Smart cloud management method for wheat growth

Abstract

The invention provides a smart cloud management method for wheat growth, and relates to the technical field of smart agriculture. By integrating various sensor data and establishing a statistical model and a dynamic simulation model, intelligent decision support is realized, and a comprehensive, real-time and accurate solution is provided for wheat growth management. The method comprises the following steps: collecting real-time monitoring data of wheat at field monitoring points, establishing a statistical model of four factors of wheat seedling conditions based on the real-time monitoring data of the wheat, and establishing a field monitoring point seedling condition classification dynamic simulation model based on the statistical model of the four factors of the wheat seedling conditions; based on a field monitoring point seedling condition classification dynamic simulation model, the current growth condition of wheat is comprehensively analyzed in combination with historical data, weather forecast, soil humidity and nutrient content, and the future growth trend of wheat is predicted through a big data analysis technology. Based on a smart cloud management platform, real-time monitoring data visualization, seedling condition classification level query, historical data comparison and analysis, and prediction and early warning functions are realized.

Description

Technical Field

[0001] The present invention relates to the field of smart agricultural technology, and more specifically, to a smart cloud management method for wheat growth. Background Art

[0002] As one of the most important food crops in the world, wheat has a cultivation history that can be traced back to ancient times, and has been cultivated for more than 10,000 years. It is widely planted in all continents of the world, especially in the temperate regions of Asia, Europe and North America. The stable production of wheat is of immeasurable value in ensuring global food security, promoting economic development and maintaining social stability. However, with the increase in population growth, climate change and resource and environmental pressures, wheat production is facing unprecedented challenges.

[0003] In the traditional wheat growth management model, farmers mainly rely on long-term experience and regular field inspections to evaluate the growth status of wheat, and formulate management measures such as irrigation, fertilization, pest control, etc. This management method has many limitations: untimely data acquisition, subjective management decisions, low resource utilization efficiency, and lack of early warning and response mechanisms.

[0004] In recent years, with the rapid development of technologies such as the Internet of Things, big data, cloud computing, and artificial intelligence, smart agriculture has emerged, providing new ideas and methods for solving the above problems. Smart agriculture integrates a variety of sensors, smart devices, data analysis platforms, and decision support systems to achieve real-time monitoring, precise management, and intelligent decision-making for the entire agricultural production process. However, although smart agricultural technology has made significant progress in many fields, in the field of wheat growth management, the market still lacks a comprehensive, precise, and efficient smart cloud management method. Summary of the invention

[0005] The purpose of the present invention is to provide a comprehensive, accurate and efficient smart cloud management method for wheat growth, so as to perform real-time monitoring, accurate analysis and intelligent decision-making on the wheat growth process.

[0006] In order to achieve the above object, the present invention provides a smart cloud management method for wheat growth, the method comprising:

[0007] Step S1: collecting real-time monitoring data of wheat at field monitoring points, sending the real-time monitoring data of wheat to the smart cloud management platform, and preprocessing and storing the data, wherein the real-time monitoring data of wheat includes wheat growth environment parameters and crop growth parameters;

[0008] Step S2: Based on the real-time monitoring data of wheat, a statistical model of four elements of wheat seedling condition is established, and based on the statistical model of the four elements of wheat seedling condition, a dynamic simulation model for seedling condition classification at field monitoring points is established;

[0009] Step S3: using the established field monitoring point seedling classification dynamic simulation model, combined with historical data, weather forecasts, soil moisture, and nutrient content, a comprehensive analysis of the current growth status of wheat is conducted, and the future growth trend of wheat is predicted through big data analysis technology;

[0010] Step S4: Based on the smart cloud management platform, realize real-time monitoring data visualization, seedling classification level query, historical data comparison and analysis, and prediction and early warning functions.

[0011] Preferably, the wheat growth environment parameters include: air temperature, air humidity and light exposure time.

[0012] Preferably, the four elements of wheat seedling condition include: main stem leaf age, number of tillers per plant, number of secondary roots and total number of tillers.

[0013] Preferably, in step S2, a statistical model of four elements of wheat seedling condition is established based on the real-time monitoring data of wheat, including:

[0014] The accumulated temperature is calculated based on the near-ground air temperature value monitored in real time at the field monitoring point. Based on the accumulated temperature data, the relationship model between the main stem leaf age and the accumulated temperature is statistically established. The formula is:

[0015] MSLA=T / 80-1.5

[0016] Among them, T is the accumulated temperature reaching the overwintering period, 80 is when the accumulated temperature ≥0℃ reaches 120℃, from sowing to emergence, each leaf growth requires about 80℃ accumulated temperature, and MSLA is the main stem leaf age of wheat;

[0017] According to the rule of simultaneous extension of wheat leaves and stem tillers, a statistical model of the relationship between the stem tiller age of a single plant and the leaf age of the main stem was established. The formula is:

[0018] TPP=MSLA-3

[0019] Among them, TPP is the number of tillers per wheat plant;

[0020] According to the corresponding change law of the quantitative relationship between secondary roots and individual plants, a statistical model of the relationship between secondary roots and individual plant stem tillers was established, and the formula is:

[0021] SRPP=TPP×α

[0022] Among them, SRPP is the number of secondary roots of wheat, α is the secondary root reduction coefficient;

[0023] According to the calculation relationship between total wheat tillers and wheat sowing amount, germination rate, and tillers per plant, a relationship model between total stems and leaves and tillers per plant was established. The formula is:

[0024] GS=(S×GR)×SRPP

[0025] Among them, S is the seeding amount and GR is the germination rate.

[0026] Preferably, in step S2, a dynamic simulation model for seedling condition classification of field monitoring points is established based on a statistical model of four elements of wheat seedling condition, comprising:

[0027] The statistical model of the four elements of wheat seedling condition was classified and calculated using the biostatistics standard deviation weight method. The standard deviation weight method was used to assign corresponding weights for statistical calculations and establish the wheat seedling condition index. The formula is:

[0028]

[0029] Among them, x i (i=1,2,3,4) represent the characteristic values ​​of wheat main stem leaf age, single plant tiller, total tiller, and secondary root, respectively. is the corresponding average value, α i is the corresponding weight;

[0030] A dynamic simulation model for wheat seedling condition classification at field monitoring points was established based on the wheat seedling condition index.

[0031] Preferably, the step of establishing a dynamic simulation model for classifying seedling conditions at field monitoring points according to the wheat seedling condition index comprises:

[0032] According to the wheat planting distribution and geographic information of the field monitoring points, the optimal discrete point spatial difference algorithm is screened and determined. The spatial structure model of the sample points is constructed by using the statistical laws and spatial structure distribution characteristics of the sample points. The discrete point wheat seedling index is converted into a continuous data surface. Based on the continuous data surface, a dynamic simulation model for seedling condition classification of field monitoring points is established, and a dynamic map of seedling condition contour lines based on the real-time monitoring data of the field monitoring points is output.

[0033] Preferably, the selection of the optimal discrete point spatial difference algorithm is based on multi-criteria decision analysis, based on the algorithm's computational efficiency, interpolation accuracy, robustness to outliers, and ability to maintain spatial autocorrelation, and by comparing the performance of different algorithms in simulating the spatial distribution of wheat seedlings, the algorithm that best suits the data characteristics of the current field monitoring points is determined.

[0034] Preferably, the smart cloud management platform automatically generates irrigation, fertilization, and pest and disease control measures for wheat growth management based on the analysis results of the dynamic simulation model for wheat seedling classification, and pushes them to farmers through the terminal.

[0035] Compared with the prior art, the smart cloud management method for wheat growth provided by the present invention has the following beneficial effects: The present invention provides a smart cloud management method for wheat growth, the method comprising: step S1: collecting real-time monitoring data of wheat at field monitoring points, sending the real-time monitoring data of wheat to a smart cloud management platform, and preprocessing and storing the data, wherein the real-time monitoring data of wheat includes wheat growth environment parameters and crop growth parameters; step S2: establishing a statistical model of four elements of wheat seedling conditions based on the real-time monitoring data of wheat, and establishing a dynamic simulation model for seedling condition classification at field monitoring points based on the statistical model of the four elements of wheat seedling conditions; step S3: utilizing the established dynamic simulation model for seedling condition classification at field monitoring points, combined with historical data, weather forecasts, soil moisture, and nutrient content, to conduct a comprehensive analysis of the current growth status of wheat, and predict future growth trends of wheat through big data analysis technology; step S4: based on the smart cloud management platform, realizing visualization of real-time monitoring data, query of seedling condition classification levels, comparative analysis of historical data, and prediction and early warning functions. The present invention integrates data from multiple sensors, establishes a statistical model and a dynamic simulation model based on the four elements of wheat seedling conditions, and realizes intelligent decision support, thereby providing a comprehensive, real-time and accurate solution for wheat growth management and meeting the needs of modern agriculture for refined management.

[0036] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 work.

[0038] Figure 1 A flow chart of a smart cloud management method for wheat growth provided by an embodiment of the present invention is shown;

[0039] Figure 2 A schematic diagram of the structure design of a wireless sensor network for field monitoring points provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0040] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0041] 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 one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0042] As an important food crop in the world, wheat growth management directly affects food yield and quality. Traditional wheat growth management methods have problems such as delayed data acquisition, strong subjectivity in decision-making, and low resource utilization efficiency.

[0043] Based on this, an embodiment of the present invention provides a smart cloud management method for wheat growth. Figure 1 A flow chart of a smart cloud management method for wheat growth provided by an embodiment of the present invention is shown, such as Figure 1 As shown, the method includes:

[0044] Step S1: Collect real-time monitoring data of wheat at field monitoring points, send the real-time monitoring data of wheat to the smart cloud management platform, and pre-process and store it. The real-time monitoring data of wheat includes wheat growth environment parameters and crop growth parameters.

[0045] It should be noted that if Figure 2 As shown, a variety of sensors are deployed in the field, such as temperature sensors, humidity sensors, light sensors, and image acquisition equipment for monitoring crop growth parameters, etc., to collect real-time monitoring data of wheat at field monitoring points, including wheat growth environment parameters and crop growth parameters. Specifically, wheat growth environment parameters can be air temperature, air humidity, and light duration, and crop growth parameters can be main stem leaf age, number of tillers per plant, number of secondary roots, and total number of tillers. The collected data is transmitted to the smart cloud management platform via a wireless network to ensure the timeliness and accuracy of the data. The received data is pre-processed and stored, such as cleaning, denoising, and verification, to improve data quality and provide a reliable basis for subsequent analysis.

[0046] Step S2: Based on the real-time monitoring data of wheat, a statistical model of the four elements of wheat seedling condition is established, and based on the statistical model of the four elements of wheat seedling condition, a dynamic simulation model for seedling condition classification at field monitoring points is established.

[0047] It should be noted that the four elements of wheat seedling conditions include: main stem leaf age, number of tillers per plant, number of secondary roots and total number of tillers. Main stem leaf age refers to the number of leaves on the main stem and their growth stage. Leaf age is an important indicator in the growth process of crops, which reflects the growth and development process and growth rate of crops. In the growth process of wheat, different leaf ages correspond to different growth stages and management measures. The number of tillers per plant is the total number of all tillers on a wheat plant, including main stems and tillers. Tillers are the above-ground parts of wheat, and their number directly affects the photosynthesis area and nutrient absorption capacity of wheat, thus having an important impact on wheat yield. The number of secondary roots is the number of roots in the wheat root system that grow from the base of the stem in addition to the main root. Secondary roots are the main organs for wheat to absorb water and nutrients, and their number and growth conditions are directly related to the growth rate and stress resistance of wheat. The total number of tillers is the total number of tillers on all wheat plants within a certain area. This parameter reflects the growth status and density of the wheat population and is an important basis for evaluating the wheat growth environment and formulating field management measures.

[0048] Specifically, based on the real-time monitoring data of wheat, a statistical model of four elements of wheat seedling condition was established, including:

[0049] The accumulated temperature is calculated based on the near-ground air temperature values ​​monitored in real time at the field monitoring points, and a relationship model between the main stem leaf age and the accumulated temperature is established based on the historical accumulated temperature data and crop growth laws. This model can reflect the growth rate of wheat under different accumulated temperature conditions. The formula is:

[0050] MSLA=T / 80-1.5

[0051] Among them, T is the accumulated temperature reaching the overwintering period, 80 means when the accumulated temperature ≥0℃ reaches 120℃, from sowing to emergence, each leaf growth requires about 80℃ accumulated temperature, and MSLA is the leaf age of the wheat main stem.

[0052] According to the co-extension law of wheat leaves and stem tillers, combined with real-time monitoring data, a statistical model of the relationship between the stem tiller of a single plant and the leaf age of the main stem was established. The formula is:

[0053] TPP=MSLA-3

[0054] Among them, TPP is the number of tillers per wheat plant, and the number of tillers and the leaf age of the main stem usually have a MSLA-3 coexistence relationship.

[0055] The changing rules of the quantitative relationship between secondary roots and stem tillers of a single plant were analyzed, and a statistical model of the relationship between secondary roots and stem tillers of a single plant was established. The formula is:

[0056] SRPP=TPP×α

[0057] Among them, SRPP is the number of secondary roots of wheat, α is the secondary root reduction coefficient;

[0058] According to the calculation relationship between total wheat tillers and wheat sowing amount, germination rate, and tillers per plant, a relationship model between total stems and leaves and tillers per plant was established. The formula is:

[0059] GS=(S×GR)×SRPP

[0060] Among them, S is the seeding amount and GR is the germination rate.

[0061] The statistical model based on the four elements of wheat seedling condition is used to establish a dynamic simulation model for seedling condition classification at field monitoring points, including:

[0062] The statistical model of the four elements of wheat seedling condition was classified and calculated using the biostatistics standard deviation weight method. The standard deviation weight method was used to assign corresponding weights for statistical calculations and establish the wheat seedling condition index. The formula is:

[0063]

[0064] Among them, x i (i=1,2,3,4) represent the characteristic values ​​of wheat main stem leaf age, single plant tiller, total tiller, and secondary root, respectively. is the corresponding average value, α i is the corresponding weight;

[0065] According to the wheat seedling index, a dynamic simulation model for seedling classification at field monitoring points was established. Specifically, first, according to the wheat planting distribution and geographic information of the field monitoring points, combined with factors such as data accuracy and computational efficiency, the optimal discrete point spatial difference algorithm was screened and determined. Subsequently, the statistical laws and spatial structure distribution characteristics of the sample points were used to construct a spatial structure model that can reflect the spatial variability and correlation of wheat seedlings. Then, the discrete point wheat seedling index was converted into a smooth and continuous data surface through this model to retain the spatial distribution characteristics of the original data. Finally, based on the continuous data surface, a dynamic simulation model for seedling classification at field monitoring points was established. The model can update the seedling classification in real time and support the prediction of future seedling changes. At the same time, a dynamic map of seedling contour lines based on real-time monitoring data of field monitoring points is output to intuitively display the spatial distribution and dynamic changes of wheat seedlings, providing a strong basis for agricultural management and decision support.

[0066] Step S3: Using the established field monitoring point seedling classification dynamic simulation model, combined with historical data, weather forecasts, soil moisture, and nutrient content, a comprehensive analysis of the current growth status of wheat is conducted, and the future growth trend of wheat is predicted through big data analysis technology.

[0067] It should be noted that the real-time collected field monitoring point data, such as wheat growth environment parameters and crop growth parameters, are input into the model as the basic data for model operation. Based on the input data, the model simulates the growth process of wheat under different environmental conditions based on the wheat growth mechanism, taking into account factors such as wheat growth cycle, growth rate, and environmental adaptability. Time series analysis, regression analysis and other methods are used to combine historical data and real-time monitoring data to predict the future growth trend of wheat.

[0068] Step S4: Based on the smart cloud management platform, realize real-time monitoring data visualization, seedling classification level query, historical data comparison and analysis, and prediction and early warning functions.

[0069] It should be noted that the smart cloud management platform automatically generates irrigation, fertilization, and pest control measures for wheat growth management based on the analysis results of the dynamic simulation model of wheat seedling classification, and pushes them to farmers through the terminal. Through a user-friendly interface, real-time monitoring data, seedling classification levels, historical data comparison and analysis results, and intelligent decision-making suggestions are displayed to farmers. At the same time, important information is sent to farmers in a timely manner through text messages, APP push, etc., so that they can take timely measures.

[0070] Compared with the prior art, the smart cloud management method for wheat growth provided by the embodiment of the present invention has the following beneficial effects:

[0071] 1. Real-time monitoring of wheat growth environment parameters and crop growth parameters through a variety of sensors to ensure the timeliness and accuracy of data, providing a reliable basis for precise analysis.

[0072] 2. Establish a dynamic simulation model for seedling condition classification at field monitoring points to achieve dynamic update and future prediction of seedling condition classification, and provide a scientific basis for agricultural management.

[0073] 3. Based on big data analysis technology, it automatically generates intelligent decision-making suggestions on irrigation, fertilization, pest and disease control, etc. to improve the efficiency and effectiveness of agricultural management.

[0074] 4. Provide real-time monitoring data visualization, historical data comparison and analysis and other functions. Through a user-friendly interface and multiple notification methods, ensure that farmers can understand the wheat growth status in a timely manner and take corresponding measures.

[0075] In addition, an embodiment of the present invention also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and run on the processor. The transceiver, the memory, and the processor are respectively connected via a bus. When the computer program is executed by the processor, the various processes of the above-mentioned embodiment of the smart cloud management method for wheat growth are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0076] In addition, an embodiment of the present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the various processes of the above-mentioned embodiment of the smart cloud management method for wheat growth are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0077] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technical solution that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A smart cloud management method for wheat growth, characterized in that: include: Step S1: collecting real-time monitoring data of wheat at field monitoring points, sending the real-time monitoring data of wheat to the smart cloud management platform, and preprocessing and storing the data, wherein the real-time monitoring data of wheat includes wheat growth environment parameters and crop growth parameters; Step S2: Based on the real-time monitoring data of wheat, a statistical model of four elements of wheat seedling condition is established, and based on the statistical model of the four elements of wheat seedling condition, a dynamic simulation model for seedling condition classification at field monitoring points is established; Step S3: using the established field monitoring point seedling classification dynamic simulation model, combined with historical data, weather forecasts, soil moisture, and nutrient content, a comprehensive analysis of the current growth status of wheat is conducted, and the future growth trend of wheat is predicted through big data analysis technology; Step S4: Based on the smart cloud management platform, realize real-time monitoring data visualization, seedling classification level query, historical data comparison and analysis, and prediction and early warning functions.

2. A smart cloud management method for wheat growth according to claim 1, characterized in that: The wheat growth environment parameters include: air temperature, air humidity and light exposure time.

3. The smart cloud management method for wheat growth according to claim 1, characterized in that: The four elements of wheat seedling condition include: main stem leaf age, number of tillers per plant, number of secondary roots and total number of tillers.

4. The smart cloud management method for wheat growth according to claim 3, characterized in that: In step S2, a statistical model of four elements of wheat seedling condition is established based on the real-time monitoring data of wheat, including: The accumulated temperature is calculated based on the near-ground air temperature value monitored in real time at the field monitoring point. Based on the accumulated temperature data, the relationship model between the main stem leaf age and the accumulated temperature is statistically established. The formula is: MSLA=T / 80-1.5 Among them, T is the accumulated temperature reaching the overwintering period, 80 is when the accumulated temperature ≥0℃ reaches 120℃, from sowing to emergence, each leaf growth requires about 80℃ accumulated temperature, and MSLA is the main stem leaf age of wheat; According to the rule of simultaneous extension of wheat leaves and stem tillers, a statistical model of the relationship between the stem tiller age of a single plant and the leaf age of the main stem was established. The formula is: TPP=MSLA-3 Among them, TPP is the number of tillers per wheat plant; According to the corresponding change law of the quantitative relationship between secondary roots and individual plants, a statistical model of the relationship between secondary roots and individual plant stem tillers was established, and the formula is: SRPP=TPP×α Among them, SRPP is the number of secondary roots of wheat, α is the secondary root reduction coefficient; According to the calculation relationship between total wheat tillers and wheat sowing amount, germination rate, and tillers per plant, a relationship model between total stems and leaves and tillers per plant was established. The formula is: GS=(S×GR)×SRPP Among them, S is the seeding amount and GR is the germination rate.

5. The intelligent cloud management method for wheat growth according to claim 4, characterized in that: In step S2, a dynamic simulation model for seedling condition classification of field monitoring points is established based on a statistical model of four elements of wheat seedling condition, including: The statistical model of the four elements of wheat seedling condition was classified and calculated using the biostatistics standard deviation weight method. The standard deviation weight method was used to assign corresponding weights for statistical calculations and establish the wheat seedling condition index. The formula is: Among them, x i (i=1,2,3,4) represent the characteristic values ​​of wheat main stem leaf age, single plant tiller, total tiller, and secondary root, respectively. is the corresponding average value, α i is the corresponding weight; A dynamic simulation model for wheat seedling condition classification at field monitoring points was established based on the wheat seedling condition index.

6. A smart cloud management method for wheat growth according to claim 5, characterized in that: The method of establishing a dynamic simulation model for classifying seedling conditions at field monitoring points according to the wheat seedling condition index comprises: According to the wheat planting distribution and geographic information of the field monitoring points, the optimal discrete point spatial difference algorithm is screened and determined. The spatial structure model of the sample points is constructed by using the statistical laws and spatial structure distribution characteristics of the sample points. The discrete point wheat seedling index is converted into a continuous data surface. Based on the continuous data surface, a dynamic simulation model for seedling condition classification of field monitoring points is established, and a dynamic map of seedling condition contour lines based on the real-time monitoring data of the field monitoring points is output.

7. The smart cloud management method for wheat growth according to claim 1, characterized in that: The selection of the optimal discrete point spatial difference algorithm is based on multi-criteria decision analysis. Based on the algorithm's computational efficiency, interpolation accuracy, robustness to outliers, and ability to maintain spatial autocorrelation, the algorithm that best suits the current field monitoring point data characteristics is determined by comparing the performance of different algorithms in simulating the spatial distribution of wheat seedlings.

8. The intelligent cloud management method for wheat growth according to claim 1, characterized in that: The smart cloud management platform automatically generates irrigation, fertilization, and pest and disease control measures for wheat growth management based on the analysis results of the dynamic simulation model for wheat seedling classification, and pushes them to farmers through the terminal.

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