Artificial intelligence-based plum blossom growth cycle disease image processing method and system

Abstract

The invention relates to a plum blossom growth cycle disease image processing method and system based on artificial intelligence, and belongs to the technical field of plum blossom growth cycle disease image processing. The method comprises the following steps: acquiring a plum blossom disease original image; extracting a multi-scale feature space through an image processing module; a super-resolution enhancement structure with double modules connected in series is constructed, and the two fuzzy enhancement modules respectively process features and then fuse and output the features; designing a reconstruction module containing a convolutional layer, blueprint separable convolution and residual connection, and generating high-resolution image features through feature fusion and up-sampling; global pooling and deep sharing convolution are adopted to construct an attention mechanism, and disease semantic features are captured; feature dimensions are compressed through global average pooling, the feature dimensions are mapped to a category space through a full connection layer, and a Softmax activation function is utilized to output a classification probability. According to the method, through multi-stage processing of feature enhancement-reconstruction-attention focusing, the accuracy of the model to identify the plum blossom disease image is effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of image processing of diseases during the growth period of plum blossoms, and in particular relates to an image processing method and system for diseases during the growth period of plum blossoms based on artificial intelligence. Background Art

[0002] The identification and prevention of diseases during the plum blossom's growth cycle have always been a key area of ​​research in agricultural, forestry, and horticultural plants. Traditionally, plum blossom disease prevention and control relies primarily on manual inspections to observe signs of disease, such as leaf discoloration and branch damage, combined with technicians' experience to determine the type and severity of the disease. However, the limited number of plum blossom cultivation experts and their varying experience have led to subjective plant disease identification and a high rate of misjudgment, often leading to delayed pest and disease control and inadequate nutritional regulation throughout the plum blossom's growth cycle.

[0003] With the development of computer vision and deep learning technologies, image recognition methods based on convolutional neural networks have achieved remarkable results in the detection and identification of forestry and agricultural diseases. However, general image processing and recognition technologies still face some challenges in the application of plum blossom disease recognition throughout its growth cycle. First, the phenotypic characteristics of plum blossom disease images vary significantly at different growth stages. Existing disease images in databases are relatively fuzzy, and disease features are unclear, resulting in low accuracy in existing disease recognition methods. Second, plum blossom cultivation scenarios involve complex natural background conditions, varying lighting conditions, and leaf occlusions, which interfere with the model's ability to recognize plum blossom disease images. Furthermore, the model's generalization across plum varieties and climate zones is poor, resulting in low accuracy in identifying plum blossom disease throughout its growth cycle.

[0004] Therefore, the present invention proposes an artificial intelligence-based method and system for processing images of diseases in the growth cycle of plum blossoms to solve the problem that the disease images in the existing prevention and control process of diseases in the growth cycle of plum blossoms are blurred and the background is complex, resulting in low accuracy of disease model recognition. Summary of the Invention

[0005] In order to solve the above problems, the present invention provides an artificial intelligence-based method and system for processing images of diseases in the growth period of plum blossoms.

[0006] To achieve the above-mentioned purpose, the present invention is implemented through the following technical solutions: The present invention provides an artificial intelligence-based method for processing images of diseases during the growth period of plum blossoms, comprising the following steps: S1. Obtaining images of plum blossom diseases ; S2. Plum blossom disease images Input it into the plum blossom disease image processing module for processing to obtain the mapped feature space ; S3. Construct a super-resolution enhancement module for plum blossom disease images, including two fuzzy image enhancement modules with the same structure in series; map the feature space Input to the first fuzzy image enhancement module for processing to obtain the first fuzzy image enhancement feature , the first blurred image enhancement feature Input to the second fuzzy image enhancement module and output the second fuzzy image enhancement feature ; Enhance the first blurred image feature and the second fuzzy image enhancement feature Splicing and fusion to output super-resolution enhancement features of plum blossom disease images ; S4. Construct a plum blossom disease image reconstruction module, including convolutional layer, blueprint separable convolutional layer, global residual connection layer and upsampling layer; plum blossom disease image super-resolution enhancement feature After processing by the convolution layer and the blueprint separable convolution layer, the intermediate features are obtained , intermediate features and the mapped feature space After the global residual connection layer fusion, the original image information is retained to obtain the fused global features , integrating global features The plum blossom disease image features are reconstructed through the upsampling layer to obtain the super-resolution plum blossom disease image features. ; S5. Construct a super-resolution image attention semantic feature capture module, including a global pooling layer, a deep shared convolutional layer, a feature splicing operation, and Activation function; super-resolution plum blossom disease image features After being processed by the super-resolution image attention semantic feature capture module, the super-resolution image attention semantic feature is obtained ; S6. Construct a plum blossom disease image recognition output module to focus on the semantic features of super-resolution images. The input to this module first passes through the global pooling layer to perform global average pooling on the spatial dimension of the feature map, and outputs the global average pooling feature. , the global average pooling feature Map the feature vector to the category space through the fully connected layer and output the mapping feature , and finally the mapping features Input to Activation function to obtain the probability distribution of each sample , and get the final recognition and classification prediction results .

[0007] Furthermore, step S2 specifically includes: S21. Low-resolution image of plum blossom disease After the low-resolution image channel copy operation, copy in the channel dimension After that, the copied low-resolution image is obtained by splicing along the channel dimension. , the formula is as follows: , in, Represents a splicing operation, represents the number of disease samples input into the model, Indicates the height of the disease image, Indicates the width of the diseased image, Indicates the number of channels of the disease image, ; S22. The copied low-resolution image Perform low-resolution image feature mapping operations and use blueprint separable convolution to map the copied low-resolution image to obtain the mapped feature space , the blueprint separable convolution includes a convolution kernel size of The convolution layer and convolution kernel size are The depth-wise separable convolutional layer is expressed as follows: , in, represents the blueprint separable convolution, Indicates that the convolution kernel size is The convolutional layer, Indicates that the convolution kernel size is The depth of the separable convolutional layer.

[0008] Furthermore, step S3 specifically includes: S31. Mapped feature space The convolution kernel size after the first blurred image enhancement module is The convolution layer, according to the mapped feature space The channel is divided into two sub-features with the same number of channels. The first sub-feature and the second sub-feature ; Second sub-feature After the convolution kernel size is The convolution layer is divided into third sub-features with the same number of channels along the channel and the fourth sub-characteristic ; The third sub-feature After the convolution kernel size is The convolution layer is processed to obtain the first spatial feature , the first spatial feature With the fourth sub-characteristic Perform splicing and fusion, and use the convolution kernel size of The convolutional layer fusion features are used to obtain the first output feature , the formula is as follows: , , , , in, Indicates that the features are split according to channel features. Indicates that the convolution kernel size is Depthwise separable convolutional layers, express Activation function operation; S32. The first output feature After the convolution kernel size is The convolution layer, according to the first output feature The channel splits it into the fifth sub-feature and the sixth sub-characteristic , the sixth sub-feature After the convolution kernel size is The convolution layer is divided into the seventh sub-feature with the same number of channels along the channel and the eighth sub-feature ; The seventh sub-feature The convolution kernel size is used Perform convolution operation layer by layer to obtain the second spatial feature , the second spatial feature With the eighth sub-characteristic Perform splicing and fusion, and use the convolution kernel size of The convolutional layer fusion features are used to obtain the second output feature , the formula is as follows: , , , , in, Indicates that the convolution kernel size is Depthwise separable convolutional layers; S33. The second output feature Input into the blueprint separable convolution for processing to obtain the third output feature , the formula is as follows: ; The third output feature , the first sub-feature And the fifth sub-feature After splicing and fusion and convolution kernel size is The convolution layer adjusts the features to obtain the first blurred image enhancement feature , the formula is as follows: ; The first blurred image enhancement feature Input to the second fuzzy image enhancement module to obtain the second fuzzy image enhancement feature , the features and Splicing and fusion to obtain super-resolution enhancement features of plum blossom disease images .

[0009] Furthermore, step S4 specifically includes: S41. Super-resolution enhancement features of the plum blossom disease image After the convolution kernel size is The convolutional layer and Activation function operation to obtain initial reconstruction features , the initial reconstruction features Input into the blueprint separable convolution layer for processing to obtain intermediate features , the formula is as follows: , , in, represents the blueprint separable convolution, express Activation function operation; S42. Intermediate features and the mapped feature space After the global residual connection operation, the fused global features are obtained , integrating global features After upsampling, the image features of plum blossom diseases are reconstructed to obtain super-resolution image features of plum blossom diseases. , the formula is as follows: , , in, represents the element-by-element addition operation, Represents the operation of reconstructing super-resolution images.

[0010] Furthermore, step S5 specifically includes: S51. Super-resolution plum blossom disease image features After the global average pooling layer, global pooling is performed along the height and width dimensions respectively to obtain sub-features decomposed along the height and sub-features decomposed along the width , the sub-feature and Divided equally along the channel dimension Group, get the sub-feature grouping along the height decomposition and sub-feature grouping along width decomposition , the formula is as follows: , , , , in, The number of channels representing the sub-features, represents the width of the sub-feature decomposed along the height, represents the height of the sub-features decomposed along the width, represents the global pooling operation, Indicates that the features are compressed and decomposed according to the height dimension. Indicates that the features are compressed and decomposed according to the width dimension. and Represents pair features and Group and slice features according to the feature channel dimension. ;when hour, The channel dimension index is ,when , The channel dimension index is , and so on, The operation is the same; S52. Decompose the first grouping feature along the height , the second grouping feature , the third grouping feature And the fourth group feature After the convolution kernel size is The depth of the separable 1D convolution layer is used to obtain the first group feature after processing along the height decomposition , the second grouping feature , the third grouping feature And the fourth group feature , the formula is as follows: , , , , in, 、 、 They represent the convolution kernel size respectively. 、 、 Depthwise separable convolution operation; sub-feature grouping along width decomposition The processing process is similar to that of the first grouping feature decomposed along the width. , the second grouping feature , the third grouping feature And the fourth group feature ; S53. The first grouping feature after processing along the height decomposition , the second grouping feature , the third grouping feature And the fourth group feature The first new feature is obtained by splicing, fusion and group normalization. Similarly, the first grouping feature after processing along the width decomposition , the second grouping feature , the third grouping feature And the fourth group feature After the same operation, the second new feature is obtained ; The first new feature And the second new feature Input to Activation function, respectively get the first spatial attention feature map and the second spatial attention feature map , the first spatial attention feature map , Second spatial attention feature map And super-resolution plum blossom disease image features Fusion is performed to obtain super-resolution image attention semantic features , the formula is as follows: , , , , , in, represents the group normalization operation, represents the Sigmoid activation function, Represents an element-wise multiplication operation.

[0011] Furthermore, step S6 specifically includes: Super-resolution image attention semantic features The spatial dimension of the feature map is globally averaged pooled through the global pooling layer, and the global average pooling feature is output. , global average pooling feature After processing through the fully connected layer, the output mapping features are obtained , the mapping feature Input to Activation function to obtain the probability distribution of each sample , and get the final recognition and classification prediction results , the formula is as follows: , , , , in, represents the global average pooling operation, represents the fully connected layer, Indicates that the maximum index value is the category label of the model's final recognition image. Indicates traversing the maximum index value in the first dimension.

[0012] The present invention also provides an artificial intelligence-based image processing system for plum blossom growth cycle diseases, which executes an artificial intelligence-based image processing method for plum blossom growth cycle diseases, including: Data acquisition module: used to obtain plum blossom disease images; Plum blossom disease image processing module: used to process plum blossom disease images and obtain the mapped feature space; Plum blossom disease image super-resolution enhancement module: used to perform super-resolution enhancement on the mapped feature space to obtain super-resolution enhancement features of plum blossom disease images; Plum blossom disease image reconstruction module: used to reconstruct the super-resolution enhanced features of the plum blossom disease image to obtain the super-resolution plum blossom disease image features; Super-resolution image attention semantic feature capture module: used to capture the semantic features in the super-resolution plum blossom disease image features and obtain super-resolution image attention semantic features; Plum blossom disease image recognition output module: used to input the super-resolution image attention semantic features into this module, obtain the probability distribution of each sample, and obtain the final recognition and classification prediction results.

[0013] The advantages of the present invention are: The present invention utilizes a super-resolution enhancement module for plum blossom disease images to perform super-resolution processing on diseased images, and uses a plum blossom disease image reconstruction module to convert blurry and difficult-to-identify low-resolution images into easily recognizable high-resolution images, which helps the model recognize and classify plum blossom disease images and improves the recognition accuracy of the model. Through the super-resolution image attention semantic feature capture module, the local and global spatial semantic features of the diseased images are captured, the low-level texture information of the diseased images is deeply mined, and the model's ability to process diseased images in complex scenes is enhanced, thereby effectively improving the model's accuracy in recognizing plum blossom diseased images. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0015] Figure 1 is a flow chart of the steps of the method of the present invention; Figure 2 This is a comparison of plum blossom disease images before and after processing in the method of the present invention; Figure 3 This is a comparative test effect diagram of the method of the present invention. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments derived by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0017] Example 1 In this embodiment, Figure 1 As shown, the present invention provides an artificial intelligence-based method for processing images of diseases during the growth period of plum blossoms, which specifically includes the following steps: S1. Obtaining images of plum blossom diseases ; S2. Plum blossom disease images Input it into the plum blossom disease image processing module for processing to obtain the mapped feature space ; Specifically, S21. low-resolution image of plum blossom disease After the low-resolution image channel copy operation, copy in the channel dimension After that, the copied low-resolution image is obtained by splicing along the channel dimension. , the formula is as follows: , in, Represents a splicing operation, represents the number of disease samples input into the model, Indicates the height of the disease image, Indicates the width of the diseased image, Indicates the number of channels of the disease image, ; S22. The copied low-resolution image Perform low-resolution image feature mapping operations and use blueprint separable convolution to map the copied low-resolution image to obtain the mapped feature space , the blueprint separable convolution includes a convolution kernel size of The convolution layer and convolution kernel size are The depth-wise separable convolutional layer is expressed as follows: , in, represents the blueprint separable convolution, Indicates that the convolution kernel size is The convolutional layer, Indicates that the convolution kernel size is The depth of the separable convolutional layer.

[0018] S3. Construct a super-resolution enhancement module for plum blossom disease images, including two fuzzy image enhancement modules with the same structure in series; map the feature space Input to the first fuzzy image enhancement module for processing to obtain the first fuzzy image enhancement feature , the first blurred image enhancement feature Input to the second fuzzy image enhancement module and output the second fuzzy image enhancement feature ; Enhance the first blurred image feature and the second fuzzy image enhancement feature Splicing and fusion to output super-resolution enhancement features of plum blossom disease images ; Specifically, S31. Mapped feature space The convolution kernel size after the first blurred image enhancement module is The convolution layer, according to the mapped feature space The channel is divided into two sub-features with the same number of channels. The first sub-feature and the second sub-feature ; Second sub-feature After the convolution kernel size is The convolution layer is divided into third sub-features with the same number of channels along the channel and the fourth sub-characteristic ; The third sub-feature After the convolution kernel size is The convolution layer is processed to obtain the first spatial feature , the first spatial feature With the fourth sub-characteristic Perform splicing and fusion, and use the convolution kernel size of The convolutional layer fusion features are used to obtain the first output feature , the formula is as follows: , , , , in, Indicates that the features are split according to channel features. Indicates that the convolution kernel size is Depthwise separable convolutional layers, express Activation function operation; S32. The first output feature After the convolution kernel size is The convolution layer, according to the first output feature The channel splits it into the fifth sub-feature and the sixth sub-characteristic , the sixth sub-feature After the convolution kernel size is The convolution layer is divided into the seventh sub-feature with the same number of channels along the channel and the eighth sub-feature ; The seventh sub-feature The convolution kernel size is used Perform convolution operation layer by layer to obtain the second spatial feature , the second spatial feature With the eighth sub-characteristic Perform splicing and fusion, and use the convolution kernel size of The convolutional layer fusion features are used to obtain the second output feature , the formula is as follows: , , , , in, Indicates that the convolution kernel size is Depthwise separable convolutional layers; S33. The second output feature Input into the blueprint separable convolution for processing to obtain the third output feature , the formula is as follows: ; The third output feature , the first sub-feature And the fifth sub-feature After splicing and fusion and convolution kernel size is The convolution layer adjusts the features to obtain the first blurred image enhancement feature , the formula is as follows: ; The first blurred image enhancement feature Input to the second fuzzy image enhancement module to obtain the second fuzzy image enhancement feature , the features and Splicing and fusion to obtain super-resolution enhancement features of plum blossom disease images .

[0019] S4. Construct a plum blossom disease image reconstruction module, including convolutional layer, blueprint separable convolutional layer, global residual connection layer and upsampling layer; plum blossom disease image super-resolution enhancement feature After processing by the convolution layer and the blueprint separable convolution layer, the intermediate features are obtained , intermediate features and the mapped feature space After the global residual connection layer fusion, the original image information is retained to obtain the fused global features , integrating global features The plum blossom disease image features are reconstructed through the upsampling layer to obtain the super-resolution plum blossom disease image features. ; Specifically, S41. the super-resolution enhancement feature of the plum blossom disease image After the convolution kernel size is The convolutional layer and Activation function operation to obtain initial reconstruction features , the initial reconstruction features Input into the blueprint separable convolution layer for processing to obtain intermediate features , the formula is as follows: , , in, represents the blueprint separable convolution, express Activation function operation; S42. Intermediate features and the mapped feature space After the global residual connection operation, the fused global features are obtained , integrating global features After upsampling, the image features of plum blossom diseases are reconstructed to obtain super-resolution image features of plum blossom diseases. , the formula is as follows: , , in, represents the element-by-element addition operation, Represents the operation of reconstructing super-resolution images.

[0020] S5. Construct a super-resolution image attention semantic feature capture module, including a global pooling layer, a deep shared convolutional layer, a feature splicing operation, and Activation function; super-resolution plum blossom disease image features After being processed by the super-resolution image attention semantic feature capture module, the super-resolution image attention semantic feature is obtained ; Specifically, S51. Super-resolution plum blossom disease image features After the global average pooling layer, global pooling is performed along the height and width dimensions respectively to obtain sub-features decomposed along the height and sub-features decomposed along the width , the sub-feature and Divided equally along the channel dimension Group, get the sub-feature grouping along the height decomposition and sub-feature grouping along width decomposition , the formula is as follows: , , , , in, The number of channels representing the sub-features, represents the width of the sub-feature decomposed along the height, represents the height of the sub-features decomposed along the width, represents the global pooling operation, Indicates that the features are compressed and decomposed according to the height dimension. Indicates that the features are compressed and decomposed according to the width dimension. and Represents pair features and Group and slice features according to the feature channel dimension. ;when hour, The channel dimension index is ,when , The channel dimension index is , and so on, The operation is the same; S52. Decompose the first grouping feature along the height , the second grouping feature , the third grouping feature And the fourth group feature After the convolution kernel size is The depth of the separable 1D convolution layer is used to obtain the first group feature after processing along the height decomposition , the second grouping feature , the third grouping feature And the fourth group feature , the formula is as follows: , , , , in, 、 、 They represent the convolution kernel size respectively. 、 、 Depthwise separable convolution operation; sub-feature grouping along width decomposition The processing process is similar to that of the first grouping feature decomposed along the width. , the second grouping feature , the third grouping feature And the fourth group feature ; S53. The first grouping feature after processing along the height decomposition , the second grouping feature , the third grouping feature And the fourth group feature The first new feature is obtained by splicing, fusion and group normalization. Similarly, the first grouping feature after processing along the width decomposition , the second grouping feature , the third grouping feature And the fourth group feature After the same operation, the second new feature is obtained ; The first new feature And the second new feature Input to Activation function, respectively get the first spatial attention feature map and the second spatial attention feature map , the first spatial attention feature map , Second spatial attention feature map And super-resolution plum blossom disease image features Fusion is performed to obtain super-resolution image attention semantic features , the formula is as follows: , , , , , in, represents the group normalization operation, represents the Sigmoid activation function, Represents an element-wise multiplication operation.

[0021] S6. Construct a plum blossom disease image recognition output module to focus on the semantic features of super-resolution images. The input to this module first passes through the global pooling layer to perform global average pooling on the spatial dimension of the feature map, and outputs the global average pooling feature. , the global average pooling feature Map the feature vector to the category space through the fully connected layer and output the mapping feature , and finally the mapping features Input to Activation function to obtain the probability distribution of each sample , and get the final recognition and classification prediction results .

[0022] Specifically, super-resolution image attention semantic features The spatial dimension of the feature map is globally averaged pooled through the global pooling layer, and the global average pooling feature is output. , global average pooling feature After processing through the fully connected layer, the output mapping features are obtained , the mapping feature Input to Activation function to obtain the probability distribution of each sample , and get the final recognition and classification prediction results , the formula is as follows: , , , , in, represents the global average pooling operation, represents the fully connected layer, Indicates that the maximum index value is the category label of the model's final recognition image. Indicates traversing the maximum index value in the first dimension.

[0023] Example 2 In order to verify the effectiveness of the artificial intelligence-based plum blossom growth cycle disease identification method and system proposed in the present invention in the direction of disease image processing and identification, the method was experimented with the existing forestry and agricultural disease image processing technology under the same conditions and environment, and the experimental results obtained were analyzed and demonstrated in detail to verify the effectiveness of the proposed method in the direction of plum blossom growth cycle disease image processing.

[0024] In the comparative experiments, three currently popular image processing models were selected as comparison models to demonstrate the performance of the proposed method. The three comparison models used in the experiment are: ConvNeXt is a hybrid model based on the improved CNN architecture. By combining the local feature extraction capabilities of CNN with the global modeling capabilities of Transformer, this model achieves high accuracy in image classification tasks. However, its generalization ability is poor in small sample tasks and complex scenarios; EfficientViT uses a multi-scale local attention mechanism and depthwise separable convolution to achieve classification accuracy comparable to large models, but its classification ability for long-tailed distributed data is weak and relies on pre-trained weights; SwinTransformer uses sliding window self-attention to reduce computational complexity while enhancing multi-scale feature expression capabilities. However, the window size and hierarchical structure need to be fine-tuned during training, making the model more complex.

[0025] In a comparative test of the proposed AI-based image processing method for plum blossom disease during its growth cycle, four experimental indicators were used to evaluate the effectiveness of the proposed method: Accuracy (Acc) reflects the overall classification accuracy of the proposed model for healthy and diseased plum blossom samples; Precision (Pre) measures the proportion of samples predicted by the proposed method as being diseased that are actually diseased; Recall represents the proportion of true diseased plum blossom samples that are correctly identified; and F1-score, by harmonizing the average precision and recall, is suitable for evaluating scenarios with uneven distribution of plum blossom disease data.

[0026] To this end, a comparative experiment established an image dataset for disease identification throughout the plum blossom growth cycle. This dataset, collected via web crawler from publicly available agricultural websites, plant pathology forums, and plum blossom cultivation platforms, covers plum blossom health and disease states in a variety of complex scenarios, and records the characteristics of each growth stage. The dataset contains 13 categories (including health status and disease type), and the number of images in each category exhibits a long-tail distribution. In the experiment, the images of each category in the dataset were divided into training and test sets in an 8:2 ratio to ensure the model's generalization and evaluation reliability across diverse scenarios.

[0027] Table 1 Comparison results of the proposed methods in the dataset The proposed method was experimentally verified on the plum blossom growth cycle disease dataset. Figure 2 The high-resolution images of plum blossom diseases processed by the proposed method are shown in Tables 1 and Figure 3 As shown. The experimental results show that the proposed method performs best in the image processing and recognition of plum blossom disease during its growth period. This method can process relatively fuzzy plum blossom disease images into high-resolution disease images, which helps the model to accurately identify the features of plum blossom disease images. The four experimental indicators are significantly higher than those of other models. Figure 3 In the comparative test results, the proposed method achieved 86%, 87%, and 82% accuracy in identifying plum blossom diseases such as yellowing, perforation, and plaster, respectively. These results are the best among the compared methods. These two experimental results demonstrate the effectiveness of the proposed method in image processing and identification of plum blossom diseases during their growth cycle.

[0028] Example 3 This embodiment provides an artificial intelligence-based image processing system for plum blossom growth cycle diseases, which executes the artificial intelligence-based image processing method for plum blossom growth cycle diseases described in Example 1, including: Data acquisition module: used to obtain plum blossom disease images; Plum blossom disease image processing module: used to process plum blossom disease images and obtain the mapped feature space; Plum blossom disease image super-resolution enhancement module: used to perform super-resolution enhancement on the mapped feature space to obtain super-resolution enhancement features of plum blossom disease images; Plum blossom disease image reconstruction module: used to reconstruct the super-resolution enhanced features of the plum blossom disease image to obtain the super-resolution plum blossom disease image features; Super-resolution image attention semantic feature capture module: used to capture the semantic features in the super-resolution plum blossom disease image features and obtain super-resolution image attention semantic features; Plum blossom disease image recognition output module: used to input the super-resolution image attention semantic features into this module, obtain the probability distribution of each sample, and obtain the final recognition and classification prediction results.

[0029] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. An artificial intelligence-based plum blossom growth cycle disease image processing method, characterized in that: The following steps are involved: S1. Obtaining plum blossom disease images ; S2. Plum blossom disease image Input to the plum blossom disease image processing module for processing to obtain the mapped feature space ; S3. Construct a super-resolution enhancement module for plum blossom disease images, including two fuzzy image enhancement modules with the same structure in series; map the feature space Input to the first fuzzy image enhancement module for processing to obtain the first fuzzy image enhancement feature , the first blurred image enhancement feature Input to the second fuzzy image enhancement module, output the second fuzzy image enhancement feature ; The first blurred image is enhanced and the second fuzzy image enhancement feature Splicing and fusion to output super-resolution enhancement features of plum blossom disease images ; S4. Construct a plum blossom disease image reconstruction module, including convolutional layer, blueprint separable convolutional layer, global residual connection layer and upsampling layer; super-resolution enhancement feature of plum blossom disease image After processing by the convolution layer and the blueprint separable convolution layer, the intermediate features are obtained , intermediate features The feature space with the mapping After the global residual connection layer fusion, the original image information is retained to obtain the fused global features , integrating global features The plum blossom disease image features are reconstructed through the upsampling layer to obtain the super-resolution plum blossom disease image features. ; S5. Construct a super-resolution image attention semantic feature capture module, including a global pooling layer, a deep shared convolutional layer, a feature splicing operation, and Activation function; Super-resolution plum blossom disease image features After being processed by the super-resolution image attention semantic feature capture module, the super-resolution image attention semantic feature is obtained ; S6. Construct a plum blossom disease image recognition output module to focus on the semantic features of super-resolution images The input to this module first passes through the global pooling layer to perform global average pooling on the spatial dimension of the feature map, and outputs the global average pooling feature. , the global average pooling feature Map the feature vector to the category space through the fully connected layer and output the mapping feature , and finally the mapping feature Input to Activation function to obtain the probability distribution of each sample , and obtain the final recognition and classification prediction results .

2. The method for processing plum blossom growth cycle diseases image based on artificial intelligence according to claim 1 is characterized in that: Step S2 specifically includes: S21. Low-resolution image of plum blossom disease After the low-resolution image channel copy operation, copy in the channel dimension After that, the copied low-resolution image is obtained by splicing along the channel dimension. , the formula is as follows: ， in, Represents a splicing operation, represents the number of disease samples input into the model, Indicates the height of the disease image, represents the width of the diseased image, Indicates the number of channels of the disease image, ; S22. The copied low-resolution image Perform low-resolution image feature mapping operations, use blueprint separable convolution to map the copied low-resolution image, and obtain the mapped feature space , the blueprint separable convolution includes a convolution kernel size of The convolution layer and convolution kernel size are The depth-separable convolutional layer is expressed as follows: ， in, represents the blueprint separable convolution, Indicates that the convolution kernel size is The convolutional layer, Indicates that the convolution kernel size is The depth of the separable convolutional layer.

3. The method for processing plum blossom growth cycle diseases image based on artificial intelligence according to claim 2 is characterized in that: Step S3 specifically includes: S31. Mapped feature space The convolution kernel size after the first blurred image enhancement module is The convolution layer, according to the mapped feature space The channels are divided into two sub-features with the same number of channels. The first sub-feature and the second sub-feature ; Second sub-feature After convolution again, the kernel size is The convolutional layer is divided into third sub-features with the same number of channels along the channel and the fourth sub-characteristic ; The third sub-feature After convolution, the kernel size is The convolution layer is processed to obtain the first spatial feature , the first spatial feature With the fourth sub-characteristic Perform splicing and fusion, and use the convolution kernel size of The convolutional layer fusion features are used to obtain the first output feature , the formula is as follows: ， ， ， ， in, Indicates that the features are split according to channel features. Indicates that the convolution kernel size is Depthwise separable convolutional layers, express Activation function operation; S32. The first output feature After convolution, the kernel size is The convolution layer, according to the first output feature The channel splits it into the fifth sub-feature and the sixth sub-characteristic , the sixth sub-feature After convolution, the kernel size is The convolutional layer is divided into the seventh sub-feature with the same number of channels along the channel and the eighth sub-characteristic ; The seventh sub-feature The convolution kernel size is used Convolution operation is performed layer by layer to obtain the second spatial feature , the second spatial feature With the eighth sub-characteristic Perform splicing and fusion, and use the convolution kernel size of The convolutional layer fusion features are used to obtain the second output feature , the formula is as follows: ， ， ， ， in, Indicates that the convolution kernel size is Depthwise separable convolutional layers; S33. The second output feature Input to the blueprint separable convolution for processing to obtain the third output feature , the formula is as follows: ； The third output characteristic , the first sub-feature And the fifth sub-characteristic After splicing and fusion and convolution kernel size is The convolution layer adjusts the features to obtain the first blurred image enhancement feature , the formula is as follows: ； The first blurred image is enhanced Input to the second fuzzy image enhancement module to obtain the second fuzzy image enhancement feature , the feature and Splicing and fusion to obtain super-resolution enhancement features of plum blossom disease images .

4. The method for processing plum blossom growth cycle diseases image based on artificial intelligence according to claim 3 is characterized in that: Step S4 specifically includes: S41. Super-resolution enhancement features of the plum blossom disease image After convolution, the kernel size is The convolutional layers and Activation function operation to obtain the initial reconstruction features , the initial reconstruction feature Input to the blueprint separable convolutional layer for processing to obtain intermediate features , the formula is as follows: ， ， in, represents the blueprint separable convolution, express Activation function operation; S42. Intermediate features The feature space with the mapping After the global residual connection operation, the fused global features are obtained , integrating global features After upsampling, the image features of plum blossom diseases are reconstructed to obtain super-resolution image features of plum blossom diseases. , the formula is as follows: ， ， in, represents the element-by-element addition operation, Represents the operation of reconstructing a super-resolution image.

5. The method for processing plum blossom growth cycle diseases image based on artificial intelligence according to claim 4 is characterized in that: Step S5 specifically includes: S51. Super-resolution plum blossom disease image features After the global average pooling layer, global pooling is performed along the height and width dimensions to obtain sub-features decomposed along the height. and sub-features decomposed along the width , the sub-feature and Divided equally along the channel dimension Group, get the sub-feature grouping along the height decomposition and sub-feature grouping along width decomposition , the formula is as follows: ， ， ， ， in, represents the number of channels of the sub-features, represents the width of the sub-feature decomposed along the height, represents the height of the sub-features decomposed along the width, represents the global pooling operation, Indicates that the features are compressed and decomposed according to the height dimension. Indicates that the features are compressed and decomposed according to the width dimension. and Respectively represent the characteristics of the pair and Group and slice the features according to the feature channel dimension. ;when hour, The channel dimension index is ,when , The channel dimension index is , and so on, The operation is the same; S52. Decompose the first grouping feature along the height , the second grouping feature , the third grouping feature And the fourth grouping feature After the convolution kernel size is The depth of the separable 1D convolution layer obtains the first grouping feature after processing along the height decomposition , the second grouping feature , the third grouping feature And the fourth grouping feature , the formula is as follows: ， ， ， ， in, , , They represent the convolution kernel sizes respectively. , , Depthwise separable convolution operation; sub-feature grouping along width decomposition The processing process is similar to that of the first grouping feature decomposed along the width. , the second grouping feature , the third grouping feature And the fourth grouping feature ; S53. The first grouping feature after processing along the height decomposition , the second grouping feature , the third grouping feature And the fourth grouping feature The first new feature is obtained by concatenating and fusion and using group normalization. ; Similarly, the first grouping feature after processing along the width decomposition , the second grouping feature , the third grouping feature And the fourth grouping feature After the same operation, the second new feature is obtained ; The first new feature And the second new feature Input to Activation function, respectively get the first spatial attention feature map and the second spatial attention feature map , the first spatial attention feature map , Second spatial attention feature map And super-resolution plum blossom disease image features Fusion is performed to obtain the super-resolution image attention semantic features , the formula is as follows: ， ， ， ， ， in, represents the group normalization operation, represents the Sigmoid activation function, Represents an element-wise multiplication operation.

6. The method for processing plum blossom growth cycle diseases image based on artificial intelligence according to claim 5 is characterized in that: Step S6 specifically includes: Super-resolution image attention semantic features The spatial dimension of the feature map is globally averaged pooled through the global pooling layer, and the global average pooling feature is output , global average pooling feature After processing by the fully connected layer, the output mapping features are obtained , the mapping feature Input to Activation function to obtain the probability distribution of each sample , and obtain the final recognition and classification prediction results , the formula is as follows: ， ， ， ， in, represents the global average pooling operation, represents the fully connected layer, Indicates that the maximum index value is used as the category label of the model's final recognition image. Indicates traversing the maximum index value in the first dimension.

7. An artificial intelligence-based plum blossom growth cycle disease image processing system, which executes the artificial intelligence-based plum blossom growth cycle disease image processing method as claimed in claim 1, characterized in that: include: Data acquisition module: used to obtain plum blossom disease images; Plum blossom disease image processing module: used to process plum blossom disease images and obtain mapped feature space; Plum blossom disease image super-resolution enhancement module: used to perform super-resolution enhancement on the mapped feature space to obtain super-resolution enhancement features of plum blossom disease images; Plum blossom disease image reconstruction module: used to reconstruct the super-resolution enhancement features of the plum blossom disease image to obtain the super-resolution plum blossom disease image features; Super-resolution image attention semantic feature capture module: used to capture the semantic features in the super-resolution plum blossom disease image features to obtain super-resolution image attention semantic features; Plum blossom disease image recognition output module: used to input the super-resolution image attention semantic features into this module, obtain the probability distribution of each sample, and obtain the final recognition and classification prediction results.

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