Precise agricultural management and decision support method and system based on large model
By integrating multi-source data and utilizing deep learning models, precision agricultural management and decision support are provided, solving the problems of scientificity and accuracy in traditional agricultural management and realizing efficient and intelligent agricultural production.
Patent Information
- Application Number
- CN202511379102.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-25
- Publication Date
- 2026-01-20
AI Technical Summary
Traditional agricultural management methods lack scientific rigor and accuracy, making it difficult to meet the demands of modern agriculture for high efficiency and precision. Furthermore, the integration of multi-source information and the utilization of data are inefficient.
By integrating satellite remote sensing data, IoT sensor data, meteorological data, and historical agricultural records, and using deep learning models for data cleaning, feature extraction, and model training, we can provide precision agricultural management suggestions and decision support.
It has improved the scientific nature and accuracy of agricultural production, optimized resource allocation, reduced costs, enhanced crop resistance to adverse conditions, reduced environmental pollution, and promoted agricultural modernization.
Smart Images
Figure CN121366052A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the field of large models and smart agriculture, specifically a precision agriculture management and decision support method and system based on large models. BACKGROUND
[0002] With the continuous development of agricultural technology, precision agriculture has become an important trend in modern agriculture. However, traditional agricultural management methods often rely on experience and lack scientificity and accuracy, making it difficult to meet the needs of modern agriculture for efficiency and precision.
[0003] Currently, multi-source information is increasingly widely used in the field of agriculture, and these information provides a rich data basis for precision agriculture, but how to effectively integrate these data and extract valuable information has become a problem to be solved. SUMMARY
[0004] The technical task of the present application is to address the above shortcomings, and to provide a precision agriculture management and decision support method and system based on large models, which helps to improve the scientificity and accuracy of agricultural production, and can optimize resource allocation, improve production efficiency, and promote the development of agriculture towards more intelligent, precise and efficient direction.
[0005] The technical solution adopted by the present application to solve its technical problems is:
[0006] A precision agriculture management and decision support method based on large models, comprising:
[0007] Data collection and integration: integrate satellite remote sensing data, Internet of Things sensor data, meteorological data and historical agricultural records, and perform data cleaning, format unification and preliminary integration;
[0008] Data preprocessing and feature engineering: further process the integrated data, including missing value filling, outlier detection and processing, data normalization or standardization, and extract key features;
[0009] Deep learning model construction and training: construct a deep learning model based on the processed data, select appropriate network structure, parameter initialization method, loss function and optimization algorithm for model training;
[0010] Precision agriculture management and decision support: according to the prediction results of the deep learning model, provide precision agriculture management suggestions, including irrigation, fertilization, pest control and other agricultural management suggestions, and decision support, including crop planting layout optimization, yield prediction, etc.
[0011] The method provides a scientific basis for agricultural production through the analysis and prediction of multi-source data by a deep learning model, reducing the blindness of experience judgment. At the same time, according to the growth cycle of crops, soil moisture and other key characteristics, a precise agricultural management plan is formulated, improving the efficiency of agricultural production. In addition, the method can also provide automatic and intelligent agricultural management and decision support, reducing labor costs, enhancing the resistance of crops, and helping to reduce the excessive use of fertilizers and pesticides, and reducing environmental pollution.
[0012] Further, for data collection and integration,
[0013] The satellite remote sensing data acquires macro-environmental information, including vegetation index, soil moisture and other information of farmland;
[0014] The Internet of Things sensor data monitors micro-environmental parameters in real time, including soil temperature, pH value, air temperature and humidity, etc. of farmland;
[0015] The meteorological data collects meteorological data, including rainfall, wind speed, wind direction, etc.;
[0016] The historical farm records provide detailed information of agricultural production activities, including crop planting, fertilization, irrigation, pest control, etc.
[0017] Further, the data preprocessing and feature engineering include:
[0018] Data cleaning: handling missing values and outliers;
[0019] Data standardization or normalization, used to ensure the consistency and comparability of data;
[0020] Feature extraction: using domain knowledge and statistical methods to extract key features, such as crop growth cycle characteristics, pest occurrence regularity characteristics, and the influence of weather conditions on crop growth characteristics. These features not only reflect the basic laws of crop growth and pest occurrence, but also contain the complexity and diversity of farmland environment.
[0021] Further, the deep learning large model construction and training include:
[0022] Model architecture design: select appropriate network structure according to application scenario and data characteristics;
[0023] Parameter initialization: use methods including random initialization or pre-trained model transfer learning to improve the training efficiency and performance of the model;
[0024] Loss function selection: select the corresponding loss function according to the different prediction tasks;
[0025] Optimization algorithm application: Adopt optimization algorithms including gradient descent, Adam, etc. to update model parameters.
[0026] Further, the precision agriculture management and decision support includes:
[0027] Irrigation plan making: make scientific irrigation plan according to crop growth cycle and soil moisture prediction results;
[0028] Fertilization scheme making: make reasonable fertilization scheme according to crop nutrient demand and soil nutrient content prediction results;
[0029] Disease and pest control suggestion: take timely and effective control measures according to disease and pest occurrence trend prediction results;
[0030] Decision support: provide decision support including crop planting layout optimization, yield prediction, etc.
[0031] Further, the method further includes feedback and optimization: collect the output results of precision agriculture management and decision support and feedback information in agricultural production practice, for optimizing the whole system, forming a closed-loop feedback mechanism.
[0032] Further, the feedback and optimization specifically includes:
[0033] Feedback information collection: collect feedback information in agricultural production practice;
[0034] System optimization: feedback information is adjusted, including data acquisition, preprocessing, feature engineering, model training and management decision modules, to improve system performance and prediction accuracy.
[0035] The present application also claims a precision agriculture management and decision support system based on large models, comprising:
[0036] Data acquisition and integration module for integrating multi-source data;
[0037] Data preprocessing and feature engineering module for processing data and extracting features;
[0038] Deep learning large model construction and training module for constructing and training deep learning models;
[0039] Precision agriculture management and decision support module for providing precision agriculture management suggestions and decision support;
[0040] The system can realize the precision agriculture management and decision support method based on large models as described above.
[0041] The present application also claims a precision agriculture management and decision support device based on large models, comprising: at least one memory and at least one processor;
[0042] The at least one memory is configured to store a machine readable program;
[0043] The at least one processor is configured to invoke the machine readable program to implement the method.
[0044] The present application also claims a computer readable medium having computer instructions stored thereon, wherein the computer instructions, when executed by a processor, implement the method.
[0045] Compared with the prior art, the method and system for precision agriculture management and decision support based on a large model have the following beneficial effects:
[0046] The present application integrates satellite remote sensing, Internet of Things sensors, meteorological data, and historical agricultural records, and combines deep learning technology to achieve significant benefits in the precision agriculture management and decision support system. Specifically, these benefits are reflected in the following ten aspects:
[0047] 1. Improve data utilization and accuracy: Through the data collection and integration module, the multi-source data is cleaned, formatted, and preliminarily integrated to ensure the comprehensiveness, diversity, and consistency of the data, thereby improving the utilization and accuracy of the data and providing a reliable foundation for subsequent analysis and prediction.
[0048] 2. Enhance model capability: The data preprocessing and feature engineering module further processes the original data to extract key features, enabling the deep learning large model to more effectively learn the internal rules and patterns of the data, thereby enhancing the prediction capability and accuracy of the model.
[0049] 3. Realize intelligent decision support: The deep learning large model construction and training module constructs a model with high precision and generalization capability based on high-quality data input, providing intelligent decision support for the precision agriculture management and decision support module, making agricultural production more scientific and efficient.
[0050] 4. Optimize agricultural resource allocation: The precision agriculture management and decision support module provides precise agricultural management suggestions such as irrigation, fertilization, and pest control based on model prediction results, helping agricultural producers to reasonably arrange production plans and optimize resource allocation, thereby improving agricultural production efficiency and yield.
[0051] 5. Reduce production costs: Through intelligent decision support, agricultural producers can more accurately grasp the growth status of crops and farmland environmental parameters, avoiding unnecessary resource waste, thereby reducing agricultural production costs.
[0052] 6. Improve agricultural product quality and safety: Precise agricultural management helps reduce the overuse of fertilizers and pesticides, reduce environmental pollution, and improve the quality and safety of agricultural products, meeting consumers' demand for high-quality agricultural products.
[0053] 7. Promote sustainable agricultural development: This technical solution realizes the rational use of resources and environmental protection through intelligent and precise agricultural management, promoting the sustainable development of agriculture.
[0054] 8. Improve the flexibility of agricultural production: The deep learning model can quickly adjust the agricultural management strategy according to real-time data, making agricultural production more flexible and adaptable, and better coping with uncertain factors such as climate change.
[0055] 9. Enhance the competitiveness of agricultural production: By improving agricultural production efficiency and product quality, this technical solution helps enhance the competitiveness of agricultural producers in the market and improve the overall benefits of the agricultural industry.
[0056] 10. Promote agricultural modernization: The application of this technical solution marks an important step in the process of agricultural modernization, and through intelligent and precise agricultural management means, it promotes the development of agriculture towards a more efficient, environmentally friendly and sustainable direction. BRIEF DESCRIPTION OF DRAWINGS
[0057] Figure 1 is the implementation process diagram of the precision agriculture management and decision support method based on large model provided by the embodiment of the present application;
[0058] Figure 2 is the data collection and integration diagram provided by the embodiment of the present application;
[0059] Figure 3 is the data preprocessing and feature engineering flowchart provided by the embodiment of the present application;
[0060] Figure 4 is the deep learning large model construction and training flowchart provided by the embodiment of the present application;
[0061] Figure 5 is the precision agriculture management and decision support diagram provided by the embodiment of the present application. DETAILED DESCRIPTION
[0062] The present application will be further described below in conjunction with the drawings and specific embodiments.
[0063] The embodiment of the present application provides a precision agriculture management and decision support method based on a large model, as shown in Figure 1 , comprising:
[0064] Data Collection and Integration: Integrate satellite remote sensing data, IoT sensor data, weather data, and historical agricultural records, and perform data cleaning, format unification, and preliminary integration.
[0065] Data Preprocessing and Feature Engineering: Further process the integrated data, including missing value filling, outlier detection and processing, data normalization or standardization, and key feature extraction.
[0066] Deep Learning Large Model Construction and Training: Based on the processed data, construct a deep learning model, select appropriate network structure, parameter initialization method, loss function and optimization algorithm for model training.
[0067] Precision Agriculture Management and Decision Support: Based on the prediction results of the deep learning model, provide precision agriculture management suggestions, including irrigation, fertilization, pest control, etc., and decision support, including crop planting layout optimization, yield prediction, etc.
[0068] Among them, for data collection and integration:
[0069] The satellite remote sensing data obtains macro-environmental information, including vegetation index, soil moisture, etc.
[0070] The IoT sensor data monitors micro-environmental parameters in real time, including soil temperature, pH value, air temperature and humidity, etc.
[0071] The weather data collects weather data, including rainfall, wind speed, wind direction, etc.
[0072] The historical agricultural records provide detailed information on agricultural production activities, including crop planting, fertilization, irrigation, pest control, etc.
[0073] Data preprocessing and feature engineering include:
[0074] Data cleaning: handle missing values and outliers;
[0075] Data standardization or normalization to ensure data consistency and comparability;
[0076] Feature extraction: use domain knowledge and statistical methods to extract key features, such as crop growth cycle characteristics, pest occurrence regularity characteristics, and weather conditions affecting crop growth characteristics. These features not only reflect the basic rules of crop growth and pest occurrence, but also contain the complexity and diversity of the farmland environment.
[0077] The deep learning large model construction and training include:
[0078] Model architecture design: select appropriate network structure according to application scenarios and data characteristics;
[0079] Parameter initialization: methods including random initialization or pre-trained model transfer learning are used to improve the training efficiency and performance of the model;
[0080] Loss function selection: select the corresponding loss function according to the different prediction tasks;
[0081] Optimization algorithm application: use optimization algorithms including gradient descent, Adam, etc. to update the model parameters.
[0082] The precision agriculture management and decision support includes:
[0083] Irrigation plan making: make a scientific irrigation plan according to the crop growth cycle and soil moisture prediction results;
[0084] Fertilization scheme making: make a reasonable fertilization scheme according to the crop nutrient demand and soil nutrient content prediction results;
[0085] Disease and pest control suggestions: take timely and effective control measures according to the disease and pest occurrence trend prediction results;
[0086] Decision support: provide decision support including crop planting layout optimization, yield prediction, etc.
[0087] The method also includes feedback and optimization: collect the output results of precision agriculture management and decision support and feedback information in agricultural production practice for optimizing the whole system and forming a closed-loop feedback mechanism.
[0088] The feedback and optimization specifically includes:
[0089] Feedback information collection: collect feedback information in agricultural production practice;
[0090] System optimization: feedback information is adjusted, including data acquisition, preprocessing, feature engineering, model training and management decision modules, to improve system performance and prediction accuracy.
[0091] This method integrates satellite remote sensing, Internet of Things sensors, meteorological data and historical agricultural records, etc. to build a large model using deep learning technology, providing efficient management and decision support for precision agriculture. This method contains four core and closely connected technical modules, each module has a specific function, and forms a close logical relationship with other modules, together promoting the effective operation of the whole system.
[0092] In combination with the drawings, the four core technical modules are implemented as follows:
[0093] I. Data acquisition and integration
[0094] Macro-environmental information of farmland, such as vegetation index and soil moisture, is obtained through satellite remote sensing technology. Meanwhile, micro-environmental parameters such as soil temperature, pH value, and air temperature and humidity are monitored in real time by Internet of Things sensors. In addition, meteorological data such as rainfall, wind speed, and wind direction are collected through weather stations. Historical agricultural records provide detailed information on crop planting, fertilization, irrigation, and pest control. These multi-source data are cleaned, formatted, and preliminarily integrated in the data collection and integration module to ensure data accuracy, completeness, and consistency. Figure 2
[0095] The output of this module provides a comprehensive, rich, and high-quality data foundation for the subsequent data preprocessing and feature engineering module, providing a strong guarantee for the accurate prediction and decision support of the entire system.
[0096] II. Data Preprocessing and Feature Engineering
[0097] Data preprocessing and feature engineering are the key bridge connecting the data collection and integration module and the deep learning large model construction and training module.
[0098] First, the integrated data is further cleaned and processed, including missing value filling, outlier detection and processing, data normalization or standardization, etc., to ensure data quality and consistency. On this basis, the module uses domain knowledge and statistical methods to extract key features, such as crop growth cycle characteristics, pest occurrence patterns, and the impact of weather conditions on crop growth. These features not only reflect the basic rules of crop growth and pest occurrence, but also contain the complexity and diversity of farmland environments. Figure 3
[0099] Through the processing of the data preprocessing and feature engineering module, the original data is transformed into more valuable and easily understood feature vectors for the model, providing strong support for the subsequent training of deep learning large models.
[0100] III. Deep Learning Large Model Construction and Training
[0101] Based on the feature vectors provided by the data preprocessing and feature engineering module, a large model is constructed using a deep learning framework such as TensorFlow or PyTorch. Figure 4
[0102] In terms of model architecture design, appropriate network structures are selected according to specific application scenarios and data characteristics, such as convolutional neural networks (CNN) for image data processing, recurrent neural networks (RNN) or long short-term memory networks (LSTM) for time series data analysis.
[0103] In terms of parameter initialization, methods such as random initialization or pre-trained model transfer learning are used to improve the training efficiency and performance of the model.
[0104] In terms of loss function selection, different loss functions are selected according to the different prediction tasks (such as classification, regression, etc.).
[0105] In terms of optimization algorithm application, optimization algorithms such as gradient descent and Adam are used to update the model parameters.
[0106] During the model training process, the model parameters are continuously adjusted through multiple iterations and verifications until the model reaches the expected prediction accuracy and generalization ability.
[0107] The output of the deep learning large model construction and training module provides precise prediction results for the precision agriculture management and decision support module, providing scientific basis and intelligent means for agricultural production.
[0108] Four, precision agriculture management and decision support
[0109] Based on the prediction results provided by the deep learning large model construction and training module, combined with the actual needs of agricultural production, precision agriculture management suggestions such as irrigation, fertilization, and pest control are provided. For example, based on the crop growth cycle and soil moisture prediction results, a scientific irrigation plan is developed; based on the crop nutrient demand and soil nutrient content prediction results, a reasonable fertilization scheme is developed; based on the pest occurrence trend prediction results, timely and effective prevention and control measures are taken. In addition, this module can also provide decision support for crop planting layout optimization and yield prediction, helping agricultural producers to reasonably arrange production plans, optimize resource allocation, and improve production efficiency. For example, Figure 5 as shown.
[0110] The output of the precision agriculture management and decision support module not only directly guides agricultural production practice, improves agricultural production efficiency and yield, but also collects feedback information for optimizing the entire system, forming a closed-loop feedback mechanism, and continuously improving system performance and prediction accuracy. Through the application of the precision agriculture management and decision support module, agricultural production will achieve more intelligent, precise, and efficient development.
[0111] Through these four closely connected technical modules and their logical relationships, this method realizes the whole process automation and intelligentization from multi-source data collection to precision agriculture management and decision support, providing strong technical support and decision basis for agricultural production.
[0112] This method combines deep learning technology and multi-source data fusion, integrates multi-source information, and uses deep learning technology to build a large model, providing efficient management and decision support for precision agriculture. This not only helps improve the scientific nature and accuracy of agricultural production, but also optimizes resource allocation, improves production efficiency, and promotes the development of agriculture towards more intelligent, precise, and efficient directions.
[0113] This method integrates satellite remote sensing, Internet of Things sensors, meteorological data, and historical agricultural records, and combines deep learning technology to build an efficient and precise agricultural management and decision support system. This method can achieve the following technical effects:
[0114] Improve the scientific nature of agricultural decision-making: through deep learning models to analyze and predict multi-source data, provide scientific basis for agricultural production, and reduce the blindness of experience judgment.
[0115] Implement precise agricultural management: according to the growth cycle of crops, soil moisture, nutrient content and other key characteristics, develop precise irrigation, fertilization and pest control plans to improve agricultural production efficiency.
[0116] Optimize resource allocation: through model prediction of crop yield and planting layout, help agricultural producers reasonably arrange production plans, and optimize the allocation of land, water resources and chemical fertilizers and other agricultural resources.
[0117] Improve production efficiency: automated and intelligent agricultural management and decision support systems can reduce labor costs and improve agricultural production efficiency.
[0118] Enhance crop resistance: through model prediction of disease and pest occurrence trends, take effective prevention and control measures in time to enhance crop resistance and improve yield and quality.
[0119] Promote sustainable agricultural development: precise agricultural management and decision support systems help reduce the overuse of chemical fertilizers and pesticides, reduce environmental pollution, and promote sustainable agricultural development.
[0120] Promote agricultural modernization: the application of deep learning technology and multi-source data fusion will promote the development of agriculture towards more intelligent, precise and efficient directions.
[0121] Enhance agricultural competitiveness: precise agricultural management and decision support systems help improve the quality and yield of agricultural products, enhancing the competitiveness of agriculture in the international market.
[0122] Provide convenient agricultural information services: through the construction of user-friendly interfaces and interactive methods, provide convenient and real-time agricultural information services for agricultural producers.
[0123] The embodiment of the application also provides a precision agriculture management and decision support system based on a large model, which can realize the precision agriculture management and decision support method based on a large model in the above embodiment.
[0124] The system comprises:
[0125] 1. A data acquisition and integration module for integrating satellite remote sensing data, Internet of Things sensor data, meteorological data, and historical agricultural records, and performing data cleaning, format unification, and preliminary integration.
[0126] The data acquisition and integration module comprises:
[0127] A satellite remote sensing data acquisition unit for acquiring macro-environmental information such as vegetation index and soil moisture of farmland;
[0128] An Internet of Things sensor data acquisition unit for real-time monitoring of micro-environmental parameters such as soil temperature, pH value, air temperature and humidity of farmland;
[0129] A meteorological data acquisition unit for collecting meteorological data such as rainfall, wind speed, and wind direction;
[0130] A historical agricultural record acquisition unit for providing detailed information of agricultural production activities such as crop planting, fertilization, irrigation, and pest control.
[0131] 2. A data preprocessing and feature engineering module for further processing of the integrated data, including missing value filling, outlier detection and processing, data normalization or standardization, and key feature extraction.
[0132] The data preprocessing and feature engineering module comprises:
[0133] A data cleaning unit for processing missing values and outliers;
[0134] A data standardization or normalization unit for ensuring data consistency and comparability;
[0135] A feature extraction unit for extracting key features using domain knowledge and statistical methods.
[0136] 3. A deep learning large model construction and training module for constructing a deep learning model based on the processed data, selecting appropriate network structure, parameter initialization method, loss function, and optimization algorithm for model training.
[0137] The deep learning large model construction and training module comprises:
[0138] A model architecture design unit for selecting appropriate network structure according to application scenarios and data characteristics;
[0139] Parameter initialization unit, using random initialization or pre-training model transfer learning method;
[0140] Loss function selection unit, according to the different prediction task selection corresponding loss function;
[0141] Optimization algorithm application unit, using gradient descent, Adam, etc. Optimization algorithm for model parameter update.
[0142] 4、Precision agriculture management and decision support module, according to the prediction result of deep learning model, provides irrigation, fertilization, disease and pest control, etc. Precision agriculture management suggestion, as well as crop planting layout optimization, yield prediction, etc. Decision support.
[0143] Precision agriculture management and decision support module includes:
[0144] Irrigation plan making unit, according to the crop growth cycle and soil moisture prediction results to make scientific irrigation plan;
[0145] Fertilization scheme making unit, according to the crop nutrition demand and soil nutrient content prediction results to make reasonable fertilization scheme;
[0146] Disease and pest control suggestion unit, according to the disease and pest occurrence trend prediction results to take timely and effective control measures;
[0147] Decision support unit, provides crop planting layout optimization, yield prediction, etc. Decision support.
[0148] 5、Feedback and optimization module, used for collecting the output results of precision agriculture management and decision support module and feedback information in agricultural production practice, used for optimizing the whole system, forming closed loop feedback mechanism.
[0149] Feedback and optimization module includes:
[0150] Feedback information collection unit, used for collecting feedback information in agricultural production practice;
[0151] System optimization unit, according to the feedback information to adjust data acquisition, pretreatment, feature engineering, model training and management decision, etc. Module, improve system performance and prediction accuracy.
[0152] The data collection and integration module is the data cornerstone of the entire system. This module obtains macro-environmental information of farmland such as vegetation index and soil moisture through satellite remote sensing technology; at the same time, Internet of Things sensors are responsible for real-time monitoring of micro-environmental parameters of farmland such as soil temperature, pH value, air temperature and humidity, etc. In addition, meteorological data such as rainfall, wind speed and direction, etc. are also collected through weather stations. Historical agricultural records provide detailed information on crop planting, fertilization, irrigation, pest control and other agricultural production activities. These multi-source data are cleaned, formatted and preliminarily integrated in the data collection and integration module to ensure the accuracy, integrity and consistency of the data. The output of this module provides a comprehensive, rich and high-quality data basis for the subsequent data preprocessing and feature engineering module, providing a strong guarantee for the accurate prediction and decision support of the entire system.
[0153] The data preprocessing and feature engineering module is a key bridge connecting the data collection and integration module and the deep learning large model construction and training module. This module first further cleans and processes the integrated data, including missing value filling, outlier detection and processing, data normalization or standardization, etc. to ensure the quality and consistency of the data. On this basis, the module uses domain knowledge and statistical methods to extract key features such as crop growth cycle characteristics, pest occurrence characteristics, and the influence of weather conditions on crop growth. These features not only reflect the basic laws of crop growth and pest occurrence, but also contain the complexity and diversity of the farmland environment. Through the processing of the data preprocessing and feature engineering module, the original data is transformed into more valuable and easily understood feature vectors for the model, providing strong support for the subsequent training of deep learning large models.
[0154] The deep learning large model construction and training module is the core part of the entire system. Based on the feature vectors provided by the data preprocessing and feature engineering module, this module uses deep learning frameworks such as TensorFlow, PyTorch, etc. to construct large models. In terms of model architecture design, according to the specific application scenario and data characteristics, select the appropriate network structure, such as convolutional neural network (CNN) for image data processing, recurrent neural network (RNN) or long short-term memory network (LSTM) for time series data analysis. In terms of parameter initialization, use random initialization or pre-trained model transfer learning methods to improve the training efficiency and performance of the model. In terms of loss function selection, according to the different prediction tasks (such as classification, regression, etc.), select the corresponding loss function. In terms of optimization algorithm application, use gradient descent, Adam, etc. optimization algorithm to update the model parameters. During the model training process, through multiple iterations and verification, the model parameters are constantly adjusted until the model reaches the expected prediction accuracy and generalization ability. The output of the deep learning large model construction and training module provides accurate prediction results for the precision agriculture management and decision support module, providing scientific basis and intelligent means for agricultural production.
[0155] The precision agriculture management and decision support module is the output end of the entire system, and is the final embodiment of the value of the entire system. According to the prediction results provided by the deep learning large model construction and training module, combined with the actual needs of agricultural production, this module provides precision agriculture management suggestions such as irrigation, fertilization, pest control, etc. For example, according to the crop growth cycle and soil moisture prediction results, a scientific irrigation plan is developed; according to the crop nutrient demand and soil nutrient content prediction results, a reasonable fertilization scheme is developed; according to the pest occurrence trend prediction results, timely and effective control measures are taken. In addition, this module can also provide decision support such as crop planting layout optimization, yield prediction, etc. to help agricultural producers reasonably arrange production plans, optimize resource allocation, and improve production efficiency. The output of the precision agriculture management and decision support module not only directly guides agricultural production practice, improves agricultural production efficiency and yield, but also collects feedback information for optimizing the entire system, forming a closed-loop feedback mechanism, and continuously improving system performance and prediction accuracy. Through the application of the precision agriculture management and decision support module, agricultural production will achieve more intelligent, precise and efficient development.
[0156] Through these four closely connected technical modules and their logical relationships, the system realizes the whole process automation and intelligentization from multi-source data collection to precision agriculture management and decision support, providing strong technical support and decision basis for agricultural production.
[0157] The system provides efficient management and decision support for precision agriculture by integrating multi-source information and deep learning technology, which helps to promote the development of agriculture towards more intelligent, precise and efficient direction. The data acquisition and integration module is responsible for integrating various types of agricultural related data to ensure the comprehensiveness, diversity and consistency of the data. The data preprocessing and feature engineering module further processes the integrated data to extract key features and provides high-quality data input for subsequent model training. The deep learning large model construction and training module constructs a deep learning model with high precision and generalization ability based on the processed data. Finally, the precision agriculture management and decision support module provides precision agriculture management suggestions such as irrigation, fertilization, pest control, and decision support such as crop planting layout optimization and yield prediction based on the prediction results of the model. The beneficial effects of this system are significant, including improving data utilization and accuracy, enhancing model prediction ability, realizing intelligent decision support, optimizing agricultural resource allocation, reducing production cost, improving agricultural product quality and safety, and promoting sustainable development of agriculture. In addition, this system can also improve the flexibility and competitiveness of agricultural production and promote the process of agricultural modernization.
[0158] The embodiment of the present application also provides a precision agriculture management and decision support device based on a large model, comprising at least one memory and at least one processor.
[0159] The at least one memory is used to store machine readable programs.
[0160] The at least one processor is used to call the machine readable programs to realize the precision agriculture management and decision support method based on a large model described in the above embodiments.
[0161] The embodiment of the present application also provides a computer readable medium, which stores computer instructions. When the computer instructions are executed by a processor, the precision agriculture management and decision support method based on a large model described in the above embodiments is realized. Specifically, a system or device equipped with a storage medium can be provided, which stores software program code realizing the functions of any one of the above embodiments, and makes the computer (or CPU or MPU) of the system or device read and execute the program code stored in the storage medium.
[0162] In this case, the program code read from the storage medium itself can realize the functions of any one of the above embodiments, so the program code and the storage medium storing the program code constitute a part of the present application.
[0163] The storage medium for providing the program code includes floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, nonvolatile memory cards and ROMs. Alternatively, the program code can be downloaded from a server computer via a communication network.
[0164] Further, it should be understood that not only the program code read by the computer is executed, but also the operating system and the like operating on the computer based on the instructions of the program code are caused to perform part or all of the actual operations, thereby realizing the functions of any one of the above-described embodiments.
[0165] Further, it should be understood that the program code read by the storage medium is written into the memory provided in the expansion board inserted into the computer or the memory provided in the expansion unit connected to the computer, and then the CPU and the like mounted on the expansion board or the expansion unit are caused to perform part or all of the actual operations based on the instructions of the program code, thereby realizing the functions of any one of the above-described embodiments.
[0166] The present application has been described in detail by the above drawings and preferred embodiments, however, the present application is not limited to these disclosed embodiments, and those skilled in the art can know that more embodiments of the present application can be obtained by combining the code auditing means in the above-described different embodiments, and these embodiments are also within the protection scope of the present application.
Claims
1. A large model-based precision agriculture management and decision support method, characterized in that, Comprises: Data collection and integration: integrate satellite remote sensing data, Internet of Things sensor data, meteorological data and historical agricultural records, and perform data cleaning, format unification and preliminary integration; Data preprocessing and feature engineering: further process the integrated data, including missing value filling, outlier detection and processing, data normalization or standardization, and extract key features; Deep learning model construction and training: build deep learning models based on processed data, select appropriate network structure, parameter initialization method, loss function and optimization algorithm for model training; Precision agriculture management and decision support: provide precision agriculture management suggestions based on the prediction results of deep learning models, including irrigation, fertilization, pest control, and decision support, including crop planting layout optimization and yield prediction.
2. The method according to claim 1, wherein, For data collection and integration, The satellite remote sensing data obtain macro-environmental information, including vegetation index, soil moisture information of farmland; The Internet of Things sensor data, real-time monitoring of micro-environmental parameters, including soil temperature, pH value, air temperature and humidity of farmland; The meteorological data, collect meteorological data, including rainfall, wind speed, wind direction; The historical agricultural records provide detailed information of agricultural production activities, including crop planting, fertilization, irrigation, pest control information.
3. The method of claim 1, wherein the method further comprises: The data preprocessing and feature engineering include: Data cleaning: handle missing values and outliers; Data standardization or normalization, to ensure data consistency and comparability; Feature extraction: extract key features using domain knowledge and statistical methods.
4. The method of claim 1, wherein the method further comprises: The deep learning model construction and training includes: Model architecture design: select appropriate network structure according to application scenario and data characteristics; Parameter initialization: use methods including random initialization or pre-trained model transfer learning to improve model training efficiency and performance; Loss function selection: select corresponding loss function according to different prediction tasks; Optimization algorithm application: use optimization algorithms including gradient descent, Adam to update model parameters.
5. The method of claim 1, wherein the method further comprises: The precision agriculture management and decision support includes: Irrigation plan development: develop scientific irrigation plan according to crop growth cycle and soil moisture prediction results; Fertilization scheme development: develop reasonable fertilization scheme according to crop nutrient demand and soil nutrient content prediction results; Disease and pest control suggestions: take timely and effective control measures according to disease and pest occurrence trend prediction results; Decision support: provide decision support including crop planting layout optimization and yield prediction.
6. The method of claim 1, wherein the method further comprises: The method also includes feedback and optimization: collect output results of precision agriculture management and decision support and feedback information in agricultural production practice, for optimizing the whole system, forming a closed-loop feedback mechanism.
7. The method of claim 6, wherein the method further comprises: The feedback and optimization specifically includes: Feedback information collection: collect feedback information in agricultural production practice; System optimization: feedback information is adjusted, including data collection, preprocessing, feature engineering, model training and management decision module, to improve system performance and prediction accuracy.
8. A large model-based precision agriculture management and decision support system, characterized in that, Comprises: Data collection and integration module for integrating multi-source data; Data preprocessing and feature engineering module for processing data and extracting features; The deep learning large model construction and training module is configured to construct and train a deep learning model. The precision agriculture management and decision support module is configured to provide precision agriculture management suggestions and decision support. The system is capable of implementing the precision agriculture management and decision support method based on a large model according to any one of claims 1 to 7.
9. A large model-based precision agriculture management and decision support device, characterized by, Comprise: at least one memory and at least one processor; The at least one memory is configured to store machine readable programs. The at least one processor is configured to invoke the machine readable programs to implement the method according to any one of claims 1 to 7.
10. A computer readable medium characterized by The computer readable medium stores computer instructions, and the computer instructions are executed by the processor to implement the method according to any one of claims 1 to 7.