AI-based agricultural pest and disease identification method

Abstract

The invention discloses an AI-based agricultural disease and insect pest identification method. The disease and insect pest identification method comprises the following specific steps: S100, transmitting image data of various diseases and insect pests to a disease and insect pest standard database of identification equipment; s200, carrying out image acquisition on the crops by using identification equipment, carrying out feature extraction on the shot image by using the identification equipment, and processing the extracted image data to enable the image data type to be the same as the image type in the pest and disease damage standard database; s300, comparing the acquired image features with data in a pest and disease damage standard database to determine the type of pest and disease damage; and S400, transmitting the determined pest and disease damage type to terminal equipment in a wireless transmission mode. According to the method, crops are monitored in real time through recognition equipment, the recognition equipment can collect images of the crops around the recognition equipment, and the collected data are compared with data in a database.

Description

Technical Field

[0001] The present invention relates to the technical field of pest and disease identification, and specifically to an agricultural pest and disease identification method based on AI. Background Art

[0002] During the growth process of crops, some diseases and insect pests often occur, which can cause abnormal growth of crops and even death of crops in severe cases. Therefore, when crops are affected by diseases and insect pests, corresponding measures need to be taken in time to ensure the healthy growth of crops.

[0003] There are some methods and devices for identifying pests and diseases on the market. For example, the Chinese patent application publication number CN117475304A discloses an artificial intelligence-based agricultural pest and disease identification method, which obtains an image of the infected part of the crop; extracts image features from the image of the infected part of the crop to obtain a multi-scale and multi-order feature matrix of the infected part; and determines the type of pest and disease based on the multi-scale and multi-order feature matrix of the infected part. In this way, analysis can be performed based on the image of the infected part of the crop to intelligently determine the type of pest and disease.

[0004] The artificial intelligence-based pest identification method provided by the above patent mainly relies on intelligent analysis of the infected parts of crops to determine the type of pest identification. However, the existing pest identification method only mentions obtaining images of the infected parts of crops, and there is no perfect image acquisition method. If the image needs to be manually collected, it is still troublesome, and it cannot achieve the purpose of real-time monitoring of crops, nor can it notify staff in time after pests and diseases appear in crops. Summary of the invention

[0005] The purpose of the present invention is to provide an AI-based agricultural pest identification method, aiming to improve the problem that the existing pest identification method does not have a perfect image acquisition method, the images need to be collected manually, which is troublesome, and it is not possible to monitor crops in real time, nor to notify staff in time after pests and diseases appear in crops.

[0006] The present invention is achieved in that:

[0007] An AI-based agricultural pest identification method, the specific steps of the pest identification method are as follows:

[0008] S100, transmitting image data of various pests and diseases to a pest and disease standard database of a recognition device;

[0009] S200, using a recognition device to collect images of crops, and the recognition device extracts features from the captured images, and processes the extracted image data so that the image data type is the same as the image type in the pest and disease standard database;

[0010] S300, comparing the collected image features with the image data in the pest and disease standard database to determine the type of pest and disease;

[0011] S400: Transmit the determined pest type to a terminal device by wireless transmission.

[0012] Preferably, the identification device in step S100 includes an industrial camera and a body, a mounting base is installed at the bottom of the industrial camera, the front end of the industrial camera is tilted downward, the bottom end of the mounting base is rotatably connected to a support rod, the bottom end of the support rod is installed on the body, and the support rod and the body are detachably connected, a motor is installed on the top of the side of the support rod, the output end of the motor is connected to the mounting base, and the motor is used to drive the industrial camera and the mounting base to adjust the angle; the body is wirelessly connected to a terminal.

[0013] Preferably, a shooting head is provided at the front end of the industrial camera, a stud is provided at the rear end of the industrial camera, a wire is provided at the rear end of the industrial camera, a plug connector is provided at the end of the wire, and the plug connector is plug-connected to the body.

[0014] Preferably, a diagonal support plate is obliquely provided at the rear end of the mounting seat, a mounting plate is provided at the top end of the diagonal support plate, a mounting groove is provided in the middle of the mounting plate, the mounting groove is threadedly connected to the stud, a through hole is provided in the middle of the mounting plate, the through hole is used for wires and plug connectors to pass through the mounting plate; a connecting shaft is provided at the bottom end of the mounting seat, and a transmission gear is connected to the connecting shaft.

[0015] Preferably, a bearing is provided at the top end of the support rod, and a mounting platform is provided on the side of the support rod, fixing grooves are symmetrically provided on both sides of the mounting platform, and the mounting platform cooperates with the mounting grooves to install the motor, and a fixing plate is provided at the bottom end of the support rod, and a plurality of fixing holes are evenly provided on the edge of the fixing plate.

[0016] Preferably, a driving gear is provided at the output end of the motor, and a fixing plate is provided on one side of the motor that is attached to the mounting platform, a plurality of positioning holes are symmetrically provided on both sides of the fixing plate, a connecting line is provided at the bottom end of the motor, a connecting plug is provided at the bottom end of the connecting line, and the connecting plug is connected to the machine body.

[0017] Preferably, a control panel is provided on the front of the body, and a supporting platform is provided on the top of the body, communication slots are provided on the top and sides of the body, and the communication slots are used to connect the body with the motor and the industrial camera, and supporting feet are symmetrically provided on both sides of the body, and a base plate is provided on the outer side of the supporting feet, and a plurality of bolt holes are provided on the base plate; an image feature extraction module, an image feature processing module, an image feature comparison module, a standard database of pests and diseases, and a disease type determination module are integrated inside the body, and the image feature extraction module, the image feature processing module, the image feature comparison module, the standard database of pests and diseases, and the disease type determination module are connected in sequence.

[0018] Preferably, the specific steps of image acquisition in step S200 are as follows:

[0019] S210, installing the entire identification device in the crops in the farmland, and raising the entire device by a supporting device so that the identification device is higher than the crops;

[0020] S220, the motor timing is set by the machine body to drive the industrial camera to rotate 360° forward and 360° reverse, so as to ensure that the industrial camera can align with the surrounding crops for shooting;

[0021] S230, when the motor drives the industrial camera to rotate, the industrial camera will take pictures of the surrounding crops at a high frequency;

[0022] S240, the industrial camera feeds back the taken photos to the machine body, and the machine body further processes the taken photos.

[0023] Preferably, the algorithm flow of feature comparison in step S300 is as follows:

[0024] S310, considering the pixels of the image as a matrix, where the positions corresponding to each pixel are different;

[0025] S320, compare the collected image with the image data in the pest and disease standard database on a large scale, find the pest and disease image data that is most similar to the collected image for comparison; then compare the pixel points of the collected image with the pixel points of the image in the pest and disease standard database, and determine the number of identical pixels d 1 , and the number of pixels in the image in the pest and disease standard database is d 2 ;

[0026] S330, the same number of pixels d 1 and the number of pixels in the pest and disease standard database is d 2 Substituting into the formula: Where Q is the similarity and K is the similarity coefficient;

[0027] S340, determining whether the similarity value Q is greater than 0.5, if the similarity Q is less than 0.5, determining that the number of pixels in the image of the crop without pests and diseases is d 2 The corresponding pests and diseases; then the recognition device will continue to collect images for comparison to achieve the purpose of real-time monitoring of crops; if the similarity Q is greater than 0.5, the pest and disease type of the crop is determined to be the number of pixels in the pest and disease image is d 2 The corresponding pests and diseases.

[0028] Preferably, when transmitting the image data of various types of pests and diseases to the pest and disease standard database in step S100, the solutions for the corresponding pests and diseases are also transmitted to the pest and disease standard database, and a pest treatment solution extraction module is integrated inside the body, which is used to extract the corresponding pest treatment solution from the pest and disease standard database after determining the pest and disease type, and transmit the pest treatment solution together with the disease data to the terminal device.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] 1. The present invention monitors crops in real time through identification equipment, and the identification equipment will collect images of crops around it and compare the collected data with the data in the database. When the result of comparing the collected data with the database meets the set value, the identification equipment will transmit the message of the pests and diseases suffered by the crops to the terminal to remind the staff to deal with the pests and diseases in time.

[0031] 2. The present invention can not only identify pests and diseases, but also provide workers with solutions according to the types of pests and diseases, so as to ensure that inexperienced workers can also quickly take corresponding measures according to the solutions provided by the identification equipment.

[0032] 3. The camera of the identification device of the present invention can be rotated 360° driven by a motor, so that the identification device can monitor and identify crops within a certain range around it. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is a flow chart of the pest identification method of the present invention;

[0034] Figure 2 It is an algorithm diagram for pest identification of the present invention;

[0035] Figure 3 It is a schematic diagram of the structure of the identification device of the present invention;

[0036] Figure 4 It is a structural schematic diagram of the industrial camera of the present invention;

[0037] Figure 5It is a schematic diagram of the structure of the mounting base of the present invention;

[0038] Figure 6 It is a structural schematic diagram of the support rod of the present invention;

[0039] Figure 7 It is a structural schematic diagram of the motor of the present invention;

[0040] Figure 8 It is a structural schematic diagram of the body of the present invention;

[0041] Fig. 9 It is a structural block diagram of the interior of the machine body of the present invention.

[0042] In the figure: 1. industrial camera; 11. stud; 12. wire; 13. plug connector; 14. camera head; 2. mounting base; 21. diagonal support plate; 22. mounting plate; 23. mounting slot; 24. through hole; 25. connecting shaft; 26. transmission gear; 3. support rod; 31. bearing; 32. mounting table; 33. fixing slot; 34. fixing plate; 35. fixing hole; 4. motor; 41. driving gear; 42. fixing plate; 43. positioning hole; 44. communication line; 45. communication plug; 5. body; 51. control panel; 52. support platform; 53. communication slot; 54. support foot; 55. bottom plate; 56. bolt hole. DETAILED DESCRIPTION

[0043] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0044] The following is a further description with reference to the accompanying drawings and specific embodiments:

[0045] Example 1

[0046] like Figure 3As shown, a pest and disease identification device includes an industrial camera 1 and a body 5. A mounting seat 2 is installed at the bottom of the industrial camera 1. The front end of the industrial camera 1 is tilted downward. The mounting seat 2 is used to install the industrial camera 1 to ensure the stability of the industrial camera 1, and it is also convenient to set the industrial camera 1 tilted downward. The bottom end of the mounting seat 2 is rotatably connected to a support rod 3, and the bottom end of the support rod 3 is installed on the body 5. The support rod 3 is used to support the industrial camera 1 to ensure that the industrial camera 1 can rotate. The support rod 3 and the body 5 are detachably connected. This structure facilitates the removal and replacement of support rods 3 of different specifications. A motor 4 is installed on the top of the side of the support rod 3. The output end of the motor 4 is connected to the mounting seat 2. The motor 4 is used to drive the industrial camera 1 and the mounting seat 2 to adjust the angle; the body 5 is wirelessly connected to a terminal, and the terminal is used to receive data sent by the body 5.

[0047] like Figure 4 As shown, the front end of the industrial camera 1 is provided with a camera head 14, which is used to take images of crops. The rear end of the industrial camera 1 is provided with a stud 11, which is used to thread the industrial camera 1 and the mounting seat 2, so as to facilitate the disassembly and use of the industrial camera 1. The rear end of the industrial camera 1 is provided with a wire 12, and the end of the wire 12 is provided with a plug connector 13, which is plug-connected to the body 5; this structure is convenient for the industrial camera 1 to be connected to the body 5, and is convenient for the power on and use of the industrial camera 1.

[0048] like Figure 5 As shown, the rear end of the mounting seat 2 is inclined with a diagonal support plate 21, and the top of the diagonal support plate 21 is provided with a mounting plate 22. The diagonal support plate 21 cooperates with the mounting plate 22 to install the industrial camera 1, which is convenient for the disassembly and assembly of the industrial camera 1 and also convenient for the flexible use of the industrial camera 1. A mounting groove 23 is provided in the middle of the mounting plate 22, and the mounting groove 23 is threadedly connected to the stud 11. The mounting groove 23 is convenient for the industrial camera 1 to be threadedly connected to the mounting plate 22. A through hole 24 is provided in the middle of the mounting plate 22, and the through hole 24 is used for the wire 12 and the plug connector 13 to pass through the mounting plate 22; a connecting shaft 25 is provided at the bottom end of the mounting seat 2, and a transmission gear 26 is connected to the connecting shaft 25; the connecting shaft 25 is convenient for the mounting seat 2 to be rotatably connected to the support rod 3, and the transmission gear 26 is convenient for the output end of the motor 4 to drive the mounting seat 2 to rotate.

[0049] like Figure 6 As shown, a bearing 31 is provided at the top of the support rod 3, and the bearing 31 is convenient for the support rod 3 to be rotatably connected to the mounting seat 2. A mounting platform 32 is provided on the side of the support rod 3, and fixing grooves 33 are symmetrically provided on both sides of the mounting platform 32. The mounting platform 32 cooperates with the mounting groove 23 to install the motor 4, and a fixing plate 34 is provided at the bottom of the support rod 3, and a plurality of fixing holes 35 are evenly provided on the edge of the fixing plate 34; the fixing plate 34 and the fixing holes 35 are convenient for fixing the support rod 3 on the body 5 to ensure that the support rod 3 is sufficiently stable.

[0050] like Figure 7 As shown, the output end of the motor 4 is provided with a driving gear 41, and the driving gear 41 is convenient for the output end of the motor 4 to drive the mounting seat 2 and the industrial camera 1 to rotate. A fixing plate 42 is provided on one side of the motor 4 that is in contact with the mounting platform 32, and a plurality of positioning holes 43 are symmetrically provided on both sides of the fixing plate 42. The cooperation between the fixing plate 42 and the fixing holes 35 is convenient for fixing the position of the machine body 5 by bolts. A connecting line 44 is provided at the bottom end of the motor 4, and a connecting plug 45 is provided at the bottom end of the connecting line 44. The connecting plug 45 is connected to the machine body 5. The connecting line 44 and the connecting plug 45 are convenient for connecting the motor 4 with the machine body 5, so that the machine body 5 can control the operation of the motor 4.

[0051] like Figure 8 and Fig. 9 As shown, the front of the machine body 5 is provided with a control panel 51, and the control panel 51 is convenient for controlling the entire device. A support platform 52 is provided at the top of the machine body 5, and the support platform 52 is convenient for installing the support rod 3. A connecting slot 53 is provided on the top and side of the machine body 5, and the connecting slot 53 is used to connect the machine body 5 with the motor 4 and the industrial camera 1. Support feet 54 are symmetrically provided on both sides of the machine body 5, and the support feet are used to support the machine body 5. A bottom plate 55 is provided on the outer side of the support foot 54, and a plurality of bolt holes 56 are provided on the bottom plate 55; the cooperation between the bottom plate 55 and the bolt holes 56 is convenient for fixing the position of the machine body 5 by bolts. The machine body 5 is integrated with an image feature extraction module, an image feature processing module, an image feature comparison module, a pest and disease standard database, and a disease type determination module, and the image feature extraction module, the image feature processing module, the image feature comparison module, the pest and disease standard database, and the disease type determination module are connected in sequence; the image feature extraction module is convenient for extracting image data, and the image feature processing module is used to process images. The image feature comparison module is used to compare images, the pest and disease standard database is used to store pest and disease data, and the disease type determination module is convenient for determining the disease type of crops.

[0052] Working principle: When in use, the entire device is installed between the crops in the farmland, and the entire device is lifted up by the supporting device so that the device is higher than the crops. Then the entire device is powered on, and then the motor 4 is set through the body 5 to drive the industrial camera 1 to rotate forward and flip at a fixed time. The industrial camera 1 continuously takes pictures of the crops to achieve the purpose of image acquisition, and also achieves the purpose of real-time monitoring of the crops.

[0053] Example 2

[0054] like Figure 1 and Figure 2 As shown, an AI-based agricultural pest identification method uses a pest identification device provided in Example 1. The specific steps of the pest identification method are as follows:

[0055] S100, transmitting image data of various types of pests and diseases to a pest and disease standard database of a recognition device, so as to facilitate comparison of subsequently collected image data with data in the pest and disease standard database.

[0056] S200. Capture images of crops using an identification device, and the identification device extracts features from the captured images and processes the extracted image data so that the image data type is the same as the image type in a pest and disease standard database; this makes it convenient to compare the captured images with the images in the database, thereby improving the accuracy of pest and disease identification.

[0057] S210, installing the entire recognition device in the crops in the farmland, and raising the entire device through a supporting device so that the recognition device is higher than the crops; this can prevent the crops from blocking the industrial camera, thereby limiting the range of images that can be captured by the industrial camera.

[0058] S220, the motor timing is set by the machine body to drive the industrial camera to rotate 360° forward and 360° reverse, ensuring that the industrial camera can align with the surrounding crops for shooting; this structure allows the motor to rotate forward and reverse effectively to avoid the problem of wire entanglement.

[0059] S230, when the motor drives the industrial camera to rotate, the industrial camera will take pictures of the surrounding crops at a high frequency;

[0060] S240, the industrial camera feeds back the taken photos to the machine body, and the machine body further processes the taken photos.

[0061] S300, comparing the collected image features with the data in the pest and disease standard database to determine the type of the pest and disease;

[0062] S310, considering the pixels of the image as a matrix, where the positions corresponding to each pixel are different;

[0063] S320, compare the collected image with the image data in the pest and disease standard database on a large scale, find the pest and disease image data that is most similar to the collected image for comparison; then compare the pixel points of the collected image with the pixel points of the image in the pest and disease standard database, and determine the number of identical pixels d 1 , and the number of pixels in the image in the pest and disease standard database is d 2 ;

[0064] S330, the same number of pixels d 1 and the number of pixels in the pest and disease standard database is d 2 Substituting into the formula: Where Q is the similarity and K is the similarity coefficient;

[0065] S340, determining whether the similarity value Q is greater than 0.5, if the similarity Q is less than 0.5, determining that the number of pixels in the image of the crop without pests and diseases is d 2 The corresponding pests and diseases; then the recognition device will continue to collect images for comparison to achieve the purpose of real-time monitoring of crops; if the similarity Q is greater than 0.5, the pest and disease type of the crop is determined to be the number of pixels in the pest and disease image is d 2 The corresponding pests and diseases.

[0066] S400: Transmit the determined pest type to a terminal device by wireless transmission.

[0067] Example 3

[0068] like Figure 1 and Figure 2 As shown, an AI-based agricultural pest identification method uses a pest identification device provided in Example 1. The specific steps of the pest identification method are as follows:

[0069] S100, transmitting the image data of various types of pests and diseases to the pest and disease standard database of the identification device; so as to facilitate the comparison of the image data collected subsequently with the data in the pest and disease standard database. When transmitting the image data of various types of pests and diseases to the pest and disease standard database, the solutions for the corresponding pests and diseases are also transmitted to the pest and disease standard database, and a pest and disease treatment solution extraction module is integrated inside the machine body, which is used to extract the corresponding pest and disease treatment solution from the pest and disease standard database after determining the pest and disease type, and transmit the pest and disease treatment solution together with the disease data to the terminal device.

[0070] S200. Capture images of crops using an identification device, and the identification device extracts features from the captured images and processes the extracted image data so that the image data type is the same as the image type in a pest and disease standard database; this makes it convenient to compare the captured images with the images in the database, thereby improving the accuracy of pest and disease identification.

[0071] S210, installing the entire recognition device in the crops in the farmland, and raising the entire device through a supporting device so that the recognition device is higher than the crops; this can prevent the crops from blocking the industrial camera, thereby limiting the range of images that can be captured by the industrial camera.

[0072] S220, the motor timing is set by the machine body to drive the industrial camera to rotate 360° forward and 360° reverse, so as to ensure that the industrial camera can align with the surrounding crops for shooting;

[0073] S230, when the motor drives the industrial camera to rotate, the industrial camera will take pictures of the surrounding crops at a high frequency;

[0074] S240, the industrial camera feeds back the taken photos to the machine body, and the machine body further processes the taken photos.

[0075] S300, comparing the collected image features with the data in the pest and disease standard database to determine the type of the pest and disease;

[0076] S310, considering the pixels of the image as a matrix, where the positions corresponding to each pixel are different;

[0077] S320, compare the collected image with the image data in the pest and disease standard database on a large scale, find the pest and disease image data that is most similar to the collected image for comparison; then compare the pixel points of the collected image with the pixel points of the image in the pest and disease standard database, and determine the number of identical pixels d 1 , and the number of pixels in the image in the pest and disease standard database is d 2 ;

[0078] S330, the same number of pixels d 1 and the number of pixels in the pest and disease standard database is d 2 Substituting into the formula: Where Q is the similarity and K is the similarity coefficient;

[0079] S340, determining whether the similarity value Q is greater than 0.5, if the similarity Q is less than 0.5, determining that the number of pixels in the image of the crop without pests and diseases is d 2 The corresponding pests and diseases; then the recognition device will continue to collect images for comparison to achieve the purpose of real-time monitoring of crops; if the similarity Q is greater than 0.5, the pest and disease type of the crop is determined to be the number of pixels in the pest and disease image is d 2 The corresponding pests and diseases.

[0080] S400: Transmit the determined pest type and the treatment plan for the pest to the terminal device by wireless transmission.

[0081] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An AI-based agricultural pest identification method, characterized in that: The specific steps include: S100, transmitting image data of various pests and diseases to a pest and disease standard database of a recognition device; S200, using recognition equipment to collect images of crops, extract features from the captured images, and process the extracted image data so that the image data type is the same as the image type in the pest and disease standard database; S300, comparing the collected image features with the image data in the pest and disease standard database to determine the type of pest and disease; S400: Transmit the determined pest type to a terminal device by wireless transmission.

2. The AI-based agricultural pest identification method according to claim 1, characterized in that: The identification device in step S100 comprises an industrial camera (1) and a body (5), wherein a mounting base (2) is installed at the bottom of the industrial camera (1), the front end of the industrial camera (1) is tilted downward, the bottom end of the mounting base (2) is rotatably connected to a support rod (3), the bottom end of the support rod (3) is installed on the body (5), and the support rod (3) and the body (5) are detachably connected, a motor (4) is installed at the top of the side of the support rod (3), the output end of the motor (4) is connected to the mounting base (2), and the motor (4) is used to drive the industrial camera (1) and the mounting base (2) to adjust the rotation angle; the body (5) is wirelessly connected to a terminal.

3. The AI-based agricultural pest identification method according to claim 2, characterized in that: The front end of the industrial camera (1) is provided with a shooting head (14), the rear end of the industrial camera (1) is provided with a stud (11), the rear end of the industrial camera (1) is provided with a wire (12), the end of the wire (12) is provided with a plug connector (13), and the plug connector (13) is plug-connected to the body (5).

4. The AI-based agricultural pest identification method according to claim 3, characterized in that: The rear end of the mounting seat (2) is provided with an inclined support plate (21), the top of the inclined support plate (21) is provided with a mounting plate (22), the middle of the mounting plate (22) is provided with a mounting groove (23), the mounting groove (23) is threadedly connected to the stud (11), the middle of the mounting plate (22) is provided with a through hole (24), the through hole (24) is used for the wire (12) and the plug connector (13) to pass through the mounting plate (22); the bottom end of the mounting seat (2) is provided with a connecting shaft (25), and the connecting shaft (25) is connected to a transmission gear (26).

5. The AI-based agricultural pest identification method according to claim 2, characterized in that: A bearing (31) is provided at the top end of the support rod (3), and a mounting platform (32) is provided on the side of the support rod (3). Fixing grooves (33) are symmetrically provided on both sides of the mounting platform (32). The mounting platform (32) cooperates with the mounting grooves (23) to install the motor (4). A fixing plate (34) is provided at the bottom end of the support rod (3), and a plurality of fixing holes (35) are evenly provided on the edge of the fixing plate (34).

6. The AI-based agricultural pest identification method according to claim 5, characterized in that: The output end of the motor (4) is provided with a driving gear (41), and a fixing plate (42) is provided on one side of the motor (4) that is in contact with the mounting platform (32), and a plurality of positioning holes (43) are symmetrically provided on both sides of the fixing plate (42). A connecting line (44) is provided at the bottom end of the motor (4), and a connecting plug (45) is provided at the bottom end of the connecting line (44), and the connecting plug (45) is connected to the machine body (5).

7. The AI-based agricultural pest identification method according to claim 2, characterized in that: The front of the machine body (5) is provided with a control panel (51), and the top of the machine body (5) is provided with a support platform (52). The top and side of the machine body (5) are provided with communication slots (53), and the communication slots (53) are used to connect the machine body (5) with the motor (4) and the industrial camera (1). The two sides of the machine body (5) are symmetrically provided with support feet (54), and the outer side of the support feet (54) is provided with a bottom plate (55), and the bottom plate (55) is provided with a plurality of bolt holes (56); the machine body (5) is internally integrated with an image feature extraction module, an image feature processing module, an image feature comparison module, a pest and disease standard database, and a pest and disease type determination module, and the image feature extraction module, the image feature processing module, the image feature comparison module, the pest and disease standard database, and the pest and disease type determination module are sequentially connected.

8. The AI-based agricultural pest identification method according to claim 2, characterized in that: The specific steps of image acquisition in step S200 are as follows: S210, installing the entire identification device in the crops in the farmland, and raising the entire device by a supporting device so that the identification device is higher than the crops; S220, the motor timing is set by the machine body to drive the industrial camera to rotate 360° forward and 360° reverse, so as to ensure that the industrial camera can align with the surrounding crops for shooting; S230, when the motor drives the industrial camera to rotate, the industrial camera will take pictures of the surrounding crops at a high frequency; S240, the industrial camera feeds back the taken photos to the machine body, and the machine body further processes the taken photos.

9. The AI-based agricultural pest identification method according to claim 1, characterized in that: The algorithm flow of feature comparison in step S300 is as follows: S310, considering the pixels of the image as a matrix, where the positions corresponding to each pixel are different; S320, compare the collected image with the image data in the pest and disease standard database on a large scale, find the pest and disease image data that is most similar to the collected image for comparison; then compare the pixel points of the collected image with the pixel points of the image in the pest and disease standard database, determine the number of identical pixel points d1, and the number of pixel points of the image in the pest and disease standard database is d2; S330, substitute the same number of pixels d1 and the number of pixels d2 in the pest and disease standard database into the formula: Where Q is the similarity and K is the similarity coefficient; S340, determine whether the similarity value Q is greater than 0.

5. If the similarity Q is less than 0.5, it is determined that the crop does not have the pests and diseases corresponding to the number of pixels in the pest and disease image d2; then the recognition device will continue to collect images for comparison to achieve the purpose of real-time monitoring of crops; if the similarity Q is greater than 0.5, it is determined that the type of pest and disease obtained by the crop is the pest and disease corresponding to the number of pixels in the pest and disease image d2.

10. The AI-based agricultural pest identification method according to claim 2, characterized in that: When transmitting the image data of various types of pests and diseases to the pest and disease standard database in step S100, the solutions for the corresponding pests and diseases are also transmitted to the pest and disease standard database, and a pest and disease treatment solution extraction module is integrated inside the body, which is used to extract the corresponding pest and disease treatment solution from the pest and disease standard database after determining the pest and disease type, and transmit the pest and disease treatment solution together with the disease data to the terminal device.

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