A forestry pest and disease monitoring method and system based on machine learning

Abstract

The present application provides a forestry pest and disease monitoring method and system based on machine learning. By performing principal component analysis on forestry pest and disease images, multiple identification indicators of pests and diseases in the target forestry area are obtained. Based on all the identification indicators, effective feature vectors are determined for image recognition of forestry pests and diseases, and then the primary correlation features of the pest and disease images in the target forestry area are determined by the effective feature vectors. Multiple environmental impact factors are extracted from the monitoring image, and the secondary correlation features of the pest and disease images in the target forestry area are determined by all the environmental impact factors. The primary correlation features and the secondary correlation features are interactively fused to obtain multi-level distribution features for image recognition of pests and diseases. The current distribution of pests and diseases in the target forestry area is identified based on the multi-level distribution features by a pest and disease recognition model. The solution of the present application can realize diversified monitoring of forestry pests and diseases in a complex background, thereby reducing the false detection rate of forestry pests and diseases.

Description

Technical Field

[0001] The present application relates to the field of machine learning technology, and more specifically, to a forestry pest and disease monitoring method and system based on machine learning. Background Art

[0002] Forest pests and diseases have a significant impact on the health and productivity of forest ecosystems. They can lead to tree death, biodiversity decline, and economic losses. Timely detection and control of pests and diseases are key to protecting forest resources and ecological balance. Machine learning can not only accurately identify and predict forest pests and diseases, but also provide efficient and accurate decision-making support in large-scale forest management, thereby significantly improving the management efficiency of forestry resources and reducing the economic losses caused by pests and diseases.

[0003] The monitoring of forest pests and diseases based on machine learning mainly collects and analyzes data in forest ecosystems (such as image data, sensor data, environmental data, etc.), and then uses machine learning models to automatically identify and predict the occurrence of pests and diseases. Although monitoring based on machine learning has improved the efficiency of pest and disease identification to a certain extent, it still faces challenges such as data quality and model generalization ability. In existing technologies, the types and appearance characteristics of forest pests and diseases are diverse and variable. For example, a pest will show different forms in different growth stages or seasons, and the emergence of foreign pests and diseases will introduce new types, which are not included in existing models. The diversity and variability of characteristic data of different types of pests and diseases place high generalization requirements on machine learning models, making it difficult for existing models to effectively adapt to new types and changes of pests and diseases, and prone to missed detections or false detections. In addition, due to the complex backgrounds, diverse types of pests and diseases, and unstable quality of monitoring images faced in forestry areas, the accuracy and comprehensiveness of identifying different types of pests and diseases through monitoring images are insufficient, making it difficult to fully identify various types of pests and diseases in forestry. Therefore, how to achieve diversified monitoring of forestry pests and diseases under complex backgrounds, thereby reducing the false detection rate of forestry pests and diseases, has become a difficult problem faced by the industry. Summary of the Invention

[0004] The present application provides a forestry pest and disease monitoring method and system based on machine learning, which can realize diversified monitoring of forestry pests and diseases in complex backgrounds, thereby reducing the false detection rate of forestry pests and diseases.

[0005] In a first aspect, the present application provides a forestry pest and disease monitoring method based on machine learning, comprising the following steps:

[0006] Obtain historical forestry pest and disease images of the target forestry area;

[0007] Performing principal component analysis on the historical forestry pest and disease images based on a machine learning model to obtain multiple identification indicators of pests and diseases in the target forestry area, determining effective feature vectors for image recognition of forestry pests and diseases based on all the identification indicators, and then determining primary correlation features of the pest and disease images in the target forestry area using the effective feature vectors;

[0008] Conduct real-time monitoring of the target forestry area and collect current monitoring images of the target forestry area, extract multiple environmental influencing factors during the disease and insect pest monitoring process in the target forestry area based on the perception scene of the monitoring images, and then determine the secondary correlation features of the disease and insect pest images in the target forestry area based on all the environmental influencing factors;

[0009] The primary correlation features and the secondary correlation features are interactively integrated using a tree model to obtain multi-level distribution features for image recognition of pests and diseases in a target forestry area;

[0010] A pest and disease identification model is pre-trained through machine learning, and the pest and disease identification model identifies the current pest and disease distribution in the target forestry area based on the multi-level distribution characteristics.

[0011] In some embodiments, principal component analysis is performed on the historical forestry pest and disease images based on a machine learning model to obtain multiple identification indicators of pests and diseases in the target forestry area, specifically including:

[0012] For each pest and disease image in the historical forestry pest and disease images;

[0013] Preprocessing the pest and disease image to obtain a preprocessed pest and disease image;

[0014] Determining the multi-dimensional features of forestry pests and diseases in the pest and disease images based on the pre-processed pest and disease images through a machine learning model, thereby obtaining the multi-dimensional features of forestry pests and diseases in each pest and disease image;

[0015] A plurality of principal components of the historical forestry pest and disease images are extracted from all the multi-dimensional features, and the extracted principal components are then used as identification indicators of the pest and disease in the target forestry area.

[0016] In some embodiments, determining effective feature vectors for image recognition of forestry pests and diseases based on all recognition indicators specifically includes:

[0017] Get the variance of the principal component corresponding to each identification indicator;

[0018] Determine the variance contribution corresponding to each identification indicator through the variance of the principal component corresponding to each identification indicator;

[0019] The effective feature vector for image recognition of forest pests and diseases is determined based on all variance contributions.

[0020] In some embodiments, determining the primary correlation features of the pest and disease images in the target forestry area using the effective feature vector specifically includes:

[0021] Determining the correlation between each identification indicator in the effective feature vector and the distribution of forest pests and diseases;

[0022] The first-level correlation features of the pest and disease images in the target forestry area are determined based on all the correlations.

[0023] In some embodiments, the extraction of multiple environmental influencing factors during the pest and disease monitoring process in the target forestry area based on the perception scene of the monitoring image specifically includes:

[0024] Segmenting the monitoring image into different perception areas based on the perception scene of the monitoring image;

[0025] Extract pixel-level features of each perception area;

[0026] By combining all pixel-level features with external environmental data, multiple environmental influencing factors in the process of pest and disease monitoring in the target forestry area are determined.

[0027] In some embodiments, the tree model is used to interactively fuse the primary correlation features and the secondary correlation features to obtain multi-level distribution features for image recognition of pests and diseases in the target forestry area, specifically including:

[0028] Determining the interaction rate between the first-level correlation feature and the second-level correlation feature using a tree model;

[0029] The first-level association features and the second-level association features are fused based on the interaction rate to obtain multi-level distribution features for image recognition of pests and diseases in the target forestry area.

[0030] In some embodiments, the pest and disease identification model identifies the current pest and disease distribution in the target forestry area based on the multi-level distribution characteristics, specifically including:

[0031] Using the current monitoring image of the target forestry area as the input of the pest and disease identification model;

[0032] The pest and disease identification model is used to confidently identify the pests and diseases in the target forestry area based on the multi-level distribution characteristics to obtain the current pest and disease distribution results of the target forestry area.

[0033] In a second aspect, the present application provides a forestry pest and disease monitoring system based on machine learning, comprising:

[0034] An acquisition module is used to obtain historical forestry pest and disease images of the target forestry area;

[0035] a processing module for performing principal component analysis on the historical forestry pest and disease images based on a machine learning model to obtain multiple identification indicators of pests and diseases in the target forestry area, determining effective feature vectors for image recognition of forestry pests and diseases based on all the identification indicators, and further determining primary correlation features of the pest and disease images in the target forestry area using the effective feature vectors;

[0036] The processing module is further configured to monitor the target forestry area in real time, collect current monitoring images of the target forestry area, extract multiple environmental influencing factors during the disease and insect pest monitoring process in the target forestry area based on the perception scene of the monitoring images, and then determine the secondary correlation features of the disease and insect pest images in the target forestry area based on all the environmental influencing factors;

[0037] The processing module is further configured to interactively fuse the primary correlation features and the secondary correlation features using a tree model to obtain multi-level distribution features for image recognition of pests and diseases in the target forestry area;

[0038] An execution module is used to pre-train a pest and disease monitoring model through machine learning, and the pest and disease monitoring model identifies the current pest and disease distribution in the target forestry area based on the multi-level distribution characteristics.

[0039] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned forestry pest and disease monitoring method based on machine learning.

[0040] In a fourth aspect, the present application provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the computer implements the above-mentioned forestry pest and disease monitoring method based on machine learning.

[0041] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0042] In the present application, historical forestry pest and disease images of a target forestry area are obtained; principal component analysis is performed on the historical forestry pest and disease images based on a machine learning model to obtain multiple identification indicators of pests and diseases in the target forestry area, and an effective feature vector is determined for image recognition of forestry pests and diseases according to all the identification indicators, and then the primary correlation features of the pest and disease images in the target forestry area are determined through the effective feature vector; the target forestry area is monitored in real time, and the current monitoring image of the target forestry area is collected, and multiple environmental influencing factors in the pest and disease monitoring process of the target forestry area are extracted based on the perception scene of the monitoring image, and then the secondary correlation features of the pest and disease images in the target forestry area are determined from all the ring influencing factors; the primary correlation features and the secondary correlation features are interactively fused using a tree model to obtain multi-level distribution features for image recognition of pests and diseases in the target forestry area; a pest and disease recognition model is pre-trained through machine learning, and the pest and disease recognition model identifies the current pest and disease distribution in the target forestry area based on the multi-level distribution features.

[0043] It can be seen that in this application, firstly, the primary correlation features of the pest and disease images in the target forestry area are determined by the effective feature vectors when performing image recognition on forestry pests and diseases, which can help the pest and disease recognition model to better classify and identify forestry pests and diseases, and improve the accuracy of comprehensive recognition of forestry pests and diseases; secondly, the secondary correlation features of the pest and disease images in the target forestry area are determined by the environmental influencing factors in the process of pest and disease monitoring in the target forestry area, and the environmental influencing factors can effectively supplement the feature information of the pest and disease images, so as to better understand the influence of external factors on the distribution of pests and diseases; then, the primary correlation features and the secondary correlation features are interactively fused, the primary correlation features mainly focus on the morphological and behavioral characteristics of the pests and diseases themselves, and the secondary ... The features take into account the impact of the external environment on pests and diseases. Through interactive fusion, the image features of forestry pests and diseases and environmental information can be combined to provide more dimensional clues for forestry pest and disease identification. The multi-level distribution features obtained by interactive fusion can provide more comprehensive forestry pest and disease distribution information, thereby improving the pest and disease recognition model's ability to identify various pests and diseases, so as to realize diversified monitoring of forestry pests and diseases under complex backgrounds; finally, the pest and disease recognition model completed by machine learning pre-training performs confident identification of pests and diseases in the target forestry area based on the multi-level distribution features, which can reduce the false detection rate of pests and diseases in the target forestry area; in summary, this scheme can realize diversified monitoring of forestry pests and diseases under complex backgrounds, thereby reducing the false detection rate of forestry pests and diseases. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0045] Figure 1 is an exemplary flow chart of a forestry pest and disease monitoring method based on machine learning according to some embodiments of the present application;

[0046] Figure 2 is an exemplary flow chart of determining a valid feature vector according to some embodiments of the present application;

[0047] Figure 3 is an exemplary flow chart of determining primary correlation features according to some embodiments of the present application;

[0048] Figure 4 1 is a schematic structural diagram of a forestry pest and disease monitoring system based on machine learning according to some embodiments of the present application;

[0049] Figure 5 It is a structural diagram of a computer device for implementing a forestry pest and disease monitoring method based on machine learning as shown in some embodiments of the present application. DETAILED DESCRIPTION

[0050] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0051] refer to Figure 1 , which is an exemplary flow chart of a forestry pest and disease monitoring method based on machine learning according to some embodiments of the present application. The forestry pest and disease monitoring method 100 based on machine learning mainly includes the following steps:

[0052] In step 101, historical forestry pest and disease images of a target forestry area are obtained.

[0053] In specific implementation, all pest and disease images of the target forestry area in the past year can be obtained from the forestry pest and disease image resource library, and all the obtained pest and disease images are combined into historical forestry pest and disease images of the target forestry area. In other embodiments, other methods can also be used for acquisition, which is not specifically limited here.

[0054] It should be noted that the historical forestry pest and disease images in this application are images of all forestry pests and diseases collected in the past in the target forestry area. In addition, the forestry pest and disease image resource library in this application includes various types of pest and disease images in the target forestry area collected in the past by equipment such as drones, satellite images or fixed cameras. Among them, the type, location and severity of the pests and diseases in each pest and disease image are marked, which will not be repeated here.

[0055] In step 102, principal component analysis is performed on the historical forestry pest and disease images based on the machine learning model to obtain multiple identification indicators of pests and diseases in the target forestry area. The effective feature vector for image recognition of forestry pests and diseases is determined based on all the identification indicators, and the first-level correlation features of the pest and disease images in the target forestry area are determined through the effective feature vector.

[0056] In some embodiments, principal component analysis of the historical forestry pest and disease images based on a machine learning model to obtain multiple identification indicators of pests and diseases in the target forestry area can be achieved by the following steps:

[0057] For each pest and disease image in the historical forestry pest and disease images;

[0058] Preprocessing the pest and disease image to obtain a preprocessed pest and disease image;

[0059] Determining the multi-dimensional features of forestry pests and diseases in the pest and disease images based on the pre-processed pest and disease images through a machine learning model, thereby obtaining the multi-dimensional features of forestry pests and diseases in each pest and disease image;

[0060] A plurality of principal components of the historical forestry pest and disease images are extracted from all the multi-dimensional features, and the extracted principal components are then used as identification indicators of the pest and disease in the target forestry area.

[0061] In specific implementation, the pest and disease image is preprocessed to obtain the preprocessed pest and disease image, which can be achieved in the following ways, namely: the pest and disease image can be denoised (for example, smoothed by Gaussian filtering), image size adjusted (for example, adjusting the image size to a fixed 224x224 pixels), color space converted (for example, converting the pest and disease image from RGB space to other color spaces (such as HSV)), etc., to obtain the preprocessed pest and disease image. Other methods can also be used for implementation in other embodiments, which are not limited here. In addition, it should be noted that the goal of preprocessing in this application is to clearly display the key features of pests and diseases in forestry pest and disease images, remove noise, and reduce interference from irrelevant information, thereby preparing for subsequent feature extraction and principal component analysis.

[0062] In specific implementation, the multi-dimensional features of forestry pests and diseases in the pest and disease image are determined based on the pre-processed pest and disease image by a machine learning model. This can be achieved in the following manner: first, the color change features in the forestry pest and disease image can be captured using a color histogram or the distribution of other color spaces (such as HSV). Secondly, the texture features in the forestry pest and disease image are extracted through gray-level co-occurrence matrix (GLCM), local binary pattern (LBP), etc. Then, the geometric shape features of the pests and diseases in the forestry pest and disease image are extracted through morphological operations (such as edge detection and contour extraction), such as the area, perimeter, and shape moment of the contour of the pest and disease. Finally, different types of features (i.e., the color change features, texture features, and geometric shape features obtained above) are merged into a feature vector through a machine learning model, and the obtained result is used as the multi-dimensional feature of the forestry pests and diseases in the pest and disease image, thereby obtaining the multi-dimensional feature of the pests and diseases in each pest and disease image. In other embodiments, other methods can also be used for determination, which is not limited here.

[0063] It should be noted that the multi-dimensional features in this application are feature vectors that describe various information of forestry pests and diseases in pest and disease images, including feature dimensions such as color, texture, and shape.

[0064] In specific implementation, multiple principal components of the historical forestry pest and disease images are extracted from all multi-dimensional features, and then the extracted principal components are used as identification indicators of pests and diseases in the target forestry area. This can be achieved in the following manner, namely: first, the multi-dimensional features corresponding to all forestry pest and disease images are integrated into a feature matrix, wherein each column in the feature matrix represents a feature dimension, and the value of each row is the eigenvalue of each pest and disease image in the historical forestry pest and disease images under the corresponding feature dimension; then, the principal component analysis method is used to reduce the dimension of the feature matrix to obtain multiple principal components of the historical forestry pest and disease images (each principal component has a corresponding variance, which represents the contribution of the corresponding principal component to the data variability); then, the extracted principal components are used as identification indicators of pests and diseases in the target forestry area; other methods may also be used for determination in other embodiments, which are not limited here.

[0065] It should be noted that the recognition indicators in this application represent the discriminative pest and disease features in historical forestry pest and disease images. Therefore, the recognition indicators of pests and diseases in the target forestry area can provide effective input data for subsequent pest and disease classification, identification and other tasks.

[0066] In some embodiments, reference Figure 2 As shown in FIG, this figure is an exemplary flow chart for determining effective feature vectors in some embodiments of the present application. In this embodiment, determining effective feature vectors for image recognition of forestry pests and diseases based on all recognition indicators can be achieved by using the following steps:

[0067] First, in step 1021, the variance of the principal component corresponding to each identification index is obtained;

[0068] Next, in step 1022, the variance contribution corresponding to each identification indicator is determined by the variance of the principal component corresponding to each identification indicator;

[0069] Finally, in step 1023, effective feature vectors for image recognition of forestry pests and diseases are determined based on all variance contributions.

[0070] In specific implementation, the variance contribution in this application reflects the importance of the principal component in the pest and disease index in the overall data. The larger the variance contribution, the higher the importance of the principal component in the pest and disease index in the overall data, and the smaller the variance contribution, the lower the importance of the principal component in the pest and disease index in the overall data; the variance contribution corresponding to each identification indicator can be determined by the variance of the principal component corresponding to each identification indicator, which can be achieved in the following way, namely: output the principal component corresponding to each identification indicator through the explained_variance_ratio in the principal component analysis The ratio of the variance of the component to the variance of all principal components, and the obtained ratio is used as the variance contribution of each identification indicator. In other embodiments, other methods can also be used for determination, which is not limited here; determining the effective feature vector for image recognition of forestry pests and diseases based on all variance contributions can be achieved in the following way, namely: the first few identification indicators with larger variance contributions (i.e., the identification indicators corresponding to the principal components that can explain more than 95% of the variance) can be selected to form the effective feature vector for image recognition of forestry pests and diseases. In other embodiments, other methods can also be used for determination, which is not limited here.

[0071] It should be noted that the effective feature vector in this application is a feature that is valuable for the task of forestry pest and disease image recognition. It includes a combination of the most significant features (such as color, texture, shape, etc.) in the forestry pest and disease image. The said effective feature vector can improve the efficiency and accuracy of forestry pest and disease recognition.

[0072] In some embodiments, determining the primary correlation features of the pest and disease images in the target forestry area using the effective feature vectors can be achieved by using the following steps:

[0073] Determining the correlation between each identification indicator in the effective feature vector and the distribution of forest pests and diseases;

[0074] The first-level correlation features of the pest and disease images in the target forestry area are determined based on all the correlations.

[0075] In specific implementation, the correlation of this application reflects the degree of relationship between the identification indicators in the effective feature vector and the distribution of forestry pests and diseases. The higher the correlation, the closer the relationship between the identification indicators in the effective feature vector and the distribution of forestry pests and diseases. On the contrary, the lower the correlation, the more distant the relationship between the identification indicators in the effective feature vector and the distribution of forestry pests and diseases. Determining the correlation between each identification indicator in the effective feature vector and the distribution of forestry pests and diseases can be achieved in the following way, namely: the degree of association between each identification indicator in the effective feature vector and the distribution of forestry pests and diseases can be evaluated by an evaluation algorithm, and the result of the evaluation is used as the correlation between each identification indicator in the effective feature vector and the distribution of forestry pests and diseases. Among them, the evaluation algorithm is, for example, genetic algorithm, ensemble learning and reinforcement learning, etc. In other embodiments, other methods can also be used for implementation, which is not limited here.

[0076] In specific implementation, the following method can be used to determine the primary correlation features of the pest and disease images in the target forestry area based on all the correlations, namely: first, a correlation threshold is set based on prior experience and historical experimental data. The correlation threshold is used to select important features (i.e., identification indicators) with higher correlation in the effective feature vector. Usually, the correlation threshold is set to 0.5. Then, all the correlations are compared with the correlation threshold, and then the identification indicators with absolute values ​​of correlations in the effective feature vector greater than the correlation threshold are extracted. Finally, all the extracted identification indicators are combined into the primary correlation features of the pest and disease images in the target forestry area. Figure 3 As shown, this figure is an exemplary flow chart for determining the primary correlation features in some embodiments of the present application. In other embodiments, other methods may also be used for determination, which is not limited here.

[0077] It should be noted that the first-level correlation features in this application are image features that are representative of the characteristics of various forest pests and diseases when performing image recognition on them. The first-level correlation features can improve the accuracy of comprehensive recognition of forest pests and diseases.

[0078] In step 103, the target forestry area is monitored in real time, and the current monitoring image of the target forestry area is collected. Based on the perception scene of the monitoring image, multiple environmental influencing factors in the process of pest and disease monitoring in the target forestry area are extracted, and then the secondary correlation features of the pest and disease images in the target forestry area are determined by all the ring influencing factors.

[0079] In specific implementation, the target forestry area is monitored in real time. The current image of the target forestry area can be collected in real time by image acquisition devices such as cameras or drones as the current monitoring image of the target forestry area. In other embodiments, other methods can also be used for collection, which is not limited here.

[0080] It should be noted that the monitoring images in this application are images used to monitor and identify pests and diseases in the target forestry area.

[0081] In some embodiments, extracting multiple environmental influencing factors during pest and disease monitoring in a target forestry area based on the perceived scene of the monitoring image can be achieved by using the following steps:

[0082] Segmenting the monitoring image into different perception areas based on the perception scene of the monitoring image;

[0083] Extract pixel-level features of each perception area;

[0084] By combining all pixel-level features with external environmental data, multiple environmental influencing factors in the process of pest and disease monitoring in the target forestry area are determined.

[0085] In specific implementation, segmenting the monitoring image into different perception areas based on the perception scene of the monitoring image can be achieved in the following manner, namely, segmenting the monitoring image into different perception areas based on the perception scene of the monitoring image through an image segmentation algorithm (such as U-Net, Mask R-CNN), wherein the perception areas include, for example, healthy vegetation, damaged vegetation, soil, shadows, and other areas; extracting pixel-level features of each perception area can be achieved in the following manner, namely, performing image analysis on each perception area through a convolutional neural network in deep learning to extract pixel-level features of each perception area, wherein the pixel-level features include: color (such as RGB value, HSV component, etc., which reflects the visual information of the perception area), texture (such as contrast, correlation, energy, etc., which is used to describe the surface texture of the perception area), spectral reflectance (the reflectance of each pixel in a specific band, which reflects the health status of vegetation in the perception area), etc. In other embodiments, other methods can also be used for determination, which is not limited here.

[0086] It should be noted that the perception area in this application reflects the regionalized structural information in the monitoring image, such as vegetation distribution, bare land and shadows in the monitoring image; in addition, the pixel-level features represent the detail information in the perception area, that is, the attribute value of a single pixel in the perception area (such as color, texture or spectral value).

[0087] In specific implementation, the following method can be used to determine multiple environmental influencing factors in the process of pest and disease monitoring in the target forestry area by combining all pixel-level features with external environmental data, namely: first, use the weather API (WeatherForecast API) (such as OpenWeatherMap, WeatherStack) to obtain external environmental data with a collection time consistent with the current monitoring image of the target forestry area, and the external environmental data include external environmental factors such as temperature, humidity, precipitation, slope, and slope direction. Then, through correlation analysis based on all pixel-level features and the external environmental data, the weight value of each external environmental factor in the external environmental data affecting the distribution (i.e., growth and spread) of forestry pests and diseases in the target forestry area is calculated. Finally, the obtained results are combined into multiple environmental influencing factors in the process of pest and disease monitoring in the target forestry area. In other embodiments, other methods can also be used for determination, which is not limited here.

[0088] It should be noted that the environmental impact factors in this application represent the key environmental conditions that affect the distribution of forestry pests and diseases in the target forestry area.

[0089] In some embodiments, determining the secondary correlation features of the pest and disease images in the target forestry area based on all ring impact factors can be achieved by using the following steps:

[0090] Determine the impact of each external environmental factor in the target forestry area on the characteristics of forestry pests and diseases through machine learning and each environmental impact factor;

[0091] The secondary correlation features of pest and disease images in the target forestry area are determined through all the influencing factors.

[0092] In specific implementation, the influence amount corresponding to each environmental influencing factor when identifying the characteristics of forestry pests and diseases in each external environmental factor of the target forestry area through machine learning and each environmental influencing factor can be achieved in the following manner, namely: a model of the mapping relationship between each environmental influencing factor and the identification characteristics of forestry pests and diseases can be trained based on historical experience data and each environmental influencing factor through machine learning methods (such as multivariate regression, random forest, XGBoost). The model is used to describe the degree of influence of each external environment on the identification of forestry pests and diseases in the target forestry area, and then each environmental influencing factor is used as the input of the model. The degree of influence of the model output on the identification characteristics of forestry pests and diseases under each environmental influencing factor is corresponded as the influence amount corresponding to each environmental influencing factor when each external environmental factor of the target forestry area identifies the characteristics of forestry pests and diseases. In other embodiments, other methods can also be used for determination, which is not limited here.

[0093] It should be noted that the impact value in this application reflects the degree of influence of external environmental factors on the identification characteristics of forestry pests and diseases. The larger the impact value, the greater the degree of influence of external environmental factors on the identification characteristics of forestry pests and diseases. The smaller the impact value, the smaller the degree of influence of external environmental factors on the identification characteristics of forestry pests and diseases.

[0094] In specific implementation, the following method can be used to determine the secondary correlation features of the pest and disease images in the target forestry area through all the influence quantities, namely: based on all the influence quantities, environmental influence factors with significant influence (i.e., influence quantities higher than 80%) are selected, and then the correlation image features corresponding to the selected environmental influence factors and forestry pests and diseases are used as the secondary correlation features of the pest and disease images in the target forestry area. For example: for each external environmental factor, the correlation image features corresponding to the external environmental factors with all environmental influence factors higher than 80% and the forestry pest and disease features (i.e., the vector formed by the color features of the forestry pest and disease images when the external environmental factors change) are used as the secondary correlation features of the pest and disease images in the target forestry area. In other embodiments, other methods can also be used for determination, which is not limited here.

[0095] It should be noted that the secondary correlation features in this application represent image features in which pests and diseases are significantly correlated with the external environment in forestry pest and disease images. They can reflect the distribution patterns of forestry pests and diseases affected by the external environment. In addition, the secondary correlation features can enhance the robustness of the forestry pest and disease recognition model and reflect the indirect impact of the external environment on the distribution of forestry pests and diseases.

[0096] In step 104, the primary correlation features and the secondary correlation features are interactively fused using a tree model to obtain multi-level distribution features for image recognition of pests and diseases in the target forestry area.

[0097] In some embodiments, the interactive fusion of the primary correlation features and the secondary correlation features using a tree model to obtain multi-level distribution features for image recognition of pests and diseases in a target forestry area can be achieved by the following steps:

[0098] Determining the interaction rate between the first-level correlation feature and the second-level correlation feature using a tree model;

[0099] The first-level association features and the second-level association features are fused based on the interaction rate to obtain multi-level distribution features for image recognition of pests and diseases in the target forestry area.

[0100] It should be noted that the interaction rate in the present application represents the degree of complementarity of the fusion between the first-level association features and the second-level association features. As a preferred embodiment, the interaction rate between the first-level association features and the second-level association features can be determined using a tree model in the following manner, namely: a tree-based model (such as a random forest) can be used to evaluate the relationship strength between the first-level association features and the second-level association features, and the obtained evaluation result is used as the interaction rate between the first-level association features and the second-level association features. In other embodiments, other methods can also be used for determination, which is not specifically limited here.

[0101] In specific implementation, the first-level association features and the second-level association features are fused based on the interaction rate to obtain multi-level distribution features for image recognition of pests and diseases in the target forestry area. This can be achieved in the following manner, namely: the first-level association features and the second-level association features can be automatically fused based on the interaction rate through a feature fusion method, and the fused feature vectors are then reshaped into a matrix form (such as a two-dimensional array) suitable for convolution operations, thereby using the obtained matrix form (such as a two-dimensional array) as the multi-level distribution features for image recognition of pests and diseases in the target forestry area. The feature fusion method is, for example, a deep learning feature fusion method. Other methods can also be used for determination in other embodiments, which are not limited here.

[0102] It should be noted that the multi-level distribution features in this application are matrices that characterize the characteristic laws of the distribution of forestry pests and diseases at different image spatial scales. The multi-level distribution features can improve the ability of the forestry pest and disease recognition model to capture complex features.

[0103] In step 105, a pest and disease identification model is pre-trained through machine learning, and the pest and disease identification model identifies the current pest and disease distribution in the target forestry area based on the multi-level distribution characteristics.

[0104] It should be noted that the pest and disease identification model in the present application is a model for monitoring and identifying the distribution of pests and diseases in the target forestry area. It has a strong ability to identify the characteristics of pests and diseases. As a preferred embodiment, the pest and disease identification model can be pre-trained based on a general large-scale forestry pest and disease monitoring data set through a machine learning model (such as a convolutional neural network), and then the pest and disease identification model can be made to learn to identify the characteristics of forestry pests and diseases through an optimization algorithm, thereby completing the pre-training of the pest and disease identification model. In other embodiments, other methods can also be used for pre-training, which is not limited here.

[0105] In some embodiments, the pest and disease identification model can identify the current pest and disease distribution in the target forestry area based on the multi-level distribution characteristics by using the following steps:

[0106] Using the current monitoring image of the target forestry area as the input of the pest and disease identification model;

[0107] The pest and disease identification model is used to confidently identify the pests and diseases in the target forestry area based on the multi-level distribution characteristics to obtain the current pest and disease distribution results of the target forestry area.

[0108] In specific implementation, the pests and diseases in the target forestry area are confidently identified based on the multi-level distribution characteristics by the pest and disease identification model, and the current pest and disease distribution results of the target forestry area are obtained in the following manner, namely: the current monitoring image of the target forestry area is input into the pest and disease identification model, and then the confidence of the pest and disease category in each area in the current monitoring image of the target forestry area is determined by the pest and disease identification model based on the multi-level distribution characteristics, and a pest and disease distribution map is generated (that is, the forestry pest and disease classification results of each area), so as to obtain the current pest and disease distribution results of the target forestry area, and finally, the obtained pest and disease distribution results are sent to forestry management personnel or a forestry pest and disease monitoring platform.

[0109] In addition, in another aspect of the present application, in some embodiments, the present application provides a forestry pest monitoring system based on machine learning, referring to Figure 4 , which is a schematic diagram of the structure of a forestry pest and disease monitoring system based on machine learning according to some embodiments of the present application. The forestry pest and disease monitoring system based on machine learning 400 includes: an acquisition module 401, a processing module 402 and an execution module 403, which are described as follows:

[0110] Acquisition module 401, in this application, acquisition module 401 is mainly used to acquire historical forestry pest and disease images of the target forestry area;

[0111] Processing module 402, in this application, is mainly used to perform principal component analysis on the historical forestry pest and disease images based on a machine learning model to obtain multiple identification indicators of pests and diseases in the target forestry area, determine effective feature vectors for image recognition of forestry pests and diseases based on all the identification indicators, and then determine the primary correlation features of the pest and disease images in the target forestry area through the effective feature vectors;

[0112] The processing module 402 in the present application is further configured to perform real-time monitoring of the target forestry area, collect current monitoring images of the target forestry area, extract multiple environmental influencing factors during the disease and insect pest monitoring process in the target forestry area based on the perception scene of the monitoring images, and then determine secondary correlation features of the disease and insect pest images in the target forestry area based on all the environmental influencing factors;

[0113] The processing module 402 in the present application is further used to interactively fuse the primary correlation features and the secondary correlation features using a tree model to obtain multi-level distribution features for image recognition of pests and diseases in the target forestry area;

[0114] Execution module 403, in this application, execution module 403 is mainly used to pre-train a pest and disease identification model through machine learning, and the pest and disease identification model identifies the current pest and disease distribution in the target forestry area based on the multi-level distribution characteristics.

[0115] The above describes in detail the examples of the forestry pest and disease monitoring method and system based on machine learning provided by the embodiments of the present application. It can be understood that, in order to realize the above functions, the corresponding device includes hardware structures and / or software modules corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to realize the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application.

[0116] In some embodiments, the present application also provides a computer device, which includes a memory and a processor, the memory is used to store computer programs, and the processor is used to call and run the computer programs from the memory, so that the computer device executes the above-mentioned forestry pest and disease monitoring method based on machine learning.

[0117] In some embodiments, reference Figure 5 , the dotted line in the figure indicates that the unit or module is optional. The figure is a structural diagram of a computer device for implementing the forestry pest monitoring method based on machine learning in the present application. The forestry pest monitoring method based on machine learning in the above embodiment can be Figure 5 The computer device 500 is implemented as shown, and the computer device 500 includes at least one processor 501, a memory 502 and at least one communication unit 505. The computer device 500 can be a terminal device, a server or a chip.

[0118] The processor 501 may be a general-purpose processor or a special-purpose processor. For example, the processor 501 may be a central processing unit (CPU), which may be used to control the computer device 500, execute software programs, and process data from the software programs. The computer device 500 may also include a communication unit 505 for inputting (receiving) and outputting (transmitting) signals.

[0119] For example, the computer device 500 may be a chip, the communication unit 505 may be an input and / or output circuit of the chip, or the communication unit 505 may be a communication interface of the chip, and the chip may be a component of a terminal device, a network device, or other device.

[0120] For another example, the computer device 500 may be a terminal device or a server, and the communication unit 505 may be a transceiver of the terminal device or the server, or the communication unit 505 may be a transceiver circuit of the terminal device or the server.

[0121] The computer device 500 may include one or more memories 502, on which a program 504 is stored. The program 504 can be executed by the processor 501 to generate instructions 503, so that the processor 501 executes the method described in the above method embodiment according to the instructions 503. Optionally, data (such as a target audit model) can also be stored in the memory 502. Optionally, the processor 501 can also read data stored in the memory 502. The data can be stored at the same storage address as the program 504, or at a different storage address from the program 504.

[0122] The processor 501 and the memory 502 may be provided separately or integrated together, for example, integrated on a system on chip (SOC) of a terminal device.

[0123] It should be understood that each step of the above method embodiment can be completed by a hardware-based logic circuit or software-based instructions in the processor 501. The processor 501 can be a CPU, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, such as discrete gates, transistor logic devices, or discrete hardware components.

[0124] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0125] For example, in some embodiments, the present application also provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the computer implements the above-mentioned forestry pest and disease monitoring method based on machine learning.

[0126] In summary, in the forestry pest and disease monitoring method and system based on machine learning disclosed in the embodiment of the present application, historical forestry pest and disease images of the target forestry area are obtained; principal component analysis is performed on the historical forestry pest and disease images based on the machine learning model to obtain multiple identification indicators of pests and diseases in the target forestry area, and the effective feature vector for image recognition of forestry pests and diseases is determined according to all the identification indicators, and then the first-level correlation features of the pest and disease images in the target forestry area are determined through the effective feature vector; the target forestry area is monitored in real time, and the current monitoring image of the target forestry area is collected, and based on the perception of the monitoring image The scene extracts multiple environmental influencing factors during the pest and disease monitoring process in the target forestry area, and then determines the secondary correlation features of the pest and disease images in the target forestry area by all the ring influencing factors; uses the tree model to interactively fuse the primary correlation features and the secondary correlation features to obtain multi-level distribution features for image recognition of pests and diseases in the target forestry area; pre-trains the pest and disease recognition model through machine learning, and the pest and disease recognition model identifies the current pest and disease distribution in the target forestry area based on the multi-level distribution features; can realize diversified monitoring of forest pests and diseases under complex backgrounds, thereby reducing the false detection rate of forest pests and diseases.

[0127] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0128] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A forestry pest and disease monitoring method based on machine learning, characterized in that: The steps include: Obtain historical forestry pest and disease images of the target forestry area; Performing principal component analysis on the historical forestry pest and disease images based on a machine learning model to obtain multiple identification indicators of pests and diseases in the target forestry area, determining effective feature vectors for image recognition of forestry pests and diseases based on all the identification indicators, and then determining primary correlation features of the pest and disease images in the target forestry area using the effective feature vectors; Conduct real-time monitoring of the target forestry area and collect current monitoring images of the target forestry area, extract multiple environmental influencing factors during the disease and insect pest monitoring process in the target forestry area based on the perception scene of the monitoring images, and then determine the secondary correlation features of the disease and insect pest images in the target forestry area based on all the environmental influencing factors; The primary correlation features and the secondary correlation features are interactively integrated using a tree model to obtain multi-level distribution features for image recognition of pests and diseases in a target forestry area; Pre-training a pest and disease identification model through machine learning, wherein the pest and disease identification model identifies the current pest and disease distribution in the target forestry area based on the multi-level distribution characteristics; The tree model is used to interactively fuse the primary correlation features and the secondary correlation features to obtain multi-level distribution features for image recognition of pests and diseases in the target forestry area, specifically including: Determining the interaction rate between the first-level correlation feature and the second-level correlation feature using a tree model; The first-level correlation feature and the second-level correlation feature are fused based on the interaction rate to obtain a multi-level distribution feature for image recognition of pests and diseases in the target forestry area; The interaction rate represents the degree of complementarity between the first-level correlation features and the second-level correlation features.

2. The method according to claim 1, wherein Based on the machine learning model, principal component analysis is performed on the historical forestry pest and disease images to obtain multiple identification indicators of pests and diseases in the target forestry area, including: For each pest and disease image in the historical forestry pest and disease images; Preprocessing the pest and disease image to obtain a preprocessed pest and disease image; Determining the multi-dimensional features of forestry pests and diseases in the pest and disease images based on the pre-processed pest and disease images through a machine learning model, thereby obtaining the multi-dimensional features of forestry pests and diseases in each pest and disease image; A plurality of principal components of the historical forestry pest and disease images are extracted from all the multi-dimensional features, and the extracted principal components are then used as identification indicators of the pest and disease in the target forestry area.

3. The method according to claim 1, wherein According to all the identification indicators, the effective feature vectors for image recognition of forest pests and diseases are determined to include: Get the variance of the principal component corresponding to each identification indicator; Determine the variance contribution corresponding to each identification indicator through the variance of the principal component corresponding to each identification indicator; The effective feature vector for image recognition of forest pests and diseases is determined based on all variance contributions.

4. The method according to claim 1, wherein Determining the primary correlation features of the pest and disease images in the target forestry area by using the effective feature vector specifically includes: Determining the correlation between each identification indicator in the effective feature vector and the distribution of forest pests and diseases; The first-level correlation features of the pest and disease images in the target forestry area are determined based on all the correlations.

5. The method according to claim 1, wherein The multiple environmental influencing factors extracted based on the perception scene of the monitoring image during the pest and disease monitoring process in the target forestry area specifically include: Segmenting the monitoring image into different perception areas based on the perception scene of the monitoring image; Extract pixel-level features of each perception area; By combining all pixel-level features with external environmental data, multiple environmental influencing factors in the process of pest and disease monitoring in the target forestry area are determined.

6. The method according to claim 1, wherein The pest and disease identification model identifies the current pest and disease distribution in the target forestry area based on the multi-level distribution characteristics, specifically including: Using the current monitoring image of the target forestry area as the input of the pest and disease identification model; The pest and disease identification model is used to confidently identify the pests and diseases in the target forestry area based on the multi-level distribution characteristics to obtain the current pest and disease distribution results of the target forestry area.

7. A forestry pest and disease monitoring system based on machine learning, which uses the method according to any one of claims 1 to 6 to monitor forestry pests and diseases, characterized in that: The system includes: An acquisition module is used to obtain historical forestry pest and disease images of the target forestry area; a processing module for performing principal component analysis on the historical forestry pest and disease images based on a machine learning model to obtain multiple identification indicators of pests and diseases in the target forestry area, determining effective feature vectors for image recognition of forestry pests and diseases based on all the identification indicators, and further determining primary correlation features of the pest and disease images in the target forestry area using the effective feature vectors; The processing module is further configured to monitor the target forestry area in real time, collect current monitoring images of the target forestry area, extract multiple environmental influencing factors during the disease and insect pest monitoring process in the target forestry area based on the perception scene of the monitoring images, and then determine the secondary correlation features of the disease and insect pest images in the target forestry area based on all the environmental influencing factors; The processing module is further configured to interactively fuse the primary correlation features and the secondary correlation features using a tree model to obtain multi-level distribution features for image recognition of pests and diseases in the target forestry area; An execution module is used to pre-train a pest and disease monitoring model through machine learning, and the pest and disease monitoring model identifies the current pest and disease distribution in the target forestry area based on the multi-level distribution characteristics.

8. A computer device, characterized in that: The computer device includes a memory and a processor, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the forestry pest and disease monitoring method based on machine learning as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions or codes, and when the instructions or codes are executed on a computer, the computer implements the forestry pest and disease monitoring method based on machine learning as described in any one of claims 1 to 6.

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