Artificial Intelligence-Based Method and System for Identifying and Analyzing Salt-Tolerant Mulberry Varieties

Abstract

The present invention discloses a method and system for identifying and analyzing salt-tolerant mulberry varieties based on artificial intelligence, including: obtaining mulberry tree image data of different mulberry varieties at each growth stage and performing preprocessing, extracting attribute features and performing category division to construct a first topological structure diagram; obtaining growth image data of each mulberry variety in different physiological environments, performing stress feature analysis and constructing a second topological structure diagram; evaluating the salt tolerance of various mulberry trees based on the first topological structure diagram and the second topological structure diagram to obtain salt tolerance evaluation information; constructing a mulberry variety identification and analysis model, using the first topological structure diagram, the second topological structure diagram and the salt tolerance evaluation information to establish a mulberry knowledge graph and perform model training, identifying and analyzing the mulberry trees to be identified, and determining whether they are suitable for planting in the expected planting area. The accuracy and comprehensiveness of the identification of salt-tolerant mulberry varieties are improved and the breeding and identification cycle is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of mulberry variety identification, and in particular to an artificial intelligence-based salt-alkali tolerant mulberry variety identification and analysis method and system. Background Art

[0002] In the current agricultural development process, the improvement and utilization of saline-alkali land has become one of the important research directions in the world. However, due to the significant restrictions of saline-alkali stress on plant growth, the selection and cultivation of salt-alkali-tolerant crop varieties with strong adaptability is an important means to effectively improve the utilization efficiency of saline-alkali land. Under saline-alkali stress, the physiological characteristics and morphological features of plants will change significantly, such as changes in leaf color, thickening of surface texture, increase in dry areas, and shrinkage of plant morphology.

[0003] Traditionally, the identification of salt-alkali tolerant mulberry varieties is often carried out through expert experience and other methods, which has subjective problems and the problem of being unable to quantify salt-alkali tolerance, thus making it difficult to select and breed varieties for saline-alkali land. Therefore, how to improve the accuracy and comprehensiveness of the identification of salt-alkali tolerant mulberry varieties, thereby reducing the identification cycle of breeding work and providing certain guidance is an urgent problem to be solved. Summary of the invention

[0004] The present invention overcomes the defects of the prior art and provides an artificial intelligence-based identification and analysis method and system for salt-alkali tolerant mulberry varieties, the important purpose of which is to improve the accuracy and comprehensiveness of the identification of salt-alkali tolerant mulberry varieties.

[0005] To achieve the above-mentioned purpose, the first aspect of the present invention provides a method for identifying and analyzing salt-alkali tolerant mulberry varieties based on artificial intelligence, comprising:

[0006] Acquire mulberry tree image data of different varieties of mulberry trees at various growth stages and perform preprocessing, extract attribute features of the preprocessed mulberry tree images and perform category classification, and construct a first topological structure diagram;

[0007] Acquire growth image data of each mulberry tree variety in different physiological environments in the first topological structure diagram, analyze stress characteristics of the corresponding mulberry tree variety under each physiological environment, and construct a second topological structure diagram;

[0008] Based on the first topological structure diagram and the second topological structure diagram, the salt-alkali tolerance of each type of mulberry tree under different physiological environments is evaluated to obtain salt-alkali tolerance evaluation information;

[0009] Constructing a mulberry variety identification and analysis model, using the first topological structure diagram, the second topological structure diagram and the salt-alkali tolerance evaluation information to establish a mulberry knowledge graph and perform model training, identifying and analyzing the mulberry trees to be identified, and obtaining mulberry tree identification and analysis information;

[0010] Judge whether the mulberry variety to be identified is suitable for planting in the expected planting area according to the mulberry tree identification and analysis information.

[0011] In this solution, obtaining the mulberry tree image data of different mulberry varieties at each growth stage and performing preprocessing, extracting the attribute features of the preprocessed mulberry tree images and performing category division, and constructing the first topological structure diagram specifically includes:

[0012] Obtain the mulberry tree image data of different mulberry varieties at each growth stage, input the obtained mulberry tree image data into a Gaussian filter for smoothing filtering, and perform wavelet transform on the filtered mulberry tree image data;

[0013] Decompose the filtered image into sub-bands of different scales and frequencies through wavelet transform, and perform threshold processing through a preset processing threshold, and set the wavelet coefficients smaller than the processing threshold to zero;

[0014] After completing the threshold processing, extract the high-frequency wavelet coefficients, perform linear enhancement on the extracted high-frequency wavelet coefficients by using a linear enhancement method, and perform wavelet coefficient merging based on the enhanced high-frequency wavelet coefficients;

[0015] Obtain the merged wavelet coefficients and perform inverse wavelet transform, reconstruct the sub-band information of different scales and frequencies to obtain the reconstructed mulberry tree image, and extract the attribute features according to the reconstructed mulberry tree image to obtain the attribute feature information;

[0016] The attribute feature information is the variety attribute and growth stage attribute corresponding to each reconstructed mulberry tree image. Generate the attribute data labels of each reconstructed image according to the attribute feature information, and perform category division on each reconstructed image in combination with the clustering algorithm to generate the first data set;

[0017] Introduce the ORB image extraction algorithm to calculate the features of the mulberry tree images of each category in the first data set and extract the image features to obtain the feature vectors of each mulberry variety category at different growth stages;

[0018] Construct a directed description relationship with the first node of the mulberry tree species and the second node of the growth stage, connect the first node and the second node to form a topological structure diagram, and associate the feature vectors of each mulberry variety category at different growth stages to obtain the first topological structure diagram.

[0019] In this solution, obtaining the growth image data of each mulberry variety in different physiological environments in the first topological structure diagram, analyzing the stress characteristics of the corresponding mulberry variety in each physiological environment, and constructing the second topological structure diagram specifically includes:

[0020] Based on big data retrieval, obtain the growth image data of each mulberry variety in different physiological environments in the first topological structure diagram, and perform image preprocessing on the obtained growth image data;

[0021] Obtain the physiological environment characteristics of mulberry trees in each growth image from the preprocessed growth image data, calculate the Euclidean distance values between the physiological environment characteristics corresponding to each image, and use the Euclidean distance values to classify the preprocessed growth images to obtain several subsets of physiological environment categories;

[0022] Use the color moment method to calculate the color moments of each channel in the RGB three channels of each growth image in each category subset, including the first-order moment, the second-order moment, and the third-order moment, and connect the calculated color moments to form the color features of the corresponding growth image;

[0023] Extract the morphological features of each growth image in each physiological environment category subset based on the machine vision algorithm, where the morphological features include leaf texture features, leaf edge features, and leaf area features;

[0024] Construct the stress features of each category subset through the extracted color features and morphological features, which are used to characterize the survival performance of the corresponding mulberry tree varieties in each physiological environment category subset, and obtain stress feature information;

[0025] Extract the physiological environment characteristics of each physiological environment category subset, build a second topological structure diagram with the mulberry tree species as the first node and the physiological environment characteristics as the second node, and associate the stress feature information with the second topological structure diagram.

[0026] In this solution, evaluate the saline-alkali tolerance of various categories of mulberry trees in different physiological environments based on the first topological structure diagram and the second topological structure diagram to obtain saline-alkali tolerance evaluation information, specifically including:

[0027] Obtain the first topological structure diagram and the second topological structure diagram, extract the image features of various categories of mulberry trees in different growth stages based on the first topological structure diagram, and input them into the generative adversarial network for adversarial training;

[0028] Let the generative adversarial network learn the normal survival performance characteristics of various categories of mulberry trees through the input image features of various categories of mulberry trees in different growth stages, and output the trained generative adversarial network;

[0029] Obtain the survival performance characteristics of various categories of mulberry trees in different physiological environments according to the second topological structure diagram, input them into the trained generative adversarial network for reconstruction analysis, and obtain the reconstructed survival performance characteristics of various categories of mulberry trees in different physiological environments;

[0030] Calculate the reconstruction error between the reconstructed survival performance characteristics of various categories of mulberry trees in different physiological environments and the initial input survival performance characteristics of various categories of mulberry trees in different physiological environments;

[0031] Set several reconstruction error intervals and associate the reconstruction error intervals with the saline-alkali tolerance ability levels. Evaluate the saline-alkali tolerance abilities of various categories of mulberry trees in different physiological environments through the calculated reconstruction errors, and obtain saline-alkali tolerance ability evaluation information.

[0032] In this solution, for the construction of the mulberry tree variety identification and analysis model, a mulberry tree knowledge graph is established and the model is trained by using the first topological structure diagram, the second topological structure diagram and the saline-alkali tolerance ability evaluation information, and the mulberry tree to be identified is identified and analyzed. Specifically, it includes:

[0033] Obtain the first topological structure diagram, the second topological structure diagram and the saline-alkali tolerance ability evaluation information. Extract the survival performance characteristics of each mulberry tree variety at different growth stages based on the first topological structure diagram, and extract the survival performance characteristics of each mulberry tree variety in different physiological environments based on the second topological structure diagram;

[0034] Fuse the survival performance characteristics of each mulberry tree variety at different growth stages with the survival performance characteristics of each mulberry tree variety in different physiological environments to obtain the survival performance characteristics of each mulberry tree variety at each growth stage in different physiological environments;

[0035] Construct a mulberry tree knowledge graph with the association path of mulberry tree variety - physiological environment - saline-alkali tolerance ability according to the survival performance characteristics of each mulberry tree variety at each growth stage in different physiological environments and the saline-alkali tolerance ability evaluation information;

[0036] Use a dual-channel convolutional neural network to construct a mulberry tree variety identification and analysis model. Based on the mulberry tree knowledge graph, construct a training data set to perform deep learning and training on the mulberry tree variety identification and analysis model to obtain a mulberry tree variety identification and analysis model that meets the expectations;

[0037] Obtain the information of the mulberry tree to be identified and the information of the expected planting area, and input them into the mulberry tree variety identification and analysis model to identify and analyze the currently unknown mulberry tree. Extract the image features of the mulberry tree to be identified through the first channel, and extract the regional environment features of the expected planting area through the second channel;

[0038] Input the extracted image features and regional environment features into the set parameter-sharing fully connected layer for weighted fusion, and perform variety identification and saline-alkali tolerance ability scoring on the mulberry tree to be identified according to the fused features to obtain mulberry tree identification and analysis information.

[0039] In this solution, for judging whether the mulberry tree variety to be identified is suitable for planting in the expected planting area according to the mulberry tree identification and analysis information, it specifically includes:

[0040] Obtain the mulberry tree identification and analysis information, extract the saline-alkali tolerance ability score of the target mulberry tree variety in the current expected planting area based on the mulberry tree identification and analysis information, and make a judgment with a preset threshold;

[0041] If the salinity tolerance score of the target mulberry variety in the current expected planting area is greater than the preset threshold, indicating that the target mulberry variety is suitable for planting in the current expected planting area, then recommended planting information is generated.

[0042] If the salinity tolerance score of the target mulberry variety in the current expected planting area is less than the preset threshold, indicating that the target mulberry variety is not suitable for planting in the current expected planting area, then non-recommended planting information is generated.

[0043] The second aspect of the present invention provides an artificial intelligence-based mulberry variety salinity tolerance identification and analysis system, which includes: a memory and a processor. The memory contains an artificial intelligence-based mulberry variety salinity tolerance identification and analysis method program. When the artificial intelligence-based mulberry variety salinity tolerance identification and analysis method program is executed by the processor, the following steps are implemented:

[0044] Obtain mulberry tree image data of different mulberry varieties at each growth stage and perform preprocessing, extract the attribute features of the preprocessed mulberry tree images and perform category division, and construct a first topological structure diagram.

[0045] Obtain the growth image data of each mulberry variety in the first topological structure diagram in different physiological environments, analyze the stress characteristics of the corresponding mulberry varieties in each physiological environment, and construct a second topological structure diagram.

[0046] Evaluate the salinity tolerance of various mulberry varieties in different physiological environments based on the first topological structure diagram and the second topological structure diagram to obtain salinity tolerance evaluation information.

[0047] Construct a mulberry variety identification and analysis model, use the first topological structure diagram, the second topological structure diagram and the salinity tolerance evaluation information to establish a mulberry knowledge graph and perform model training, identify and analyze the mulberry tree to be identified, and obtain mulberry identification and analysis information.

[0048] Judge whether the mulberry variety to be identified is suitable for planting in the expected planting area according to the mulberry identification and analysis information.

[0049] The present invention discloses a method and system for identifying and analyzing salt-tolerant mulberry varieties based on artificial intelligence, including: obtaining mulberry tree image data of different mulberry varieties at each growth stage, preprocessing the data, extracting attribute features and classifying them to construct a first topological structure diagram; obtaining growth image data of each mulberry variety in different physiological environments, analyzing stress features and constructing a second topological structure diagram; evaluating the salt tolerance of various mulberry categories based on the first topological structure diagram and the second topological structure diagram to obtain salt tolerance evaluation information; constructing a mulberry variety identification and analysis model, using the first topological structure diagram, the second topological structure diagram and the salt tolerance evaluation information to establish a mulberry knowledge graph and conduct model training, identifying and analyzing the mulberry to be identified, and determining whether it is suitable for planting in the desired planting area. The accuracy and comprehensiveness of the identification of salt-tolerant mulberry varieties are improved, and the breeding and identification cycle is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments or exemplary examples of the present invention, the following will briefly introduce the drawings required for use in the embodiments or exemplary descriptions. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to these drawings shown without creative efforts.

[0051] Figure 1 It is a flowchart of a method for identifying and analyzing salt-tolerant mulberry varieties based on artificial intelligence provided by an embodiment of the present invention;

[0052] Figure 2 It is a flowchart of a method for planting and analyzing salt-tolerant mulberry varieties provided by an embodiment of the present invention;

[0053] Figure 3 It is a block diagram of a system for identifying and analyzing salt-tolerant mulberry varieties based on artificial intelligence provided by an embodiment of the present invention;

[0054] The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] In order to be able to more clearly understand the above objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0056] Many specific details are set forth in the following description in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0057] Figure 1 Flowchart of a method for identifying and analyzing salt-tolerant mulberry varieties based on artificial intelligence provided by an embodiment of the present invention;

[0058] As Figure 1 shown, the present invention provides a flowchart of a method for identifying and analyzing salt-tolerant mulberry varieties based on artificial intelligence, including:

[0059] S102, obtaining mulberry tree image data of different mulberry varieties at each growth stage, preprocessing the obtained data, extracting the attribute features of the preprocessed mulberry tree images, classifying the features, and constructing a first topological structure diagram;

[0060] S104, obtaining the growth image data of each mulberry variety in the first topological structure diagram under different physiological environments, analyzing the stress characteristics of the corresponding mulberry varieties under each physiological environment, and constructing a second topological structure diagram;

[0061] S106, evaluating the salt tolerance of various mulberry varieties under different physiological environments based on the first topological structure diagram and the second topological structure diagram to obtain salt tolerance evaluation information;

[0062] S108, constructing a mulberry variety identification and analysis model, using the first topological structure diagram, the second topological structure diagram, and the salt tolerance evaluation information to establish a mulberry knowledge graph and perform model training, identifying and analyzing the mulberry to be identified, and obtaining mulberry identification and analysis information;

[0063] S110, judging whether the mulberry variety to be identified is suitable for planting in the expected planting area according to the mulberry identification and analysis information.

[0064] It should be noted that the present invention provides a method for identifying and analyzing salt-tolerant mulberry varieties based on artificial intelligence. By obtaining the leaf and plant images of mulberry trees under salt stress, using image processing technology to extract multi-dimensional phenotypic data such as color, texture, morphology, and edge features, and then combining multi-source data such as soil salinity and environmental conditions for comprehensive analysis, the mulberry variety is identified and analyzed. The stress characteristics represent the survival performance of this mulberry variety under different physiological environments. By analyzing the difference between the survival performance and the survival performance under the normal suitable environment, the salt tolerance is evaluated, and thus the salt tolerance evaluation result is obtained. Subsequently, a mulberry variety identification and analysis model is established using a dual-channel convolutional neural network, and a mulberry knowledge graph is established using the first topological structure diagram, the second topological structure diagram, and the salt tolerance evaluation information to identify and analyze the mulberry to be identified, and obtain mulberry identification and analysis information. According to the mulberry identification and analysis information, it is judged whether the mulberry variety to be identified is suitable for planting in the expected planting area. The accuracy of the identification result is improved.

[0065] Further, in a preferred embodiment of the present invention, acquiring mulberry tree image data of different mulberry tree varieties at each growth stage, preprocessing the acquired mulberry tree image data, extracting the attribute features of the preprocessed mulberry tree images and classifying them, and constructing a first topological structure diagram specifically includes:

[0066] Acquire mulberry tree image data of different mulberry tree varieties at each growth stage, input the acquired mulberry tree image data into a Gaussian filter for smoothing filtering, and perform wavelet transform on the filtered mulberry tree image data;

[0067] Decompose the filtered image into sub-bands of different scales and frequencies through wavelet transform, and perform threshold processing through a preset processing threshold, and set the wavelet coefficients smaller than the processing threshold to zero;

[0068] After completing the threshold processing, extract the high-frequency wavelet coefficients, perform linear enhancement on the extracted high-frequency wavelet coefficients using a linear enhancement method, and perform wavelet coefficient merging based on the enhanced high-frequency wavelet coefficients;

[0069] Acquire the merged wavelet coefficients and perform inverse wavelet transform, reconstruct the sub-band information of different scales and frequencies to obtain a reconstructed mulberry tree image, and extract attribute features according to the reconstructed mulberry tree image to obtain attribute feature information;

[0070] The attribute feature information is the variety attribute and growth stage attribute corresponding to each reconstructed mulberry tree image. Generate attribute data labels for each reconstructed image according to the attribute feature information, and classify each reconstructed image by combining a clustering algorithm to generate a first data set;

[0071] Introduce the ORB image extraction algorithm to calculate the features of the mulberry tree images of each category in the first data set and extract the image features to obtain the feature vectors of each mulberry tree variety category at different growth stages;

[0072] Construct a directed description relationship with the first node as the mulberry tree species and the second node as the growth stage, connect the first node and the second node to form a topological structure diagram, and associate the feature vectors of each mulberry tree variety category at different growth stages to obtain a first topological structure diagram.

[0073] It should be noted that, first, high-resolution image data of different mulberry varieties at each growth stage are obtained and input into a Gaussian filter for smoothing processing. Smoothing filtering can effectively reduce random noise in the image while retaining the main structure and characteristic information of the mulberry image. Wavelet transform is performed on the filtered image data to decompose the image into sub-bands of different scales and frequencies. The multi-scale analysis feature of wavelet transform can hierarchically analyze different information in the image, thereby separating the detail information (high-frequency components) from the global structure (low-frequency components). The wavelet coefficients are processed through a preset processing threshold, and the wavelet coefficients below the threshold are set to zero to remove weak signals or residual noise. After the threshold processing is completed, the high-frequency wavelet coefficients are extracted and enhanced using a linear enhancement method to improve the visibility and resolution of details in the image. After the wavelet coefficients are merged, the sub-band information of different scales and frequencies is reconstructed through inverse wavelet transform to obtain the reconstructed mulberry image. These reconstructed images more clearly retain the important information of the mulberry variety and growth stage. Next, attribute feature information is extracted from the reconstructed images, including the variety attribute and growth stage attribute corresponding to the image, for generating the attribute data label of the reconstructed image. Combining with a clustering algorithm, the reconstructed images with similar features are classified to form the first data set. The ORB (Oriented FAST and Rotated BRIEF) image extraction algorithm is introduced to calculate and extract the key features of the mulberry tree images of each category in the first data set, obtaining the feature vectors of different mulberry varieties at each growth stage, which are used to describe the growth forms of different categories of mulberry trees at different stages. Subsequently, a topological structure diagram is constructed, with the mulberry variety category as the first node and the growth stage as the second node, and the nodes are connected according to the directed relationship to form a topological structure describing the association relationship between the mulberry variety and the growth stage. Finally, the above-extracted image feature vectors are associated with the topological structure diagram to generate the first topological structure diagram, providing support for subsequent variety identification and analysis.

[0074] Further, in a preferred embodiment of the present invention, the growth image data of each mulberry variety in different physiological environments in the first topological structure diagram are obtained, the stress characteristics of the corresponding mulberry varieties in each physiological environment are analyzed, and a second topological structure diagram is constructed, specifically including:

[0075] Based on big data retrieval, the growth image data of each mulberry variety in different physiological environments in the first topological structure diagram are obtained, and image preprocessing is performed on the obtained growth image data;

[0076] The physiological environment characteristics of the mulberry trees in each growth image are obtained through the preprocessed growth image data, the Euclidean distance values between the physiological environment characteristics corresponding to each image are calculated, and the preprocessed growth images are classified using the Euclidean distance values to obtain several subsets of physiological environment categories;

[0077] Calculate the color moments of each channel in the RGB three channels of each growth image in each category subset using the color moment method, including the first moment, the second moment, and the third moment, and connect the calculated color moments to form the color features of the corresponding growth image;

[0078] Extract the morphological features of each growth image in each physiological environment category subset based on the machine vision algorithm, and the morphological features include leaf texture features, leaf edge features, and leaf area features;

[0079] Construct the stress features of each category subset through the extracted color features and morphological features, which are used to characterize the survival performance of the corresponding mulberry varieties in each physiological environment category subset, and obtain the stress feature information;

[0080] Extract the physiological environment features of each physiological environment category subset, build a second topological structure diagram with the mulberry species as the first node and the physiological environment features as the second node, and associate the stress feature information with the second topological structure diagram.

[0081] It should be noted that first, big data technology is used to retrieve and obtain the growth image data of different mulberry tree varieties in various physiological environments in the first topological structure diagram. The characteristics reflecting the performance of mulberry trees in different physiological environments are extracted from each image. These physiological environment characteristics include light, humidity, temperature, saline-alkali concentration, etc. By calculating the Euclidean distance values between the physiological environment characteristics corresponding to each image, their similarity or difference is quantified, and the growth images are automatically classified according to the Euclidean distance values, forming several subsets of physiological environment categories with similar environmental conditions. For each subset of physiological environment categories, the color moment method is used to analyze the color distribution characteristics of each growth image, and the first moment, second moment, and third moment in the RGB three color channels of each image are calculated respectively to quantify the color average value, standard deviation, skewness, and other information of each channel. The calculated color moment feature values are connected to form the overall color feature of each image, which can comprehensively reflect the color changes of leaves under different physiological environments. At the same time, morphological features are extracted from each growth image based on machine vision algorithms, including the texture features, edge features, and area features of the leaves. Among them, the texture features capture the surface roughness and lesion texture distribution of the leaves through methods such as gray-level co-occurrence matrix; the edge features depict the degree of curling or damage of the leaf shape through edge detection algorithms; the leaf area features are used to quantify the impact of saline-alkali stress on the growth of mulberry trees. Furthermore, the stress feature information of each subset of physiological environment categories is generated through these color and morphological features, which is used to characterize the survival status and adaptability performance of the corresponding mulberry tree varieties in each subset of categories. Next, the physiological environment characteristics of each subset of physiological environment categories are correlated with the mulberry tree species information. A second topological structure diagram is built with the mulberry tree variety as the first node and the physiological environment characteristics as the second node, and the stress feature information extracted from each category subset is correlated with the second topological structure diagram to form a comprehensive analysis framework for quantifying and evaluating the performance of mulberry tree varieties under different environmental conditions.

[0082] Further, in a preferred embodiment of the present invention, the saline-alkali tolerance ability of each category of mulberry trees in different physiological environments is evaluated based on the first topological structure diagram and the second topological structure diagram to obtain saline-alkali tolerance ability evaluation information, which specifically includes:

[0083] The first topological structure diagram and the second topological structure diagram are obtained, and the image features of each category of mulberry trees in different growth stages are extracted based on the first topological structure diagram and input into the generative adversarial network for adversarial training;

[0084] The normal survival performance characteristics of each category of mulberry trees are learned in the generative adversarial network through the input image features of each category of mulberry trees in different growth stages, and the trained generative adversarial network is output;

[0085] Obtain the survival performance characteristics of various categories of mulberry trees in different physiological environments according to the second topological structure diagram, and input them into the trained generative adversarial network for reconstruction analysis to obtain the reconstructed survival performance characteristics of various categories of mulberry trees in different physiological environments;

[0086] Calculate the reconstruction error between the reconstructed survival performance characteristics of various categories of mulberry trees in different physiological environments and the survival performance characteristics of the initial input of various categories of mulberry trees in different physiological environments;

[0087] Set several reconstruction error intervals and associate the reconstruction error intervals with the saline-alkali tolerance ability levels, and evaluate the saline-alkali tolerance ability of various categories of mulberry trees in different physiological environments through the calculated reconstruction errors to obtain the saline-alkali tolerance ability evaluation information.

[0088] It should be noted that based on the first topological structure diagram, the image features of various categories of mulberry trees in different growth stages are extracted. These features are specifically manifested as color features, texture features, morphological features, etc., comprehensively reflecting the multi-dimensional attributes of the growth state of mulberry trees. The extracted image features are then used as inputs and fed into a generative adversarial network (GAN) for adversarial training. In the generative adversarial network, the generator and the discriminator conduct dynamic games. The generator attempts to simulate and generate real-like mulberry tree image features, while the discriminator is responsible for distinguishing real data from generated data. During the adversarial training process, the network continuously iterates and learns, enabling the generator to gradually master the normal survival performance characteristics of various categories of mulberry trees in different growth stages. Finally, the trained generative adversarial network is output, and its generation ability can reflect the normal survival state of mulberry trees at different stages. Next, use the survival performance characteristics of various categories of mulberry trees recorded in the second topological structure diagram and input them into the trained generative adversarial network for reconstruction analysis. During this analysis process, the network attempts to generate the reconstructed data it believes based on the input features, that is, it tends to generate normal survival performance characteristics. Subsequently, calculate the reconstruction error between the reconstructed survival performance characteristics of various categories of mulberry trees in different physiological environments and their initial input features. The reconstruction error reflects the difference between the growth state of the corresponding mulberry tree variety in each physiological environment and the normal condition, that is, it represents the saline-alkali tolerance ability. Set several reconstruction error intervals and associate the reconstruction error intervals with the saline-alkali tolerance ability levels, and evaluate the saline-alkali tolerance ability of various categories of mulberry trees in different physiological environments through the calculated reconstruction errors, and finally output the saline-alkali tolerance ability evaluation information. Thus, it can efficiently and accurately quantify the saline-alkali tolerance levels of different mulberry tree varieties, and also provide a scientific basis for variety screening and cultivation strategies, contributing to meeting the actual needs in saline-alkali land agricultural development.

[0089] Further, in a preferred embodiment of the present invention, for constructing the mulberry variety identification and analysis model, a mulberry knowledge graph is established and model training is carried out by using the first topological structure diagram, the second topological structure diagram and the saline-alkali tolerance ability evaluation information, and the mulberry to be identified is identified and analyzed, specifically including:

[0090] Obtain the first topological structure diagram, the second topological structure diagram and the saline-alkali tolerance ability evaluation information, extract the survival performance characteristics of each mulberry variety at different growth stages based on the first topological structure diagram, and extract the survival performance characteristics of each mulberry variety under different physiological environments based on the second topological structure diagram;

[0091] Fuse the survival performance characteristics of each mulberry variety at different growth stages with the survival performance characteristics of each mulberry variety under different physiological environments to obtain the survival performance characteristics of each mulberry variety at each growth stage under different physiological environments;

[0092] Construct a mulberry knowledge graph with the association path of mulberry variety - physiological environment - saline-alkali tolerance ability according to the survival performance characteristics of each mulberry variety at each growth stage under different physiological environments and the saline-alkali tolerance ability evaluation information;

[0093] Use a dual-channel convolutional neural network to construct a mulberry variety identification and analysis model, and perform deep learning and training on the mulberry variety identification and analysis model based on the constructed training data set of the mulberry knowledge graph to obtain a mulberry variety identification and analysis model that meets the expectations;

[0094] Obtain the information of the mulberry to be identified and the information of the expected planting area, input them into the mulberry variety identification and analysis model to identify and analyze the currently unknown mulberry, extract the image features of the mulberry to be identified through the first channel, and extract the regional environment features of the expected planting area through the second channel;

[0095] Input the extracted image features and regional environment features into the set parameter-sharing fully connected layer for weighted fusion, and perform variety identification and saline-alkali tolerance ability scoring on the mulberry to be identified according to the fused features to obtain the mulberry identification and analysis information.

[0096] It should be noted that based on the first topological structure diagram, the survival performance characteristics of each mulberry variety at different growth stages are extracted, and these characteristics reflect the performance of mulberry trees changing over time under normal conditions. At the same time, based on the second topological structure diagram, the survival performance characteristics of each mulberry variety in different physiological environments are extracted to reveal the impact of special conditions such as saline-alkali environments on its adaptability. By fusing the above two types of characteristics, the characteristics of each mulberry variety at different growth stages are combined with its performance in different physiological environments to generate more comprehensive information. Such fused characteristics can comprehensively characterize the survival performance of each mulberry variety under diverse environmental and growth conditions. Combining with the saline-alkali tolerance evaluation information, a knowledge graph with the core correlation paths of mulberry variety, physiological environment, and saline-alkali tolerance ability is constructed using these multi-dimensional characteristics. This knowledge graph systematically integrates the growth characteristics, environmental adaptability, and stress resistance ability of mulberry varieties, providing a structured knowledge basis for subsequent analysis. A mulberry variety identification and analysis model is designed using a dual-channel convolutional neural network (CNN). The data extracted from the mulberry knowledge graph is used as the training data set to perform deep learning training on the model to ensure that it can effectively identify and distinguish the characteristic performances of mulberry varieties, and a model with the desired performance is obtained. For the mulberry tree to be identified, by obtaining its image information and the environmental information of the target planting area, it is input into the trained identification and analysis model. The first channel of the model extracts the image characteristics of the mulberry tree to be identified through convolutional operations, such as leaf texture, edge shape, and color changes, etc.; the second channel extracts the environmental characteristics of the target planting area, such as parameters like soil salinity, humidity, and climate conditions. These characteristics are weighted and fused through a preset parameter-sharing fully connected layer to associate the biological characteristics of the mulberry tree with its environmental adaptability. The fused characteristics are further used for variety identification and saline-alkali tolerance ability scoring. The model accurately determines the specific variety of the mulberry tree to be identified based on the weights of different characteristics, and at the same time scores its adaptability in the target area to generate the final mulberry tree identification and analysis information. The accurate identification of mulberry varieties and the scientific evaluation of saline-alkali tolerance ability are realized, providing an efficient and intelligent solution for the selection of mulberry varieties for planting in saline-alkali lands.

[0097] Furthermore, in a preferred embodiment of the present invention, the judging whether the mulberry variety to be identified is suitable for planting in the desired planting area according to the mulberry tree identification and analysis information specifically includes:

[0098] Obtain the mulberry tree identification and analysis information, extract the saline-alkali tolerance ability score of the target mulberry variety in the current desired planting area based on the mulberry tree identification and analysis information, and make a judgment with a preset threshold;

[0099] If the saline-alkali tolerance ability score of the target mulberry variety in the current desired planting area is greater than the preset threshold, it means that the target mulberry variety is suitable for planting in the current desired planting area, and then generate recommended planting information;

[0100] If the salinity tolerance score of the target mulberry variety in the current desired planting area is less than the preset threshold, indicating that the target mulberry variety is not suitable for planting in the current desired planting area, then non-recommended planting information is generated.

[0101] It should be noted that first, the salinity tolerance score of the target mulberry variety in the current desired planting area is extracted by obtaining the mulberry identification and analysis information. This score comprehensively considers the characteristics of the mulberry variety and the environmental conditions of the desired area, reflecting the potential performance ability of the target variety under actual planting conditions. By comparing the score value with the preset threshold, it is determined whether the variety is suitable for planting in the target area. When the salinity tolerance score of the target mulberry variety is higher than the preset threshold, it means that the variety has strong adaptability and resistance to the saline-alkali conditions in the target area and can grow well in this area. Therefore, recommended planting information is generated to provide guidance for growers and suggest that they choose this variety for planting in the target area. On the contrary, if the score is lower than the preset threshold, it indicates that the target mulberry variety may have difficulty adapting to saline-alkali stress or other environmental limiting conditions in the current area and is not suitable for planting. At this time, non-recommended planting information will be generated to prompt growers that the variety has insufficient adaptability in the target area, thus avoiding waste of resources or failed planting attempts. Intelligently evaluating the environmental suitability of the planting area provides a scientific basis for planting decisions and improves the success rate and efficiency of mulberry planting projects.

[0102] Figure 2 Flowchart of the method for analyzing the planting of salt-tolerant mulberry varieties provided by an embodiment of the present invention;

[0103] As Figure 2 shown, the present invention provides a flowchart of the method for analyzing the planting of salt-tolerant mulberry varieties, including:

[0104] S202, obtain the information of the mulberry to be identified and the information of the desired planting area, input them into the mulberry variety identification and analysis model to identify and analyze the current unknown mulberry, and extract the image features of the mulberry to be identified and the regional environmental features of the desired planting area;

[0105] S204, input the extracted image features and regional environmental features into the set parameter-sharing fully connected layer for weighted fusion, and conduct variety identification and salinity tolerance score of the mulberry to be identified according to the fused features to obtain the mulberry identification and analysis information;

[0106] S206, extract the salinity tolerance score of the target mulberry variety in the current desired planting area based on the mulberry identification and analysis information, and make a judgment with the preset threshold;

[0107] S208, if the salinity tolerance score of the target mulberry variety in the current desired planting area is greater than the preset threshold, indicating that the target mulberry variety is suitable for planting in the current desired planting area, then generate recommended planting information;

[0108] S210. If the salinity tolerance score of the target mulberry variety in the current expected planting area is less than the preset threshold, it means that the target mulberry variety is not suitable for planting in the current expected planting area, and then information of not recommending planting is generated.

[0109] It should be noted that first, the information of the mulberry tree to be identified and the environmental information of the expected planting area are used as inputs and input into the pre-trained mulberry variety identification and analysis model. The model extracts mulberry tree image features through the first channel, such as the color, texture, edge characteristics, etc. of the leaves. At the same time, it extracts the environmental characteristics of the planting area through the second channel, including salinity, soil humidity, and climate conditions, etc. Next, the extracted image features and regional environmental features are input into a fully connected layer with shared parameters. This layer adopts a weighted fusion strategy to conduct correlation analysis on multi-source data and generate a comprehensive feature representation. Based on the fused comprehensive features, the variety classification and salinity tolerance score of the mulberry tree to be identified are carried out, and mulberry tree identification and analysis information is output. After completing the identification and analysis, the salinity tolerance score of the target mulberry variety in the current expected planting area is extracted. This score is obtained by synthesizing the variety characteristics and environmental adaptability output by the model and is the key basis for evaluating planting suitability. By comparing with the preset salinity tolerance threshold, it is judged whether the target variety is suitable for planting in the target area. If the score is higher than the threshold, it means that the variety has good adaptability in the expected planting area and can grow normally in the target environment, thus generating information of recommending planting and prompting the planter of the planting potential of this variety. On the contrary, if the score is lower than the threshold, it indicates that the adaptability of the target variety to the expected area is weak and it may be difficult to grow due to adverse environmental conditions, generating information of not recommending planting and prompting the planter to make a careful decision to avoid possible planting failures or waste of resources.

[0110] Figure 3 A mulberry tree salinity tolerance variety identification and analysis system 3 based on artificial intelligence provided by an embodiment of the present invention, the system includes: a memory 31 and a processor 32. The memory 31 contains a program of a mulberry tree salinity tolerance variety identification and analysis method based on artificial intelligence. When the program of the mulberry tree salinity tolerance variety identification and analysis method based on artificial intelligence is executed by the processor 32, the following steps are implemented:

[0111] Obtain mulberry tree image data of different varieties at each growth stage and perform preprocessing, extract the preprocessed mulberry tree image attribute features and perform category division, and construct a first topological structure diagram;

[0112] Obtain the growth image data of each mulberry tree variety in different physiological environments in the first topological structure diagram, analyze the stress characteristics of the corresponding mulberry tree varieties in each physiological environment, and construct a second topological structure diagram;

[0113] Evaluate the saline-alkali tolerance of various categories of mulberry trees in different physiological environments based on the first topological structure diagram and the second topological structure diagram to obtain saline-alkali tolerance evaluation information;

[0114] Construct a mulberry tree variety identification and analysis model, establish a mulberry tree knowledge graph using the first topological structure diagram, the second topological structure diagram, and the saline-alkali tolerance evaluation information, and perform model training to identify and analyze the mulberry tree to be identified, obtaining mulberry tree identification and analysis information;

[0115] Judge whether the mulberry tree variety to be identified is suitable for planting in the desired planting area according to the mulberry tree identification and analysis information.

[0116] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed with each other can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical, or other forms.

[0117] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units; they can be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0118] In addition, each functional unit in the embodiments of the present invention can be all integrated in a processing unit, or each unit can be separately used as a unit, or two or more units can be integrated in a unit; the above integrated unit can be implemented in the form of hardware, or in the form of a hardware plus a software functional unit.

[0119] Those of ordinary skill in the art can understand that all or part of the steps to implement the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above method embodiments; and the foregoing storage medium includes: mobile storage devices, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks, or optical disks and other various media that can store program codes.

[0120] Alternatively, if the above integrated units of the present invention are implemented in the form of software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as removable storage devices, ROM, RAM, magnetic disks, or optical discs.

[0121] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for identifying and analyzing salt-alkali tolerant mulberry varieties based on artificial intelligence, characterized in that: include: Acquire mulberry tree image data of different varieties of mulberry trees at various growth stages and perform preprocessing, extract attribute features of the preprocessed mulberry tree images and perform category classification, and construct a first topological structure diagram; Acquire growth image data of each mulberry tree variety in different physiological environments in the first topological structure diagram, analyze stress characteristics of the corresponding mulberry tree variety under each physiological environment, and construct a second topological structure diagram; Based on the first topological structure diagram and the second topological structure diagram, the salt-alkali tolerance of each type of mulberry tree under different physiological environments is evaluated to obtain salt-alkali tolerance evaluation information; Constructing a mulberry variety identification and analysis model, using the first topological structure diagram, the second topological structure diagram and the salt-alkali tolerance evaluation information to establish a mulberry knowledge graph and perform model training, identifying and analyzing the mulberry trees to be identified, and obtaining mulberry tree identification and analysis information; Determining whether the mulberry tree variety to be identified is suitable for planting in the desired planting area according to the mulberry tree identification analysis information; The method of obtaining mulberry tree image data of different varieties of mulberry trees at various growth stages and preprocessing them, extracting attribute features of the preprocessed mulberry tree images and classifying them into categories, and constructing a first topological structure diagram specifically includes: Acquire mulberry tree image data of different varieties of mulberry trees at various growth stages, input the acquired mulberry tree image data into a Gaussian filter for smoothing filtering, and perform wavelet transformation on the filtered mulberry tree image data; The filtered image is decomposed into sub-bands of different scales and frequencies by wavelet transform, and threshold processing is performed by a preset processing threshold, and the wavelet coefficients smaller than the processing threshold are set to zero; After completing the threshold processing, high-frequency wavelet coefficients are extracted, and the extracted high-frequency wavelet coefficients are linearly enhanced by a linear enhancement method, and wavelet coefficients are merged based on the enhanced high-frequency wavelet coefficients; Acquire the merged wavelet coefficients and perform inverse wavelet transform, reconstruct subband information of different scales and frequencies to obtain a reconstructed mulberry tree image, and extract attribute features based on the reconstructed mulberry tree image to obtain attribute feature information; The attribute feature information is the variety attribute and growth stage attribute corresponding to each reconstructed mulberry tree image, and the attribute data label of each reconstructed image is generated according to the attribute feature information, and each reconstructed image is classified into categories in combination with a clustering algorithm to generate a first data set; Introducing the ORB image extraction algorithm to perform feature calculation and extract image features on each category of mulberry tree images in the first data set, and obtain feature vectors of each mulberry tree variety category at different growth stages; A directed description relationship is constructed with the mulberry tree type as the first node and the growth stage as the second node, the first node and the second node are connected to form a topological structure diagram, and the characteristic vectors of each mulberry tree variety category at different growth stages are associated to obtain a first topological structure diagram.

2. The method for identifying and analyzing salt-alkali tolerant mulberry varieties based on artificial intelligence according to claim 1, characterized in that: The step of obtaining growth image data of each mulberry tree variety in different physiological environments in the first topological structure diagram, analyzing stress characteristics of the corresponding mulberry tree variety in each physiological environment, and constructing a second topological structure diagram specifically includes: Based on big data retrieval, the growth image data of each mulberry tree variety in the first topological structure diagram in different physiological environments are obtained, and image preprocessing is performed on the obtained growth image data; The physiological environment characteristics of the mulberry tree in each growth image are obtained through the preprocessed growth image data, the Euclidean distance value between the physiological environment characteristics corresponding to each image is calculated, and the preprocessed growth image is classified by using the Euclidean distance value to obtain a number of physiological environment category subsets; The color moment method is used to calculate the color moment of each channel in the RGB three-color channels of each growth image in each category subset, including the first-order moment, the second-order moment and the third-order moment, and the calculated color moments are connected to form the color features of the corresponding growth image; Extracting morphological features of each growth image in each physiological environment category subset based on a machine vision algorithm, wherein the morphological features include leaf texture features, leaf edge features, and leaf area features; The extracted color features and morphological features constitute the stress features of each category subset, which are used to characterize the survival performance of the corresponding mulberry varieties in each physiological environment category subset and obtain stress feature information; The physiological environment characteristics of each physiological environment category subset are extracted, a second topological structure diagram is constructed with the mulberry tree species as the first node and the physiological environment characteristics as the second node, and the stress characteristic information is associated with the second topological structure diagram.

3. The method for identifying and analyzing salt-alkali tolerant mulberry varieties based on artificial intelligence according to claim 1, characterized in that: The step of evaluating the salt-alkali tolerance of each type of mulberry tree under different physiological environments based on the first topological structure diagram and the second topological structure diagram to obtain salt-alkali tolerance evaluation information specifically includes: Obtaining a first topological structure diagram and a second topological structure diagram, extracting image features of mulberry trees of different categories at different growth stages based on the first topological structure diagram, and inputting the features into a generative adversarial network for adversarial training; By inputting the image features of mulberry trees of different categories at different growth stages, the generative adversarial network is made to learn the normal survival performance characteristics of mulberry trees of different categories, and the trained generative adversarial network is output; Obtaining survival performance characteristics of each category of mulberry trees under different physiological environments according to the second topological structure diagram, inputting them into the trained generative adversarial network for reconstruction analysis, and obtaining reconstructed survival performance characteristics of each category of mulberry trees under different physiological environments; Calculate the reconstruction error between the reconstructed survival performance characteristics of each category of mulberry trees under different physiological environments and the initial input survival performance characteristics of each category of mulberry trees under different physiological environments; Several reconstruction error intervals were set and associated with the salt-alkali tolerance levels. The salt-alkali tolerance of each category of mulberry trees under different physiological environments was evaluated through the calculated reconstruction errors, and the salt-alkali tolerance evaluation information was obtained.

4. The method for identifying and analyzing salt-alkali tolerant mulberry varieties based on artificial intelligence according to claim 1, characterized in that: The construction of the mulberry variety identification and analysis model, using the first topological structure diagram, the second topological structure diagram and the salt-alkali tolerance evaluation information to establish a mulberry knowledge graph and perform model training, and identifying and analyzing the mulberry trees to be identified, specifically includes: Obtaining a first topological structure diagram, a second topological structure diagram, and salt-alkali tolerance evaluation information, extracting survival performance characteristics of each mulberry tree variety at different growth stages based on the first topological structure diagram, and extracting survival performance characteristics of each mulberry tree variety under different physiological environments based on the second topological structure diagram; The survival performance characteristics of each mulberry tree variety at different growth stages are combined with the survival performance characteristics of each mulberry tree variety under different physiological environments to obtain the survival performance characteristics of each mulberry tree variety at each growth stage under different physiological environments; According to the survival performance characteristics of each mulberry variety at each growth stage under different physiological environments and the salt-alkali tolerance evaluation information, a mulberry knowledge graph with mulberry variety-physiological environment-salt-alkali tolerance as the association path is constructed; A mulberry variety identification and analysis model is constructed using a dual-channel convolutional neural network, and a training data set is constructed based on the mulberry knowledge graph to perform deep learning and training on the mulberry variety identification and analysis model to obtain a mulberry variety identification and analysis model that meets expectations; Obtaining information about the mulberry tree to be identified and information about the expected planting area, inputting the information into the mulberry tree variety identification and analysis model to identify and analyze the current unknown mulberry tree, extracting image features of the mulberry tree to be identified through a first channel, and extracting regional environmental features of the expected planting area through a second channel; The extracted image features and regional environmental features are input into the set parameter-sharing fully connected layer for weighted fusion. The variety of the mulberry trees to be identified and the salt-alkali tolerance are scored based on the fused features to obtain the mulberry tree identification analysis information.

5. The method for identifying and analyzing salt-alkali tolerant mulberry varieties based on artificial intelligence according to claim 1, characterized in that: The step of judging whether the mulberry tree variety to be identified is suitable for planting in the desired planting area according to the mulberry tree identification and analysis information specifically includes: Acquire mulberry tree identification and analysis information, extract the salt-alkali tolerance score of the target mulberry tree variety in the current expected planting area based on the mulberry tree identification and analysis information, and make a judgment with a preset threshold; If the salt-alkali tolerance score of the target mulberry variety in the current expected planting area is greater than the preset threshold, it means that the target mulberry variety is suitable for planting in the current expected planting area, and the recommended planting information is generated; If the salt-alkali tolerance score of the target mulberry variety in the current expected planting area is less than the preset threshold, it means that the target mulberry variety is not suitable for planting in the current expected planting area, and a non-planting recommendation information is generated.

6. An artificial intelligence-based identification and analysis system for salt-alkali tolerant mulberry varieties, characterized in that: The system comprises: a memory and a processor, wherein the memory contains an artificial intelligence-based identification and analysis method program for salt-alkali tolerant mulberry varieties, and when the artificial intelligence-based identification and analysis method program for salt-alkali tolerant mulberry varieties is executed by the processor, the following steps are implemented: Acquire mulberry tree image data of different varieties of mulberry trees at various growth stages and perform preprocessing, extract attribute features of the preprocessed mulberry tree images and perform category classification, and construct a first topological structure diagram; Acquire growth image data of each mulberry tree variety in different physiological environments in the first topological structure diagram, analyze stress characteristics of the corresponding mulberry tree variety under each physiological environment, and construct a second topological structure diagram; Based on the first topological structure diagram and the second topological structure diagram, the salt-alkali tolerance of each type of mulberry tree under different physiological environments is evaluated to obtain salt-alkali tolerance evaluation information; Constructing a mulberry variety identification and analysis model, using the first topological structure diagram, the second topological structure diagram and the salt-alkali tolerance evaluation information to establish a mulberry knowledge graph and perform model training, identifying and analyzing the mulberry trees to be identified, and obtaining mulberry tree identification and analysis information; Determining whether the mulberry tree variety to be identified is suitable for planting in the desired planting area according to the mulberry tree identification analysis information; The method of obtaining mulberry tree image data of different varieties of mulberry trees at various growth stages and preprocessing them, extracting attribute features of the preprocessed mulberry tree images and classifying them into categories, and constructing a first topological structure diagram specifically includes: Acquire mulberry tree image data of different varieties of mulberry trees at various growth stages, input the acquired mulberry tree image data into a Gaussian filter for smoothing filtering, and perform wavelet transformation on the filtered mulberry tree image data; The filtered image is decomposed into sub-bands of different scales and frequencies by wavelet transform, and threshold processing is performed by a preset processing threshold, and the wavelet coefficients smaller than the processing threshold are set to zero; After completing the threshold processing, high-frequency wavelet coefficients are extracted, and the extracted high-frequency wavelet coefficients are linearly enhanced by a linear enhancement method, and wavelet coefficients are merged based on the enhanced high-frequency wavelet coefficients; Acquire the merged wavelet coefficients and perform inverse wavelet transform, reconstruct subband information of different scales and frequencies to obtain a reconstructed mulberry tree image, and extract attribute features based on the reconstructed mulberry tree image to obtain attribute feature information; The attribute feature information is the variety attribute and growth stage attribute corresponding to each reconstructed mulberry tree image, and the attribute data label of each reconstructed image is generated according to the attribute feature information, and each reconstructed image is classified into categories in combination with a clustering algorithm to generate a first data set; Introducing the ORB image extraction algorithm to perform feature calculation and extract image features on each category of mulberry tree images in the first data set, and obtain feature vectors of each mulberry tree variety category at different growth stages; A directed description relationship is constructed with the mulberry tree type as the first node and the growth stage as the second node, the first node and the second node are connected to form a topological structure diagram, and the characteristic vectors of each mulberry tree variety category at different growth stages are associated to obtain a first topological structure diagram.

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