Method and system for full-cycle statistics of corn planting and growth conditions

Abstract

The invention discloses a method and a system for full-cycle statistics of corn planting and growth conditions. The system comprises a data acquisition module which acquires data of a corn growth environment in real time through various sensors arranged in a field; images of a corn field are regularly shot through an image acquisition device and used for monitoring the growth vigor and pest and disease damage conditions of corn; manual records are uploaded, and in each growth stage, professionals carry out field observation, record and upload data; the data processing and analysis module is used for data integration, data analysis and model prediction; and the information display and feedback module is used for displaying the growth condition. According to the method, statistical analysis of corn planting and growth conditions can be better carried out.

Description

Technical Field

[0001] The present invention relates to the field of corn planting, and in particular to a method and system for counting corn planting growth conditions throughout the entire cycle. Background Art

[0002] When collecting statistics on corn planting and growth, modern information technology, such as the Internet of Things, remote sensing technology, and geographic information systems (GIS), is used to conduct real-time monitoring and statistical analysis of key indicators such as corn planting area, growth cycle, growth environment, and yield forecast. By collecting and analyzing large amounts of data, the system can provide strong decision-making support for agricultural producers, policymakers, and scientific research institutions.

[0003] The existing corn planting and growth statistics system and method have the problems of single statistical mode and poor accuracy, which has brought certain impact on the use of the corn planting and growth statistics system and method. Therefore, a method and system for full-cycle statistics of corn planting and growth is proposed. Summary of the invention

[0004] The technical problem to be solved by the present invention is: how to solve the problem that the existing corn planting and growth statistics system and method have a single statistical model and poor accuracy, which has a certain impact on the use of the corn planting and growth statistics system and method, and provide a method and system for full-cycle statistics of corn planting and growth.

[0005] The present invention solves the above technical problems through the following technical solutions, which include:

[0006] Data acquisition module: The data acquisition module collects data on the corn growth environment in real time through various sensors arranged in the field;

[0007] The image acquisition equipment is used to regularly take images of corn fields to monitor the growth, pests and diseases of corn;

[0008] Upload manual records. At each growth stage, professionals will conduct field observations, record and upload data;

[0009] Data processing and analysis module: The data processing and analysis module is used for data integration, data analysis and model prediction;

[0010] Information display and feedback module: used to display growth status.

[0011] Furthermore, the data acquisition module includes three parts: a sensor network, an image acquisition device and a manual recorder;

[0012] The sensor network monitors soil moisture, temperature, and light intensity environmental parameters in real time and continuously through sensors installed in the field;

[0013] Image acquisition equipment uses drones, ground robots or fixed cameras to capture images of corn fields regularly or on demand. The collected images are used to analyze the growth, pest and disease conditions, and growth density of corn;

[0014] Manual records are used for on-site observation and recording by professionals at relevant stages of corn growth and for uploading relevant information.

[0015] Furthermore, the specific process of data integration is as follows:

[0016] Data collection: ensuring data from all data sources can be effectively captured and stored;

[0017] Data cleaning: remove duplicate data, process missing values, correct erroneous data, and improve data quality;

[0018] Data conversion: convert data in different formats and units into a unified format and unit;

[0019] Data merging: Merge the cleaned and converted data into a comprehensive data set according to certain logical relationships.

[0020] Furthermore, the specific content of the data analysis is as follows:

[0021] Descriptive statistical analysis: calculate the mean, standard deviation, maximum and minimum statistics of the data to describe the basic characteristics of the data;

[0022] Correlation analysis: Analyze the correlation between different variables;

[0023] Time series analysis: analyzing trends and periodicity of data over time;

[0024] Machine Learning: Use machine learning algorithms to perform classification, regression, cluster analysis on data and identify complex patterns and relationships.

[0025] Furthermore, the specific process of the model prediction is as follows:

[0026] Model selection: Select a prediction model based on the nature of the problem and the characteristics of the data, including linear regression, decision tree, and neural network;

[0027] Model training: Use historical data to train the model and adjust model parameters to optimize predictive performance;

[0028] Model validation: The prediction accuracy and generalization ability of the model are evaluated through cross-validation methods;

[0029] Predictive application: Apply the trained model to current or future data to predict corn yield and quality indicators.

[0030] Furthermore, the specific content of the growth status display is as follows:

[0031] Growth rate display: Display the growth rate of corn at different growth stages through line graphs or bar graphs, including changes in plant height and leaf area indicators;

[0032] Pest and disease distribution display: Use maps or heat maps to display the distribution of pests and diseases in corn fields. Use color depth and density to indicate the severity of pests and diseases;

[0033] Environmental parameter display: display the changes of environmental parameters;

[0034] Decision support: Provide users with decision support for field management based on data analysis results.

[0035] Furthermore, the content of decision support includes:

[0036] Irrigation decision support: Analyze sensor data to understand the current soil moisture content. At the same time, combine meteorological data and the water demand characteristics of crop growth stages to comprehensively evaluate the irrigation needs of corn, and give irrigation recommendations based on irrigation needs, including irrigation time, amount, and irrigation method;

[0037] Fertilization decision support: by analyzing the growth of corn and soil nutrient content data, the nutrient requirements of corn are evaluated. Based on the results of nutrient requirement analysis, fertilization recommendations are given, including the time, type, amount and method of fertilization.

[0038] Pest and disease control decision support: corn field images taken by image acquisition equipment combined with image recognition technology can monitor the occurrence of pests and diseases in real time, and give corresponding prevention and control suggestions based on the results of pest and disease monitoring;

[0039] A method for counting corn planting and growth conditions throughout the entire cycle, the method comprising the following steps:

[0040] Step 1: Various sensors placed in the field collect data on the corn growth environment in real time;

[0041] Step 2: Regularly take images of the corn field using image acquisition equipment to monitor the growth, disease and insect pest conditions of the corn;

[0042] Step 3: At each growth stage, professionals conduct on-site observations, record and upload data;

[0043] Step 4: Perform data integration, data analysis and model prediction;

[0044] Step 5: Display the growth status.

[0045] Compared with the prior art, the present invention has the following advantages: the method and system for the full-cycle statistics of corn planting and growth, the full-cycle statistics system of corn planting and growth is a comprehensive system integrating data collection, processing, analysis and display. By using advanced technologies such as the Internet of Things, remote sensing, artificial intelligence and big data, the system can comprehensively and accurately monitor and analyze the growth of corn, and provide scientific basis and decision support for field management. With the continuous advancement of technology and the in-depth promotion of application, the system will play an increasingly important role in improving corn yield, quality and management efficiency. The three data collection methods of sensor network, image acquisition equipment and manual recording complement each other and together constitute a comprehensive and three-dimensional monitoring system. By comprehensively applying these three methods, all-round monitoring and precise management of the corn growth environment can be achieved, providing a strong guarantee for the high-yield, high-quality and efficient production of corn, making the system and method more worthy of promotion and use. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0047] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation method and a specific operation process are given, but the protection scope of the present invention is not limited to the following embodiment.

[0048] like Figure 1 As shown, this embodiment provides a technical solution: a system for counting corn planting growth conditions throughout the entire cycle, including:

[0049] Sensor network: Various sensors (such as soil moisture sensors, temperature sensors, light intensity sensors, etc.) arranged in the field are used to collect data on the corn growth environment in real time.

[0050] Image acquisition equipment: Use drones, ground robots or fixed cameras to regularly capture images of corn fields to monitor corn growth, pests and diseases, etc.

[0051] Manual records: Professionals conduct on-site observation and records at key growth stages, such as sowing, seedling emergence, jointing, tasseling, silking, grain filling and maturity;

[0052] Data processing and analysis module: Data integration: Integrate the various collected data to form a complete corn growth data set.

[0053] Data analysis: Use statistical, machine learning and other methods to process and analyze data to identify key characteristics and trends in the corn growth process.

[0054] Model prediction: Based on historical data and current growth conditions, a prediction model is established to predict corn yield, quality, etc.

[0055] Information display and feedback module: Growth status display: Intuitively display the growth status of corn through charts, maps, etc., including growth rate, distribution of pests and diseases, etc.

[0056] Decision support: Provide decision support for field management based on the analysis results, such as suggestions on irrigation, fertilization, pest and disease control, etc.

[0057] User interaction: Provide a user-friendly interactive interface to facilitate users to query corn growth status, receive management suggestions, etc.

[0058] The full-cycle statistical system for corn planting and growth is a comprehensive system that integrates data collection, processing, analysis and display. By using advanced technologies such as the Internet of Things, remote sensing, artificial intelligence and big data, the system can comprehensively and accurately monitor and analyze the growth of corn, and provide scientific basis and decision-making support for field management. With the continuous advancement of technology and the in-depth promotion of its application, the system will play an increasingly important role in improving corn yield, quality and management efficiency. The data acquisition module plays a vital role in the agricultural monitoring system. It collects detailed information about the crop growth environment, as well as the growth status and health status of the crop in various ways. In the mentioned corn growth monitoring system, the data acquisition module mainly includes three parts: sensor network, image acquisition equipment and manual recording, each of which has its unique role and advantages.

[0059] Sensor Networks

[0060] Function: The sensor network monitors key environmental parameters such as soil moisture, temperature, and light intensity in real time and continuously through a variety of sensors installed in the field. These parameters are crucial to understanding the growing environment of corn because they directly affect the growth rate, yield, and quality of corn.

[0061] Advantages:

[0062] Real-time: Able to reflect environmental changes instantly and provide a basis for taking timely measures.

[0063] Accuracy: Sensor measurement data usually has high accuracy, which helps in the implementation of precision agriculture.

[0064] Automation: Reduce manual intervention and improve data collection efficiency.

[0065] Image acquisition equipment

[0066] Function: Image acquisition equipment uses high-tech means such as drones, ground robots or fixed cameras to regularly or on demand capture images of corn fields. These images are used to analyze information such as corn growth, pests and diseases, and growth density.

[0067] Advantages:

[0068] Wide coverage: High-altitude equipment such as drones can quickly cover large tracts of farmland and improve monitoring efficiency.

[0069] Visualization: Image data is intuitive and easy to understand, making it easy for non-professionals to understand and analyze.

[0070] Intelligent analysis: Combining image processing technology and artificial intelligence algorithms, it can automatically identify pests and diseases, evaluate crop growth status, etc.

[0071] Manual Recording

[0072] Function: Professionals conduct field observations and records at key stages of corn growth. These records usually include corn growth and development, pest and disease occurrence, soil conditions, etc.

[0073] Advantages:

[0074] Professional knowledge: Professionals have rich agricultural knowledge and practical experience and can accurately judge the growth status and potential problems of crops.

[0075] Flexibility: Manual recording can flexibly adjust the monitoring frequency and content according to actual conditions.

[0076] Emotional value: Manual records also reflect people’s emotional care and responsibility for crops, which helps to enhance the humanistic sentiment of agricultural production.

[0077] In the corn growth monitoring system, the three data collection methods of sensor network, image acquisition equipment and manual recording complement each other and together form a comprehensive and three-dimensional monitoring system. Through the comprehensive application of these three methods, all-round monitoring and precise management of the corn growth environment can be achieved, providing a strong guarantee for the high-yield, high-quality and efficient production of corn.

[0078] The specific process of data integration is as follows:

[0079] Data collection: ensuring data from all data sources can be effectively captured and stored;

[0080] Data cleaning: remove duplicate data, process missing values, correct erroneous data, and improve data quality;

[0081] Data conversion: convert data in different formats and units into a unified format and unit;

[0082] Data merging: Merge the cleaned and converted data into a comprehensive data set according to certain logical relationships.

[0083] The data processing and analysis module is a crucial part of the agricultural monitoring system. It is responsible for converting the collected raw data into valuable information and insights to support decision-making and optimize management. In the corn growth monitoring system, the data processing and analysis module mainly includes three core parts: data integration, data analysis and model prediction.

[0084] Data Integration

[0085] Function: Data integration is the process of collecting, cleaning, transforming and merging corn growth data from different sources (such as sensor networks, image acquisition devices and manual records) to form a complete, consistent and easy-to-analyze data set.

[0086] step:

[0087] Data collection: Ensure that data from all data sources is effectively captured and stored.

[0088] Data cleaning: Remove duplicate data, process missing values, correct erroneous data, etc. to improve data quality.

[0089] Data conversion: Convert data in different formats and units into a unified format and unit to facilitate subsequent analysis.

[0090] Data merging: Merge the cleaned and converted data into a comprehensive data set according to certain logical relationships.

[0091] Data analysis

[0092] Function: Data analysis is the use of statistics, machine learning and other methods to deeply explore data sets to identify key characteristics and trends in the corn growth process and reveal the laws and relationships behind the data.

[0093] method:

[0094] Descriptive statistical analysis: Calculate the mean, standard deviation, maximum value, minimum value and other statistics of the data to describe the basic characteristics of the data.

[0095] Correlation analysis: Analyze the correlation between different variables, such as the relationship between soil moisture and corn growth rate.

[0096] Time series analysis: Analyze trends and periodicity in data over time, such as the different stages in the growth cycle of corn.

[0097] Machine Learning: Use machine learning algorithms to analyze data through classification, regression, clustering, etc. to identify complex patterns and relationships.

[0098] Model predictions

[0099] Function: Based on historical data and current growth conditions, a forecasting model is established to predict key indicators such as corn yield and quality, providing forward-looking guidance for agricultural production.

[0100] step:

[0101] Model selection: Select an appropriate prediction model, such as linear regression, decision tree, neural network, etc., based on the nature of the problem and the characteristics of the data.

[0102] Model training: Use historical data to train the model and adjust model parameters to optimize prediction performance.

[0103] Model validation: Evaluate the model’s predictive accuracy and generalization ability through methods such as cross-validation.

[0104] Predictive application: Apply the trained model to current or future data to predict key indicators such as corn yield and quality.

[0105] Comprehensive Application

[0106] In the corn growth monitoring system, the data processing and analysis module converts raw data into valuable predictions and insights through three steps: data integration, data analysis, and model prediction. These predictions and insights can not only help farmers better understand the growth status of corn, but also guide them to take more accurate and effective management measures, such as adjusting irrigation, fertilization, pest and disease control strategies, etc., to optimize the growth environment of corn and improve yield and quality. At the same time, these predictions and insights can also provide important references for agricultural scientific research and policy making.

[0107] Furthermore, the specific content of the growth status display is as follows:

[0108] Growth rate display: Display the growth rate of corn at different growth stages through line graphs or bar graphs, including changes in plant height and leaf area indicators;

[0109] Pest and disease distribution display: Use maps or heat maps to display the distribution of pests and diseases in corn fields. Use color depth and density to indicate the severity of pests and diseases;

[0110] Environmental parameter display: display the changes of environmental parameters;

[0111] Decision support: Provide users with decision support for field management based on data analysis results.

[0112] Furthermore, the content of decision support includes:

[0113] Irrigation decision support: Analyze sensor data to understand the current soil moisture content. At the same time, combine meteorological data and the water demand characteristics of crop growth stages to comprehensively evaluate the irrigation needs of corn, and give irrigation recommendations based on irrigation needs, including irrigation time, amount, and irrigation method;

[0114] Fertilization decision support: by analyzing the growth of corn and soil nutrient content data, the nutrient requirements of corn are evaluated. Based on the results of nutrient requirement analysis, fertilization recommendations are given, including the time, type, amount and method of fertilization.

[0115] Pest and disease control decision support: corn field images taken by image acquisition equipment combined with image recognition technology can monitor the occurrence of pests and diseases in real time, and give corresponding prevention and control suggestions based on the results of pest and disease monitoring;

[0116] A method for counting corn planting and growth conditions throughout the entire cycle, the method comprising the following steps:

[0117] Step 1: Various sensors placed in the field collect data on the corn growth environment in real time;

[0118] Step 2: Regularly take images of the corn field using image acquisition equipment to monitor the growth, disease and insect pest conditions of the corn;

[0119] Step 3: At each growth stage, professionals conduct on-site observations, record and upload data;

[0120] Step 4: Perform data integration, data analysis and model prediction;

[0121] Step 5: Display the growth status.

[0122] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0123] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0124] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A system for counting corn planting and growth conditions throughout the entire cycle, characterized in that: include: Data acquisition module: The data acquisition module collects data on the corn growth environment in real time through various sensors arranged in the field; The image acquisition equipment is used to regularly take images of corn fields to monitor the growth, pests and diseases of corn; Upload manual records. At each growth stage, professionals will conduct field observations, record and upload data; Data processing and analysis module: The data processing and analysis module is used for data integration, data analysis and model prediction; Information display and feedback module: used to display growth status.

2. The system for collecting statistics on corn planting and growth in the whole cycle according to claim 1 is characterized in that: The data acquisition module includes three parts: sensor network, image acquisition equipment and manual recording; The sensor network monitors soil moisture, temperature, and light intensity environmental parameters in real time and continuously through sensors installed in the field; Image acquisition equipment uses drones, ground robots or fixed cameras to capture images of corn fields regularly or on demand. The collected images are used to analyze the growth, pest and disease conditions, and growth density of corn; Manual records are used for on-site observation and recording by professionals at relevant stages of corn growth and for uploading relevant information.

3. The system for counting corn planting and growth conditions throughout the entire cycle according to claim 1 is characterized in that: The specific process of data integration is as follows: Data collection: ensuring data from all data sources can be effectively captured and stored; Data cleaning: remove duplicate data, process missing values, correct erroneous data, and improve data quality; Data conversion: convert data in different formats and units into a unified format and unit; Data merging: Merge the cleaned and converted data into a comprehensive data set according to certain logical relationships.

4. The system for collecting statistics on corn planting and growth conditions throughout the entire cycle according to claim 1, characterized in that: The specific contents of the data analysis are as follows: Descriptive statistical analysis: calculate the mean, standard deviation, maximum and minimum statistics of the data to describe the basic characteristics of the data; Correlation analysis: Analyze the correlation between different variables; Time series analysis: analyzing trends and periodicity of data over time; Machine Learning: Use machine learning algorithms to perform classification, regression, cluster analysis on data and identify complex patterns and relationships.

5. The system for collecting statistics on corn planting and growth conditions throughout the entire cycle according to claim 1 is characterized in that: The specific process of model prediction is as follows: Model selection: Select a prediction model based on the nature of the problem and the characteristics of the data, including linear regression, decision tree, and neural network; Model training: Use historical data to train the model and adjust model parameters to optimize predictive performance; Model validation: The prediction accuracy and generalization ability of the model are evaluated through cross-validation methods; Predictive application: Apply the trained model to current or future data to predict corn yield and quality indicators.

6. The system for collecting statistics on corn planting and growth conditions throughout the entire cycle according to claim 1, characterized in that: The specific contents of the growth situation are as follows: Growth rate display: Display the growth rate of corn at different growth stages through line graphs or bar graphs, including changes in plant height and leaf area indicators; Pest and disease distribution display: Use maps or heat maps to display the distribution of pests and diseases in corn fields; use color depth and density to indicate the severity of pests and diseases; Environmental parameter display: display the changes of environmental parameters; Decision support: Provide users with decision support for field management based on data analysis results.

7. The system for collecting statistics on corn planting and growth conditions throughout the entire cycle according to claim 1, characterized in that: Decision support includes: Irrigation decision support: analyzing sensor data to understand the current soil moisture content; At the same time, combined with meteorological data and the water demand characteristics of the crop growth stage, the irrigation needs of corn are comprehensively evaluated, and irrigation recommendations are given based on the irrigation needs, including the time, amount and method of irrigation; Fertilization decision support: by analyzing the growth of corn and soil nutrient content data, the nutrient requirements of corn are evaluated. Based on the results of nutrient requirement analysis, fertilization recommendations are given, including the time, type, amount and method of fertilization. Pest and disease control decision support, through the corn field images taken by image acquisition equipment, combined with image recognition technology, can monitor the occurrence of pests and diseases in real time, and give corresponding prevention and control suggestions based on the results of pest and disease monitoring.

8. A method for collecting statistics on corn planting and growth throughout the entire cycle, the method being based on the system required by any one of claims 1 to 7, and characterized in that: The method comprises the following steps: Step 1: Various sensors placed in the field collect data on the corn growth environment in real time; Step 2: Regularly take images of the corn field using image acquisition equipment to monitor the growth, disease and insect pest conditions of the corn; Step 3: At each growth stage, professionals conduct on-site observations, record and upload data; Step 4: Perform data integration, data analysis and model prediction; Step 5: Display the growth status.

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