Plant disease and pest detection equipment and method based on unmanned aerial vehicle high-altitude texture imaging

Abstract

The invention discloses a plant disease and insect pest detection device and method based on unmanned aerial vehicle high-altitude texture imaging, and belongs to the technical field of intelligent agriculture. The plant disease and insect pest detection device comprises an unmanned aerial vehicle body, a holder, a high-altitude texture camera and a small host; the high-altitude texture camera is arranged below the holder, the holder is arranged below the small host, the small host is arranged below the unmanned aerial vehicle main body, the unmanned aerial vehicle main body is provided with a front high-definition camera, a battery and an illumination sensor, and the high-altitude texture camera is wirelessly provided with a handle and a flight remote controller through Bluetooth; according to the plant disease and insect pest detection method, high-precision recognition of disease and insect pests is achieved on the basis of high-altitude texture feature fusion in combination with an environment perception driven data enhancement module, a self-adaptive multi-scale image convolution feature extraction and fusion module and a disease and insect pest area attention fusion module. The technology effectively supports precise detection of diseases and insect pests in agricultural production, and provides a scientific basis for agricultural management and pesticide application.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart agriculture, and in particular relates to plant disease and insect pest detection equipment and methods based on unmanned aerial vehicle high-altitude texture imaging. Background Art

[0002] Traditional imaging technology has many limitations in agriculture and other fields, including limited field of view and coverage, reliance on manual operation, high cost, and difficulty in dynamic monitoring and multi-angle observation. In particular, the limitation of field of view and coverage has become a core problem, hindering effective data collection and accurate analysis. However, the emergence of drone high-altitude texture imaging technology has provided a new way to solve these problems. This innovative image acquisition and processing method can accurately obtain various texture information on the surface of an object. Moreover, high-quality high-altitude texture images can greatly improve the recognition ability of deep learning models for objects.

[0003] In the agricultural field, the use of drone high-altitude texture imaging technology can obtain rich texture information on the surface of vegetation from a high-altitude bird's-eye view, including important features such as the morphology, texture, and color of plant leaves. By obtaining this texture information, it can provide important data support and decision-making basis for agricultural production. Subsequently, using image processing and deep learning algorithms, we can accurately analyze the collected data and accurately identify and locate possible pest and disease problems of plants. This comprehensive coverage of data helps to detect plant health problems in a timely manner, take corresponding measures, reduce the loss of crops to pests and diseases, thereby improving production efficiency and quality, and can also promote the development of macroscopic phenotyping in plant pest and disease research. Therefore, the research of this technology in the field of smart agriculture is of great significance, and can be used in many aspects such as plant health monitoring, pest and disease monitoring and prevention, precision fertilization and irrigation, and farmland planning and management.

[0004] The realization of UAV high-altitude texture imaging and plant pest and disease identification methods mainly relies on the synergy of a series of equipment, including UAVs, gimbals, small hosts, touch screens, accessory interfaces, and high-definition cameras. The identification of pests and diseases is completed through a multi-scale feature high-altitude texture feature fusion algorithm. Among them, the parameters of high-definition cameras and lenses are crucial for the acquisition of high-altitude texture images, and the complex physical imaging principles need to be analyzed. In addition, the identification of pests and diseases in high-altitude texture images is also difficult. Therefore, there is an urgent need for a plant pest and disease detection device and method based on UAV high-altitude texture imaging to overcome the difficulties existing in the above-mentioned technologies, so as to better identify plant pests and diseases in the field and provide more accurate management and decision-making support for agricultural production. Summary of the invention

[0005] The purpose of the present invention is to provide a plant pest and disease detection device and method based on drone high-altitude texture imaging, and to achieve accurate detection of pests and diseases through a plant pest and disease identification method fused with high-altitude texture features. Through the device of the present invention, high-definition texture images of plant surfaces can be taken at high altitudes, and data enhancement, adaptive multi-scale feature extraction and fusion, and pest and disease area attention fusion can be used to improve the accuracy and real-time performance of detection.

[0006] To achieve the above-mentioned objectives, the present invention provides a plant disease and pest detection device based on unmanned aerial vehicle high-altitude texture imaging, comprising an unmanned aerial vehicle body, a gimbal, a high-altitude texture camera and a small host; the high-altitude texture camera is arranged below the gimbal, the gimbal is arranged below the small host, the small host is arranged below the unmanned aerial vehicle body, the unmanned aerial vehicle body is configured with a front high-definition camera, a battery, and a light sensor, and the high-altitude texture camera is wirelessly configured with a handle and a flight remote control via Bluetooth.

[0007] Preferably, the high-altitude texture camera is a combination of a 61-megapixel camera and a lens with a focal length of 70 mm to 200 mm, and the 61-megapixel camera and the lens with a focal length of 70 mm to 200 mm are determined by the Gaussian imaging formula, and the specific expression is as follows:

[0008]

[0009] Among them, u is the object distance, v is the image distance, and f is the focal length.

[0010] The present invention also provides a method for detecting plant diseases and insect pests based on high-altitude texture imaging of unmanned aerial vehicles, comprising the following steps:

[0011] Step 1: Train a plant disease and insect pest recognition model;

[0012] Step 2: Deploy the trained plant disease and insect pest recognition model to a small host;

[0013] Step 3: Take images through a high-altitude texture camera, and transmit the captured image data to the small host computer deployed with the plant disease and insect pest identification model in step 2 for detection.

[0014] Preferably, the specific process of training the plant pest and disease recognition model in step 1 is as follows:

[0015] Step 101: adjusting the brightness, contrast and saturation of the image according to the real-time light intensity, collecting plant disease and insect pest image data, and performing data preprocessing;

[0016] Step 102: input the preprocessed data obtained in step 101 into the ResNet-101 model, and train the ResNet-101 model to learn and extract features of plant pests and diseases texture, shape and structure from the plant pests and diseases image, and convert the extracted features into a high-dimensional feature vector f0;

[0017] Step 103: Input the high-dimensional feature vector f0 extracted in step 102 into the 3×3 convolution layer and the normalization layer respectively to obtain an attention map A focusing on the adaptive nearest neighbor K. opt ;

[0018] Step 104: Input the high-dimensional feature vector f0 extracted in step 102 into the graph convolution layer and the global average pooling layer whose nearest neighbor set is {K1=3, K2=5, K3=7} to obtain a multi-scale feature set {f1,f2,f3,f a};

[0019] Step 105: Through the multi-scale feature set {f1,f2,f3,f a} and the attention map A of the adaptive nearest neighbor K opt Fusion is performed to obtain a multi-scale fusion feature map f m ;

[0020] Step 106: The multi-scale fusion feature map f obtained in step 105 is m After 3×3 convolutional layers, fully connected layers and activation layers, and then connected to f m Add itself to obtain the spatial-channel feature enhancement map F;

[0021] Step 107: Map the spatial-channel feature enhancement map F obtained in step 106 to the category space through a fully connected layer, and use the softmax classifier to calculate the probability distribution of each category, and output the final classification result.

[0022] Preferably, the specific expression of the illumination perception enhancement in step 101 is as follows:

[0023] I aug =AL(I,LI);

[0024] Among them, I is the plant disease image, I aug is the enhanced image, LI is the real-time illumination intensity, and AL(·) is the adaptive function that adjusts the image brightness according to the illumination intensity.

[0025] Preferably, the specific expression of the feature extraction process in step 102 is as follows:

[0026] f0=R(I aug ; φ);

[0027] Among them, φ is the parameter of the ResNet-101 model, R represents the ResNet-101 model, and f0 is the output feature vector.

[0028] Preferably, the specific expression of the attention map of the adaptive nearest neighbor K in step 103 is as follows:

[0029] A opt =Conv(Norm(Conv(f0)));

[0030] Among them, Conv(·) represents the convolution operation on image I, and Norm(·) is the normalization operation, which is used to standardize the feature map after convolution to ensure its numerical stability during training.

[0031] Preferably, in step 105, a multi-scale fusion feature map f is obtained. m The specific expression is as follows:

[0032] f m =(f1+f2+f3+f a )×A opt .

[0033] Preferably, the specific expression of the space-channel feature enhancement map F in step 106 is as follows:

[0034] F = σ(Conv(f m ))*σ(FC(f m ))+f m ;

[0035] Among them, FC(.) is the fully connected layer and σ is the activation function.

[0036] Preferably, the specific expression of mapping the spatial-channel feature enhancement map F to the category space through the fully connected layer in step 107 is as follows:

[0037]

[0038] Among them, P(a i |b) indicates that the given input image b belongs to category a i The probability of is the i-th element of the j-th category output by the fully connected layer, and n is the total number of categories.

[0039] Therefore, the present invention adopts the above-mentioned plant disease and insect pest detection equipment and method based on drone high-altitude texture imaging, which has the following beneficial effects:

[0040] (1) Comprehensive application areas: The use of UAV high-altitude texture imaging equipment and methods can quickly and efficiently detect plant diseases and insect pests in the field, and provide accurate and detailed data support for research, monitoring and decision-making in the fields of agriculture, forestry, environmental monitoring, urban planning, etc., with broad application prospects and market potential.

[0041] (2) High-quality texture image acquisition: The quality of high-altitude imaging is greatly improved through the professional combination of a 61-megapixel full-frame micro-single digital camera and a lens with a focal length of 70mm to 200mm.

[0042] (3) Efficient pest and disease identification method: The pest and disease detection method uses an environmental perception-driven data enhancement module, an adaptive multi-scale graph convolution feature extraction and fusion module, and a pest and disease regional attention fusion module to enhance the ability to extract plant pest and disease features. Compared with the traditional single-scale extraction method, this model can achieve higher resolution in detail feature extraction, thereby improving the accuracy of pest and disease detection. The plant pest and disease identification model is trained through the above method and deployed on a small host to achieve real-time image processing and recognition without relying on external network connections or cloud servers, greatly improving the reliability and practicality of the system.

[0043] (4) Rapid response and real-time recognition capability: Deploy the trained model to the drone equipment to achieve real-time recognition of plant diseases and pests in the image, helping farmers to detect and deal with diseases and pests in a timely manner and reduce crop losses.

[0044] (5) Accurate shooting angle adjustment: The drone’s front-mounted high-definition camera can monitor the images sent back in real time. The flight path and shooting angle can be accurately adjusted through the flight controller and Ronin 4D handle in conjunction with the DJI RS 3Pro gimbal. Combined with the autofocus function, the clarity and quality of the captured images can be ensured.

[0045] (6) Flexible and convenient equipment matching: The DJI RS 3Pro gimbal used has a quick-release design and is equipped with matching accessories, making the equipment installation, storage and use more flexible and convenient, improving the convenience and comfort of operation.

[0046] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is an overall structural diagram of the plant disease and insect pest detection device based on drone high-altitude texture imaging of the present invention;

[0048] Figure 2 A top view of a plant disease and insect pest detection device based on drone high-altitude texture imaging according to the present invention;

[0049] Figure 3 A schematic diagram of a high-altitude texture camera and its imaging process according to an embodiment of the present invention;

[0050] Figure 4 This is a schematic diagram of the structure of a handle according to an embodiment of the present invention;

[0051] Figure 5 This is a schematic diagram of the structure of a flight remote controller according to an embodiment of the present invention;

[0052] Figure 6 It is a schematic diagram of the framework of the plant disease and insect pest identification method based on the fusion of multi-scale feature high-altitude texture features of the present invention;

[0053] Figure 7 A schematic diagram of feature visualization of the plant disease and insect pest identification method based on multi-scale high-altitude texture feature fusion of the present invention;

[0054] Among them, 1. Small host; 2. Drone body; 3. High-altitude texture camera; 4. Gimbal; 5. Front HD camera; 6. Photo button; 7. Shooting angle selection button; 8. Flight mode button; 9. Left joystick; 10. Zoom button; 11. Right joystick; 12. Light sensor. DETAILED DESCRIPTION

[0055] The following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] See also Figure 1-7 , a plant pest and disease detection device based on drone high-altitude texture imaging, including a drone body 2, a gimbal 4, a high-altitude texture camera 3 and a small host 1; the high-altitude texture camera 3 is arranged below the gimbal 4, the gimbal 4 is arranged below the small host 1, the small host 1 is arranged below the drone body 2, the drone body 2 is equipped with a front high-definition camera 5, a battery, and a light sensor 12, and the high-altitude texture camera 3 is wirelessly equipped with a handle and a flight remote control via Bluetooth. The high-altitude texture camera 3 is composed of a 61-megapixel camera and a lens with a focal length of 70mm to 200mm. The 61-megapixel camera and the lens with a focal length of 70mm to 200mm are determined by the Gaussian imaging formula. The Gaussian imaging formula can describe the relationship between lens imaging and determine the positional relationship between the object and the image, which can help us choose the best camera and lens. The specific expression is as follows:

[0057]

[0058] Among them, u is the object distance (this distance is the length between the lens and the plant leaves, which is about 10 meters. At this time, the high-altitude flying drone has little impact on the photographed object), v is the image distance, and f is the focal length.

[0059] In order to further verify the availability of the selected camera and lens, we used the camera internal and external parameter calculation formula to verify and prove that the camera and lens we matched are effective. The formula is as follows:

[0060]

[0061] Among them, the focal length f = 70mm ~ 200mm, the object distance u = 10m, the length and width of the SONY Alpha 7R V full-frame micro single digital camera are x = 35.7mm and y = 23.8mm respectively, and the length and width of the photographed crop are x c =1.8m and y c =1.2m.

[0062] Among them, the DJI RS 3 Pro gimbal, Ronin 4D handle, 61-megapixel SONY Alpha 7R V full-frame micro-single digital camera, and SONY FE 24-70mm F2.8 GM OSS II lens with a focal length of 70mm to 200mm are selected, which can support the acquisition of high-definition texture images of objects with a size of 1.8m×1.2m; the configured drone front HD camera provides image information in front of the flight route, and the flight of the drone can be accurately controlled through the flight remote control, and it is also equipped with two high-capacity batteries to support the flight; the bottom interface of the small host is connected to the DJI RS 3 Pro gimbal, and the data transmission between devices is realized through the Bluetooth function, and it also deploys a plant disease and insect pest recognition model with lower parameters; DJI RS The 3Pro gimbal is a quick-release design, a professional-grade three-axis stabilized gimbal system designed specifically for high-definition cameras, making its use, storage, installation and leveling more flexible and convenient, and supports a load weight of 4.5 kg; the Ronin4D handle and flight remote control rely on Bluetooth wireless connection to the high-altitude texture camera to achieve automatic focus of the lens, long-distance monitoring and collaborative shooting; the light sensor obtains light intensity data and assists the high-definition camera to make adjustments, so that the brightness, contrast and saturation of the image are better. Using the above equipment, during the process of high-altitude texture image acquisition, all data transmission and operation instructions are processed by the small host and its Bluetooth device. In order to find a more suitable shooting angle, the flight controller can see the picture sent back by the front high-definition camera of the drone, and the flight route can be adjusted in time through the left rocker 9 and the right rocker 11. Combined with the shooting angle selection button 7 in the Ronin 4D handle, the shooting angle of the high-definition camera can be adjusted through the DJI RS 3Pro gimbal, and the lens autofocus can be completed through the zoom button 10. Finally, the photo button 6 is controlled to take pictures, and the high-quality pictures with high-altitude textures are transmitted to the flight remote controller for display. The flight mode button 8 controls the flight status of the drone. When taking off, the state needs to be switched from the "forbidden" model to the "unlocked" mode before taking off. When collecting photos along the pre-planned flight route, adjustments can be made at any time according to the two control instruments, which greatly improves the quality and efficiency of data collection. Then, the DJI RS 3Pro gimbal is used to adjust the shooting angle of the multispectral camera, but there is no need to focus. The drone is stabilized and the instrument is left to complete the photo collection. Then, multispectral photos with different band information can be seen on the flight remote controller, thereby avoiding the trouble of constantly adjusting the equipment when collecting on the ground, and greatly improving the speed of picture collection.

[0063] A plant disease and insect pest detection method based on drone high-altitude texture imaging includes the following steps:

[0064] Step 1: Train the plant pest and disease recognition model; the specific process is as follows:

[0065] Step 101: adjust the brightness, contrast and saturation of the image according to the real-time light intensity, collect plant disease and insect pest image data, and perform data preprocessing; wherein the specific expression of image enhancement is as follows:

[0066] I aug =AL(I,LI);

[0067] Among them, I is the plant disease image, I aug is the enhanced image, LI is the real-time illumination intensity, and AL(·) is the adaptive function that adjusts the image brightness according to the illumination intensity.

[0068] Step 102: input the preprocessed data obtained in step 101 into the ResNet-101 model, train the ResNet-101 model, learn and extract features with plant pests and diseases texture, shape and structure from the plant pests and diseases image, and convert the extracted features into a high-dimensional feature vector f0; wherein, the specific expression of the feature extraction process is as follows:

[0069] f0=R(I aug ; φ);

[0070] Among them, φ is the parameter of the ResNet-101 model, R represents the ResNet-101 model, and f0 is the output feature vector.

[0071] Step 103: Input the high-dimensional feature vector f0 extracted in step 102 into the 3×3 convolution layer and the normalization layer respectively to obtain an attention map A focusing on the adaptive nearest neighbor K. opt ; Among them, the specific expression for obtaining the attention map of the adaptive nearest neighbor K is as follows:

[0072] A opt =Conv(Norm(Conv(f0)));

[0073] Among them, Conv(·) represents the convolution operation on image I, and Norm(·) is the normalization operation, which is used to standardize the feature map after convolution to ensure its numerical stability during training.

[0074] Step 104: Input the high-dimensional feature vector f0 extracted in step 102 into the graph convolution layer and the global average pooling layer whose nearest neighbor set is {K1=3, K2=5, K3=7} to obtain a multi-scale feature set {f1,f2,f3,f a}

[0075] Step 105: Through the multi-scale feature set {f1,f2,f3,f a} and the attention map A of the adaptive nearest neighbor K opt Fusion is performed to obtain a multi-scale fusion feature map f that retains more multi-scale information m ; Among them, the multi-scale fusion feature map f is obtained m The specific expression is as follows:

[0076] f m =(f1+f2+f3+f a )×A opt .

[0077] Step 106: fusion of multi-scale feature map f m After 3×3 convolutional layers, fully connected layers and activation layers, and then connected to f m Add them to each other to obtain the spatial-channel feature enhancement map F. The specific expression for obtaining the spatial-channel feature enhancement map F is as follows:

[0078] F = σ(Conv(f m ))*σ(FC(f m ))+f m ;

[0079] Among them, Conv(·) is the convolution operation, FC(·) is the fully connected layer, and σ is the activation function.

[0080] Step 107, map the spatial-channel feature enhancement map F to the category space through the fully connected layer, and use the softmax classifier to calculate the probability distribution of each category, and output the final classification result. Among them, the specific expression of mapping the spatial-channel feature enhancement map F to the category space through the fully connected layer is as follows:

[0081]

[0082] Among them, P(a i |b) indicates that the given input image b belongs to category a i The probability of is the i-th element of the j-th category output by the fully connected layer, and n is the total number of categories

[0083] Step 2: Deploy the trained plant disease and pest identification model to a small host. By embedding the plant disease and pest identification model directly into the device, we can achieve real-time image processing and recognition without relying on external network connections or cloud servers, greatly improving the reliability and practicality of the system.

[0084] Step 3: Take images through the high-altitude texture camera, and transfer the captured image data to the small host deployed with the plant pest and disease recognition model in step 2 for detection. Specifically, once the high-altitude texture camera captures the image, the data will be transferred to the small host for processing. In order to improve the accuracy of recognition, these high-altitude texture images are usually cut into 54 small images of the same size. Such operations help to reduce the burden on the model and can detect signs of plant pests and diseases more finely, thereby improving the efficiency of recognition. In addition, this also enables the model to better process images of different parts, so as to better adapt to various complex environments and scenes. It enables us to detect potential pest and disease problems in the entire image in a timely manner and take appropriate measures to deal with them, so as to reduce dependence on chemical pesticides to protect the growth and yield of crops.

[0085] pass Figure 7 We can find that the proposed method can focus on the characteristics of plant diseases and can still identify them regardless of whether the disease area is large or small. This shows that the proposed method can have a good recognition effect on diseases at long distances or close distances.

[0086] Therefore, the present invention adopts the above-mentioned plant disease and insect pest detection equipment and method based on drone high-altitude texture imaging, which can obtain more high-altitude texture features of plant diseases and insect pests, thereby realizing accurate identification of plant diseases and insect pests, and providing more accurate management and decision-making support for agricultural production.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A plant disease and insect pest detection device based on drone high-altitude texture imaging, characterized by: It includes a drone body, a gimbal, a high-altitude texture camera and a small host; the high-altitude texture camera is arranged below the gimbal, the gimbal is arranged below the small host, the small host is arranged below the drone body, the drone body is equipped with a front high-definition camera, a battery, and a light sensor, and the high-altitude texture camera is wirelessly equipped with a handle and a flight remote control via Bluetooth.

2. The plant disease and insect pest detection device based on drone high-altitude texture imaging according to claim 1 is characterized by: The high-altitude texture camera is composed of a 61-megapixel camera and a lens with a focal length of 70 mm to 200 mm. The 61-megapixel camera and the lens with a focal length of 70 mm to 200 mm are determined by the Gaussian imaging formula. The specific expression is as follows: Among them, u is the object distance, v is the image distance, and f is the focal length.

3. A method for detecting plant diseases and insect pests based on high-altitude texture imaging of unmanned aerial vehicles, characterized in that: The following steps are involved: Step 1: Train a plant disease and insect pest recognition model; Step 2: Deploy the trained plant disease and insect pest recognition model to a small host; Step 3: Take images through a high-altitude texture camera, and transmit the captured image data to the small host computer deployed with the plant disease and insect pest identification model in step 2 for detection.

4. The method for detecting plant diseases and insect pests based on drone high-altitude texture imaging according to claim 3 is characterized in that: The specific process of training the plant pest and disease recognition model in step 1 is as follows: Step 101: adjusting the brightness, contrast and saturation of the image according to the real-time light intensity, collecting plant disease and insect pest image data, and performing data preprocessing; Step 102: input the preprocessed data obtained in step 101 into the ResNet-101 model, and train the ResNet-101 model to learn and extract features of plant pests and diseases texture, shape and structure from the plant pests and diseases image, and convert the extracted features into a high-dimensional feature vector f0; Step 103: Input the high-dimensional feature vector f0 extracted in step 102 into the 3×3 convolution layer and the normalization layer respectively to obtain the attention map A focusing on the adaptive nearest neighbor K. opt ; Step 104: Input the high-dimensional feature vector f0 extracted in step 102 into the graph convolution layer and the global average pooling layer whose nearest neighbor set is {K1=3, K2=5, K3=7} to obtain a multi-scale feature set {f1,f2,f3,f a }; Step 105: Through the multi-scale feature set {f1,f2,f3,f a } and the attention map A of the adaptive nearest neighbor K opt Fusion is performed to obtain a multi-scale fusion feature map f m ; Step 106: The multi-scale fusion feature map f obtained in step 105 is m After 3×3 convolutional layers, fully connected layers and activation layers, and then connected to f m Add itself to obtain the spatial-channel feature enhancement map F; Step 107: Map the spatial-channel feature enhancement map F obtained in step 106 to the category space through a fully connected layer, and use the softmax classifier to calculate the probability distribution of each category, and output the final classification result.

5. The method for detecting plant diseases and insect pests based on drone high-altitude texture imaging according to claim 4 is characterized in that: The specific expression of the illumination perception enhancement in step 101 is as follows: I aug =AL(I,LI); Among them, I is the plant disease image, I aug is the enhanced image, LI is the real-time illumination intensity, and AL(·) is the adaptive function that adjusts the image brightness according to the illumination intensity.

6. The method for detecting plant diseases and insect pests based on drone high-altitude texture imaging according to claim 5 is characterized in that: The specific expression of the feature extraction process in step 102 is as follows: f0=R(I aug ;φ); Among them, φ is the parameter of the ResNet-101 model, R represents the ResNet-101 model, and f0 is the output feature vector.

7. The method for detecting plant diseases and insect pests based on drone high-altitude texture imaging according to claim 6, characterized in that: The specific expression of the attention map of the adaptive nearest neighbor K in step 103 is as follows: A opt =Conv(Norm(Conv(f0))); Among them, Conv(·) represents the convolution operation on image I, and Norm(·) is the normalization operation.

8. The method for detecting plant diseases and insect pests based on drone high-altitude texture imaging according to claim 7, characterized in that: In step 105, a multi-scale fusion feature map f is obtained. m The specific expression is as follows: <h2 style=";text-align:left;direction:ltr">f<h2 style=";text-align:left;direction:ltr"> m <h2 style=";text-align:left;direction:ltr"> (f1+f2+f3+f<h2 style=";text-align:left;direction:ltr"> a <h2 style=";text-align:left;direction:ltr"> )×A<h2 style=";text-align:left;direction:ltr"> opt <h2 style=";text-align:left;direction:ltr"> 。 9. The method for detecting plant diseases and insect pests based on drone high-altitude texture imaging according to claim 8, characterized in that: The specific expression of the space-channel feature enhancement map F in step 106 is as follows: F=σ(Conv(f m ))*σ(FC(f m ))+f m ; Among them, FC(.) is the fully connected layer and σ is the activation function.

10. The method for detecting plant diseases and insect pests based on drone high-altitude texture imaging according to claim 9, characterized in that: The specific expression of mapping the spatial-channel feature enhancement map F to the category space through the fully connected layer in step 107 is as follows: Among them, P(a i |b) indicates that the given input image b belongs to category a i The probability of is the i-th element of the j-th category output by the fully connected layer, and n is the total number of categories.

Images