematode control index Zaw in the pine wood nematode disease identification area is as follows: set the simulation interval time to T. During the simulation control process, obtain the pine wood nematode disease index of the pine forest simulation control model after each simulation interval time T until the pine forest simulation control model completes the simulation control. Mark the total number of pine wood nematode disease indices as Nukp, obtain the number of disease durations Bzdg, and use the formula Zaw = Nukp*ma+Bzdg*mb to obtain the pine wood nematode control index Zaw in the pine wood nematode disease identification area, where ma is the disease control coefficient and mb is the disease duration coefficient.

5. The artificial intelligence-based pine wood nematode disease intelligent identification and control system according to claim 4 is characterized in that: The method for obtaining the disease duration times Bzdg is as follows: sort all pine wood nematode disease indices in the order of acquisition time, sum the two adjacent pine wood nematode disease indices after sorting to obtain the pine wood nematode disease duration value, set the pine wood nematode disease duration threshold, and when the pine wood nematode disease duration value is greater than or equal to the pine wood nematode disease duration threshold, increase the disease duration times by one, and mark the disease duration times as Bzdg.

6. The artificial intelligence-based pine wood nematode disease intelligent identification and control system according to claim 1, characterized in that: The method for obtaining the control emergency value of the pine wood nematode disease identification area is as follows: obtain the pine wood nematode disease index Dise and the pine wood nematode control index Zaw of a pine wood nematode disease identification area, and simultaneously obtain the potential impact quantity Rek, and use the formula EMbt = 2.6 (Dise + Zaw) Rek+1.38 The control emergency value EMbt of the pine wood nematode disease identification area is obtained.

7. The artificial intelligence-based pine wood nematode disease intelligent identification and control system according to claim 1, characterized in that: The method for obtaining the potential impact quantity Rek is as follows: mark the remaining pine wilt disease identification areas as potential impact identification areas, measure the distance between the pine wilt disease identification areas and each potential impact identification area, obtain the regional distance between the pine wilt disease identification areas and each potential impact identification area, set the regional standard distance, and when the regional distance is less than the regional standard distance, increase the potential impact quantity by one and mark the potential impact quantity as Rek.

8. An artificial intelligence-based intelligent identification and control method for pine wood nematode disease, applied to an artificial intelligence-based intelligent identification and control system for pine wood nematode disease according to any one of claims 1 to 7, characterized in that: The steps include: Step 1: Divide the target pine forest area into multiple pine forest control areas; Step 2: After each pine forest control cycle, obtain aerial images of the pine forest areas of each pine forest control area, and then obtain the pine wood nematode disease index of each pine forest control area; Step 3: Based on the comparison results of the pine wood nematode disease index and the pine wood nematode disease standard index, determine whether to mark the pine forest control area as a pine wood nematode disease identification area; Step 4: Construct a pine forest simulation control model in the pine wood nematode disease identification area, and use the pine forest simulation control model to simulate control, thereby obtaining the pine wood nematode control index in the pine wood nematode disease identification area; Step 5: Obtain the control emergency value of each pine wilt disease identification area, and sort all pine wilt disease identification areas in descending order according to the control emergency value; Step 6: Control the pine wood nematode disease identification areas in sorted order.

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