Intelligent orchard expert knowledge base construction method and system based on machine learning

Abstract

The invention provides an intelligent orchard expert knowledge base construction method and system based on machine learning, and the method comprises the following steps: collecting and preprocessing the related data of an orchard, and obtaining an initial knowledge base; constructing a knowledge representation model by adopting an ontology construction method based on the initial knowledge base, and obtaining standardized knowledge representation based on the knowledge representation model; based on the standardized knowledge representation, adopting machine learning reasoning to obtain a knowledge reasoning chain; based on the knowledge reasoning chain, learning a new planting case and expert feedback by adopting an incremental learning algorithm, and automatically updating the initial knowledge base to obtain an expert knowledge base; and based on the expert knowledge base, adopting a machine learning algorithm to obtain an orchard management decision, and completing construction of the intelligent orchard expert knowledge base based on machine learning. The intelligent level of orchard management can be remarkably improved, and powerful support is provided for precise planting.

Description

Technical Field

[0001] The present invention belongs to the technical field of machine learning, and in particular relates to a method and system for constructing a smart orchard expert knowledge base based on machine learning. Background Art

[0002] The construction of smart orchards requires the systematic and structured organization of the experience and knowledge of fruit tree planting experts to form a knowledge base that can be called by the intelligent decision-making system. However, in the construction process, a series of technical difficulties such as knowledge acquisition, representation and reasoning are faced. First, fruit tree planting involves knowledge in multiple fields such as environment, pest control, cultivation management, etc. The sources of knowledge in different fields are diverse and the forms of expression are different. There is a lack of unified collection and representation methods. Secondly, the growth cycle of fruit trees is long, and the influencing factors are complex and changeable. The experience and knowledge of planting experts are often subjective and uncertain, and it is difficult to directly convert them into a structured and computable form. In addition, there may be associations and constraints between different knowledge. It is necessary to establish a reasonable knowledge organization and reasoning mechanism to realize the effective application of knowledge. Therefore, the construction of the smart orchard knowledge base requires not only agricultural field knowledge, but also artificial intelligence technologies such as machine learning and knowledge engineering. By bridging the gap between agricultural knowledge and information technology, tapping implicit knowledge, and establishing a coherent, comprehensive and explainable knowledge base, a solid knowledge foundation is provided for the intelligent decision-making of smart orchards. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides a method and system for constructing a smart orchard expert knowledge base based on machine learning, which completes the smart management of the orchard by constructing an expert knowledge base that is updated in a timely manner.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A method for constructing a smart orchard expert knowledge base based on machine learning includes the following steps:

[0006] Collect and preprocess orchard-related data to obtain an initial knowledge base;

[0007] Based on the initial knowledge base, a knowledge representation model is constructed using an ontology construction method, and a standardized knowledge representation is obtained based on the knowledge representation model;

[0008] Based on the standardized knowledge representation, machine learning reasoning is used to obtain a knowledge reasoning chain;

[0009] Based on the knowledge reasoning chain, an incremental learning algorithm is used to learn new planting cases and expert feedback, and the initial knowledge base is automatically updated to obtain an expert knowledge base;

[0010] Based on the expert knowledge base, a machine learning algorithm is used to obtain orchard management decisions, thereby completing the construction of a smart orchard expert knowledge base based on machine learning.

[0011] Preferably, the orchard-related data include orchard meteorological data, soil temperature and humidity data, water and fertilizer application amounts, pest and disease control data, fruit tree growth cycles, and historical yields.

[0012] Preferably, the method for constructing the knowledge representation model is:

[0013] Based on natural language processing technology, knowledge segmentation, part-of-speech tagging and entity recognition are performed on the initial knowledge base to obtain knowledge keywords;

[0014] Classifying the knowledge keywords based on a clustering algorithm to obtain entities, and extracting attributes and relationships of each entity;

[0015] Based on the entities, the attributes and the relationships, an ontology construction method is adopted to obtain a knowledge formalization representation model;

[0016] Based on the knowledge formalization representation model, a graph embedding algorithm is used to learn the low-dimensional vector representation of the entity and the relationship to obtain the semantic representation of the knowledge keyword;

[0017] The knowledge representation model is obtained based on the semantic representation and the knowledge formalization representation model.

[0018] Preferably, the method for obtaining the knowledge reasoning chain is:

[0019] Based on the convolutional information propagation network, the relationship between the head entity and the tail entity is updated to obtain the contextual features of the entity;

[0020] Based on the random walk algorithm, the relationship paths between adjacent entities are mined to obtain the set of all relationship paths between entity pairs;

[0021] Performing semantic feature encoding on the relationship path set based on a bidirectional gated recurrent unit network to obtain semantic vectors of all relationship paths;

[0022] Aggregate the semantic vectors of different relationship paths based on the contextual features of the entity to obtain the relationship path features of the entity pair;

[0023] The context feature and the relationship path feature are fused and added, and the knowledge reasoning chain is obtained using a softmax function.

[0024] Preferably, the method for obtaining the context feature of the entity is:

[0025] Based on average pooling, aggregate the attribute information of each entity's adjacent entities to obtain aggregated neighbor information;

[0026] Use the convolution kernel to convolve the aggregated neighbor information of each entity and update the entity representation;

[0027] Based on the updated entity representation, complete the convolutional information propagation;

[0028] In the convolution information propagation process, the head entity and the tail entity exchange and update information through their respective neighbor entities, and update the relationship representation between the entities;

[0029] Based on the update of the entity representation and the relationship representation, each entity obtains an embedding vector containing local graph structure information;

[0030] Based on the embedding vector, a context feature of the entity is obtained.

[0031] Preferably, the method for obtaining the expert knowledge base is:

[0032] Preprocess and extract features of new implant case data and expert feedback data to obtain new data features;

[0033] Identify the new data features based on the knowledge reasoning chain, and screen out new data features whose similarity with existing data in the initial knowledge base does not meet a preset threshold;

[0034] Labels are added to the screened new data features as incremental inputs, and new planting case data and expert feedback data are integrated into the initial knowledge base in batches through a small batch learning algorithm to obtain the expert knowledge base.

[0035] Preferably, the method for obtaining orchard management decisions is:

[0036] For the user's decision-making problems, based on the historical data in the expert knowledge base, a decision tree algorithm is used to classify the knowledge to obtain knowledge sets of different categories;

[0037] Based on different categories of knowledge sets, the support vector machine algorithm is used to establish a knowledge prediction model to obtain prediction results;

[0038] The prediction results are optimized based on a genetic algorithm to obtain intelligent decision-making suggestions for orchard management.

[0039] The present invention also provides a smart orchard expert knowledge base construction system based on machine learning, which is used to implement the method, including:

[0040] Data collection module, used to collect and pre-process orchard-related data to obtain an initial knowledge base;

[0041] A knowledge standardization module, used to construct a knowledge representation model based on the initial knowledge base using an ontology construction method, and obtain standardized knowledge representation based on the knowledge representation model;

[0042] A knowledge reasoning chain acquisition module, used to obtain a knowledge reasoning chain based on the standardized knowledge representation and using machine learning reasoning;

[0043] An incremental module, for learning new planting cases and expert feedback based on the knowledge reasoning chain using an incremental learning algorithm, automatically updating the initial knowledge base, and obtaining an expert knowledge base;

[0044] The intelligent decision-making module is used to obtain orchard management decisions based on the expert knowledge base and adopt a machine learning algorithm to complete the construction of a smart orchard expert knowledge base based on machine learning.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows: the intelligent orchard expert knowledge base based on machine learning can extract useful information from a large amount of orchard data through automated data collection and processing, and update the knowledge base in real time to ensure the accuracy and timeliness of the knowledge. Utilize historical data and real-time data for intelligent reasoning and decision support, and provide orchard managers with more accurate and reliable decision-making basis. It has good field adaptability and scalability, and can be easily expanded to other orchards or crop fields to meet the needs of different orchard managers. The present invention realizes the transformation from scattered original knowledge to structured knowledge base, and then to the whole process of intelligent application, solving the problems of knowledge fragmentation, update lag, application limitations, etc. in traditional orchard management. Based on the standardized representation, dynamic evolution and intelligent application of knowledge, the present invention can significantly improve the intelligence level of orchard management and provide strong support for precision planting. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0047] Figure 1 This is a flow chart of a method for constructing a smart orchard expert knowledge base based on machine learning in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0050] Embodiment 1

[0051] like Figure 1 As shown, a method for constructing a smart orchard expert knowledge base based on machine learning includes the following steps:

[0052] S1: Collect and pre-process orchard related data to obtain an initial knowledge base; a further implementation method is that the orchard related data includes orchard meteorological data, soil temperature and humidity data, water and fertilizer application amount, pest and disease control data, fruit tree growth cycle and historical yields.

[0053] S2: Based on the initial knowledge base, the ontology construction method is used to build a knowledge representation model, and standardized knowledge representation is obtained based on the knowledge representation model.

[0054] A further implementation method is that the method for constructing the knowledge representation model is:

[0055] Based on natural language processing technology, the initial knowledge base is segmented, POS tagged, and entity recognized to obtain knowledge keywords;

[0056] Classify knowledge keywords based on clustering algorithms, obtain entities, and extract the attributes and relationships of each entity;

[0057] Based on entities, attributes and relationships, ontology construction method is adopted to obtain a formal knowledge representation model;

[0058] Based on the knowledge formalization model, the graph embedding algorithm is used to learn the low-dimensional vector representation of entities and relationships to obtain the semantic representation of knowledge keywords;

[0059] Based on the semantic representation and the knowledge formal representation model, a knowledge representation model is obtained.

[0060] In this embodiment, after constructing the initial knowledge base, the knowledge text is first segmented using the Jieba word segmentation tool, and then the THULAC tool is used for part-of-speech tagging, and the key concepts such as crop names, pest names, and farming operations in the text are identified by the BiLSTM-CRF model. Then, the extracted keywords are clustered into different categories such as environmental monitoring, pest control, and cultivation management according to semantic similarity using the K-means clustering algorithm. For each category, the core attributes of the category are extracted through word frequency statistics and TF-IDF weight calculation, such as environmental monitoring including temperature, humidity, light, etc., and the correlation between attributes is analyzed, such as temperature and humidity are negatively correlated. On this basis, the ontology construction tool Protégé is used to define the ontology model of agricultural knowledge, including concept classes (entities), attribute classes, and relationship classes, to form a knowledge formalization representation model.

[0061] The knowledge from different sources, such as agricultural forums and meteorological websites, is mapped into a unified ontology model through knowledge extraction and entity alignment technology. Finally, the Neo4j graph database is used to store the agricultural knowledge graph. The entities include 4,326 concepts such as crops, pests and diseases, and agricultural operations, and the edges include 6,851 attribute associations and causal relationships. In order to mine the implicit semantics between knowledge, knowledge representation learning algorithms such as TransE are used to learn the 64-dimensional vector representation of each node (entity) and edge (relationship). The semantic correlation between concepts is calculated by cosine similarity, and the knowledge representation model is constructed by integrating explicit and implicit semantics.

[0062] S3: Based on the standardized knowledge representation, machine learning reasoning is used to obtain a knowledge reasoning chain; a further implementation method is that the method for obtaining the knowledge reasoning chain is:

[0063] S31: Based on the convolutional information propagation network, the relationship between the head entity and the tail entity is updated to obtain the contextual features of the entity.

[0064] A further implementation method is that the method for obtaining the context feature of the entity is:

[0065] S311: Aggregate attribute information of adjacent entities of each entity based on average pooling to obtain aggregated neighbor information;

[0066] S312: convolve the aggregated neighbor information of each entity using a convolution kernel to update the entity representation; and complete the convolution information propagation based on the updated entity representation;

[0067] S313: During the convolution information propagation process, the head entity and the tail entity exchange and update information through their respective neighbor entities, and update the relationship representation between the entities;

[0068] S314: Based on the update of entity representation and relationship representation, each entity obtains an embedding vector containing local graph structure information;

[0069] S315: Based on the embedding vector, obtain context features of the entity.

[0070] S32: Based on the random walk algorithm, the relationship paths between adjacent entities are mined to obtain the set of all relationship paths between entity pairs;

[0071] Randomly select the initial entity of the walk and obtain the transition probability from the current entity to the adjacent entity;

[0072] The visit to the preset termination entity is used as the termination condition of the walk, and the path passed during the walk is recorded and the target entity in the path is marked;

[0073] In a single walk, starting from the starting entity, the next entity is randomly selected according to the transition probability until the termination condition of the walk is met. In each step, it is checked whether the terminal entity is reached, and all paths containing the terminal entity are recorded.

[0074] Among all the collected paths, there may be duplicate paths. Remove duplicates by comparing the node sequences in the paths. Merge all non-duplicate paths into a set, which is the set of all relationship paths between the starting entity and the ending entity.

[0075] This embodiment also includes setting a maximum path length limit to reduce the amount of calculation, evaluating the path value based on the importance and frequency of the relationships in the path, and optimizing the random walk algorithm.

[0076] S33: Based on the bidirectional gated recurrent unit network (BiGRU), the semantic features of the relationship path set are encoded to obtain the semantic vectors of all relationship paths.

[0077] The forward GRU and backward GRU are used to encode the relation path respectively, and the forward GRU hidden state and the backward GRU hidden state are obtained. The two hidden states are concatenated, and then the concatenated hidden states are linearly transformed to obtain the semantic vector of the relation path after GRU encoding.

[0078] S34: Aggregate the semantic vectors of different relationship paths based on the contextual features of the entity to obtain the relationship path features of the entity pair; in this embodiment, the weight of each relationship path is calculated based on the contextual features of the entity, and all relationship paths are weightedly added to obtain the relationship path features.

[0079] S35: The context features and the relationship path features are fused and added, and the softmax function is used to obtain the knowledge reasoning chain.

[0080] S4: Based on the knowledge reasoning chain, an incremental learning algorithm is used to learn new planting cases and expert feedback, and the initial knowledge base is automatically updated to obtain an expert knowledge base; a further implementation method is that the method for obtaining the expert knowledge base is:

[0081] Preprocess and extract features of new implant case data and expert feedback data to obtain new data features;

[0082] Identify new data features based on the knowledge reasoning chain and filter out new data features whose similarity with existing data in the initial knowledge base does not meet the preset threshold;

[0083] Labels are added to the screened new data features as incremental inputs. Through the small batch learning algorithm, the new planting case data and expert feedback data are integrated into the initial knowledge base in batches to obtain the expert knowledge base.

[0084] Specifically, when obtaining planting cases and expert feedback data, Web crawler technology is used to capture data from network resources such as agricultural forums and planting blogs. The acquired data is processed (the same as the processing process of steps S2, S3, and S4), and natural language processing techniques such as word segmentation, part-of-speech tagging, named entity recognition, etc. are used to extract key information, such as crop name, planting time, fertilizer application amount, pest and disease control measures, etc. Based on the extracted key information, an ontological knowledge representation model is constructed, and the RDF (Resource Description Framework) format is used to organize the knowledge into a subject-predicate-object triple form. For the knowledge representation model, the newly acquired planting cases and expert feedback data are incrementally integrated into the existing knowledge base. By setting the knowledge decay factor, outdated knowledge is forgotten, and the knowledge freshness index is introduced to quantify the timeliness of knowledge. At the same time, online learning algorithms are used to monitor data flows in real time and capture new knowledge patterns. When the support of a knowledge pattern exceeds a threshold (such as 8), the update of the knowledge base is triggered (i.e., new data features are identified based on the knowledge reasoning chain, and new data features whose similarity with existing data in the initial knowledge base does not meet the preset threshold are screened).

[0085] The KGEM algorithm (Knowledge Graph Embedding Model) is used to learn the low-dimensional vector representation of knowledge in the expert knowledge base, and the evolution direction of knowledge is predicted through vector operations. According to the timestamp information of knowledge, the knowledge evolution time series is constructed, and the time series prediction model is used to predict the development trend of future knowledge and realize the dynamic evolution of the knowledge base.

[0086] S5: Based on the expert knowledge base, machine learning algorithms are used to obtain orchard management decisions and complete the construction of a smart orchard expert knowledge base based on machine learning.

[0087] In this embodiment, the reasoning process of the smart orchard expert knowledge base is as follows:

[0088] 1. Input orchard data (including environmental parameters, crop growth data, etc.);

[0089] 2. Analyze and predict through machine learning models;

[0090] 3. Output decision-making recommendations (such as irrigation recommendations, fertilization recommendations, etc.).

[0091] A further embodiment is that the method for obtaining orchard management decisions is:

[0092] For the decision-making problems of users, based on the historical data in the expert knowledge base, the decision tree algorithm is used to classify the knowledge and obtain knowledge sets of different categories;

[0093] Based on different categories of knowledge sets, the support vector machine algorithm is used to establish a knowledge prediction model to obtain prediction results;

[0094] The prediction results are optimized based on genetic algorithms to obtain intelligent decision-making suggestions for orchard management.

[0095] Specifically, based on the historical data of environmental parameters, growth conditions, etc. in the past five years in the smart orchard expert knowledge base, the C5 decision tree algorithm is used to classify the knowledge. By calculating the information gain ratio of each attribute, five attributes such as temperature, humidity, and light are selected as the classification basis, and the knowledge is divided into eight categories such as irrigation, fertilization, and pest control. For each category of knowledge set, the support vector machine algorithm is used to establish a prediction model. Taking irrigation knowledge as an example, factors such as soil moisture, air temperature, and evaporation are selected as input features, and the irrigation volume in the next 7 days is used as the output. The Gaussian kernel function is used to optimize the model parameters through grid search, and the prediction accuracy on the test set reaches 95%. On this basis, the genetic algorithm is used to optimize the prediction results, with water saving and yield as the optimization goals, and the fitness function is designed. Through 500 generations of selection, crossover, mutation and other operations, the optimal irrigation decision plan is obtained. This plan is applied to the automatic irrigation system of the orchard, and the irrigation time and water volume are dynamically adjusted according to the real-time data of the soil moisture sensor. At the same time, various parameters in the irrigation process are continuously collected, the knowledge base is updated once a week, and the gradient descent method is used to incrementally train the prediction model to improve the generalization ability of the model. When encountering extreme weather such as high temperature and drought for three consecutive days, the system automatically starts the emergency plan, increases the irrigation frequency according to the data analysis results in the knowledge base, and combines the plant physiology model to predict the impact of high temperature on the growth of fruit trees, and takes cooling measures such as shading and leaf spraying in advance. Through data visualization technology, irrigation decisions and real-time monitoring data are displayed on the management platform in the form of line graphs, heat maps, etc., and daily, weekly, and monthly irrigation reports are generated to assist orchard managers in analysis and decision-making. The system evaluates the irrigation effect based on indicators such as yield and water and fertilizer utilization rate, forms a closed-loop feedback, and continuously optimizes irrigation strategies and expert knowledge bases.

[0096] Embodiment 2

[0097] The present invention also provides a smart orchard expert knowledge base construction system based on machine learning, which is used to implement the method, including:

[0098] Data collection module, used to collect and pre-process orchard-related data to obtain an initial knowledge base;

[0099] A knowledge standardization module is used to construct a knowledge representation model based on the initial knowledge base using an ontology construction method, and obtain standardized knowledge representation based on the knowledge representation model;

[0100] A knowledge reasoning chain acquisition module is used to obtain a knowledge reasoning chain based on standardized knowledge representation and machine learning reasoning;

[0101] The incremental module is used to learn new implant cases and expert feedback based on the knowledge reasoning chain using an incremental learning algorithm, automatically update the initial knowledge base, and obtain the expert knowledge base;

[0102] The intelligent decision-making module is used to obtain orchard management decisions based on the expert knowledge base and adopt machine learning algorithms to complete the construction of the smart orchard expert knowledge base based on machine learning.

[0103] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for constructing a smart orchard expert knowledge base based on machine learning, characterized in that: The following steps are involved: Collect and preprocess orchard-related data to obtain an initial knowledge base; Based on the initial knowledge base, a knowledge representation model is constructed using an ontology construction method, and a standardized knowledge representation is obtained based on the knowledge representation model; Based on the standardized knowledge representation, machine learning reasoning is used to obtain a knowledge reasoning chain; Based on the knowledge reasoning chain, an incremental learning algorithm is used to learn new planting cases and expert feedback, and the initial knowledge base is automatically updated to obtain an expert knowledge base; Based on the expert knowledge base, a machine learning algorithm is used to obtain orchard management decisions, thereby completing the construction of a smart orchard expert knowledge base based on machine learning.

2. The method for constructing a smart orchard expert knowledge base based on machine learning according to claim 1, characterized in that: The orchard-related data include orchard meteorological data, soil temperature and humidity data, water and fertilizer application amount, pest and disease control data, fruit tree growth cycle and historical yields.

3. The method for constructing a smart orchard expert knowledge base based on machine learning according to claim 1, characterized in that: The method to construct the knowledge representation model is: Based on natural language processing technology, knowledge segmentation, part-of-speech tagging and entity recognition are performed on the initial knowledge base to obtain knowledge keywords; Classifying the knowledge keywords based on a clustering algorithm to obtain entities, and extracting attributes and relationships of each entity; Based on the entities, the attributes and the relationships, an ontology construction method is adopted to obtain a knowledge formalization representation model; Based on the knowledge formalization representation model, a graph embedding algorithm is used to learn the low-dimensional vector representation of the entity and the relationship to obtain the semantic representation of the knowledge keyword; The knowledge representation model is obtained based on the semantic representation and the knowledge formalization representation model.

4. The method for constructing a smart orchard expert knowledge base based on machine learning according to claim 3, characterized in that: The method for obtaining the knowledge reasoning chain is: Based on the convolutional information propagation network, the relationship between the head entity and the tail entity is updated to obtain the contextual features of the entity; Based on the random walk algorithm, the relationship paths between adjacent entities are mined to obtain the set of all relationship paths between entity pairs; Performing semantic feature encoding on the relationship path set based on a bidirectional gated recurrent unit network to obtain semantic vectors of all relationship paths; Aggregate the semantic vectors of different relationship paths based on the contextual features of the entity to obtain the relationship path features of the entity pair; The context feature and the relationship path feature are fused and added, and the knowledge reasoning chain is obtained using a softmax function.

5. The method for constructing a smart orchard expert knowledge base based on machine learning according to claim 4, characterized in that: The method to obtain the contextual features of an entity is: Based on average pooling, aggregate the attribute information of each entity's adjacent entities to obtain aggregated neighbor information; Use the convolution kernel to convolve the aggregated neighbor information of each entity and update the entity representation; Based on the updated entity representation, complete the convolutional information propagation; In the convolution information propagation process, the head entity and the tail entity exchange and update information through their respective neighbor entities, and update the relationship representation between the entities; Based on the update of the entity representation and the relationship representation, each entity obtains an embedding vector containing local graph structure information; Based on the embedding vector, a context feature of the entity is obtained.

6. The method for constructing a smart orchard expert knowledge base based on machine learning according to claim 4, characterized in that: The method to obtain the expert knowledge base is: Preprocess and extract features of new implant case data and expert feedback data to obtain new data features; Identify the new data features based on the knowledge reasoning chain, and screen out new data features whose similarity with existing data in the initial knowledge base does not meet a preset threshold; Labels are added to the screened new data features as incremental inputs, and new planting case data and expert feedback data are integrated into the initial knowledge base in batches through a small batch learning algorithm to obtain the expert knowledge base.

7. The method for constructing a smart orchard expert knowledge base based on machine learning according to claim 4, characterized in that: The methods to obtain orchard management decisions are: For the user's decision-making problems, based on the historical data in the expert knowledge base, a decision tree algorithm is used to classify the knowledge to obtain knowledge sets of different categories; Based on different categories of knowledge sets, the support vector machine algorithm is used to establish a knowledge prediction model to obtain prediction results; The prediction results are optimized based on a genetic algorithm to obtain intelligent decision-making suggestions for orchard management.

8. A smart orchard expert knowledge base construction system based on machine learning, used to implement the method described in any one of claims 1 to 7, characterized in that: include: Data collection module, used to collect and pre-process orchard-related data to obtain an initial knowledge base; A knowledge standardization module, used to construct a knowledge representation model based on the initial knowledge base using an ontology construction method, and obtain standardized knowledge representation based on the knowledge representation model; A knowledge reasoning chain acquisition module, used to obtain a knowledge reasoning chain based on the standardized knowledge representation and using machine learning reasoning; An incremental module, for learning new planting cases and expert feedback based on the knowledge reasoning chain using an incremental learning algorithm, automatically updating the initial knowledge base, and obtaining an expert knowledge base; The intelligent decision-making module is used to obtain orchard management decisions based on the expert knowledge base and adopt a machine learning algorithm to complete the construction of a smart orchard expert knowledge base based on machine learning.

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