Precise fertilization method and system based on rice field nutrient real-time monitoring

Abstract

The invention relates to the technical field of agricultural fertilization, in particular to a precise fertilization method and system based on rice field nutrient real-time monitoring. The method comprises the following steps: acquiring map data of a rice field; dividing the rice field into a plurality of uniform areas by using a random sampling algorithm and an expansion algorithm; acquiring soil data and nutrient data of the rice field; constructing a rice field region comprehensive evaluation model used for comprehensively evaluating the difference between rice field region nutrients and soil characteristics; classifying the uniform regions according to the difference between nutrients and soil characteristics, and obtaining classified regions; constructing a rice field nutrient demand analysis model; constructing a rice field precise fertilization model; the fertilization amount is determined, and accurate graded fertilization is realized. According to the method, the nutrient and soil characteristics of the rice field are comprehensively evaluated in different regions, the fertilization priority sequence is determined, the nutrient requirements of all the regions are accurately matched to specific fertilizers and fertilization amounts, tight connection of the regions, the nutrients, the fertilizer types and the fertilization amounts is ensured, and precise fertilization management is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural fertilization, and in particular to a precision fertilization method and system based on real-time monitoring of rice field nutrients. Background Art

[0002] As an important production base for food crops, nutrient management in rice fields has a vital impact on crop yield and quality. However, traditional nutrient management methods in rice fields often rely on empirical judgment and regular manual testing, which is not only time-consuming and labor-intensive, but also difficult to accurately reflect the real-time changes in nutrients in rice fields. With the development of modern agricultural technology, real-time monitoring of nutrients in rice fields and precise fertilization based on this have become an important means to improve agricultural production efficiency and sustainability.

[0003] Although there are some rice field nutrient monitoring technologies currently, these technologies still have many defects in practical application. First, most of the existing technologies can only provide limited nutrient data, which is difficult to fully reflect the nutrient status of rice fields; second, the regional division methods of existing technologies are often too simple to achieve fine division of rice field areas, resulting in unbalanced nutrient management; third, the existing fertilization models are often based on static data and lack the ability to dynamically adjust, and cannot accurately predict and respond to real-time changes in rice field nutrient requirements; fourth, the impact of soil characteristics on nutrients is not considered during the fertilization process, resulting in inaccurate fertilization points. These problems limit the application effect of existing technologies in precision fertilization, resulting in low fertilizer utilization, serious nutrient loss, and increased environmental pollution.

[0004] In view of the defects of the prior art, the present invention proposes a precision fertilization method based on real-time monitoring of rice field nutrients. This method uses a random sampling algorithm and an expansion algorithm, combined with rice field map data, to achieve uniform division of rice field areas, significantly improving the precision and efficiency of rice field management; by constructing a comprehensive evaluation model for rice field areas, the differences in rice field nutrients and soil properties are comprehensively evaluated, providing a scientific basis for precision fertilization; by constructing a rice field nutrient demand analysis model and a rice field precision fertilization model, accurate quantification of rice field nutrient demand and precision fertilization are achieved. Therefore, the present invention has achieved remarkable innovative results in real-time monitoring of rice field nutrients and precision fertilization, and has broad application prospects and important social value. Summary of the invention

[0005] In view of the defects in the prior art, the present invention provides a precise fertilization method and system based on real-time monitoring of nutrients in rice fields.

[0006] In a first aspect, the present invention provides a precision fertilization method based on real-time monitoring of rice field nutrients, comprising the following steps: obtaining map data of the rice field, the map data including coordinate data and area data; based on the map data, dividing the rice field into multiple uniform areas using a random sampling algorithm and an expansion algorithm; according to the uniform areas, obtaining soil data and nutrient data of the rice field; the soil data including data on soil texture, water content, pH, and electrical conductivity, and the nutrient data including data on nitrogen, phosphorus, potassium, and organic matter; based on the soil data and the nutrient data, constructing a comprehensive evaluation model for rice field areas for comprehensively evaluating differences in nutrients and soil properties in rice field areas; according to the comprehensive evaluation model for rice field areas, classifying the uniform areas according to differences in nutrients and soil properties and obtaining classified areas; based on the classified areas, constructing a rice field nutrient demand analysis model; according to the rice field nutrient demand analysis model, constructing a rice field precision fertilization model; through the rice field precision fertilization model, determining the amount of fertilizer to achieve precise graded fertilization. The present invention uses random sampling algorithm and expansion algorithm, combined with rice field map data, to evenly divide the rice field area, effectively improving the precision and efficiency of rice field management and ensuring the accuracy of nutrient demand analysis; by constructing a comprehensive assessment model for rice field areas, the differences in rice field nutrients and soil properties are comprehensively assessed, providing a scientific basis for precision fertilization; by constructing and utilizing a rice field nutrient demand analysis model and a rice field precision fertilization model for precise graded fertilization, it not only significantly improves fertilizer utilization, reduces nutrient loss and environmental pollution, but also promotes sustainable production of rice fields, providing strong support for green agricultural development.

[0007] Optionally, the method of dividing the paddy field into a plurality of uniform areas based on the map data by using a random sampling algorithm and an expansion algorithm includes: constructing a dynamic equilibrium area division model based on the map data by using a random sampling algorithm and an expansion algorithm; dividing the paddy field into a plurality of uniform areas by using the dynamic equilibrium area division model. The present invention constructs a dynamic equilibrium area division model by using a random sampling algorithm and an expansion algorithm, thereby effectively improving the flexibility and adaptability of paddy field area division and ensuring the balance of nutrient monitoring and management in various paddy field areas; by applying the dynamic equilibrium area division model, the division of paddy field areas is made more scientific and reasonable, avoiding the problems of uneven area division and unbalanced nutrient management that may exist in traditional methods, and improving the accuracy and efficiency of nutrient monitoring.

[0008] Optionally, the dynamic balancing area division model satisfies the following expression: , in, For the In the iteration The position of the coordinate point, For the In the iteration The position of the coordinate point, For the In the iteration The position of the coordinate point, is the iteration step factor, is the preset equilibrium area, For the In the iteration The area of ​​the region, for and The distance between is the adjustment range of the coordinate point position, is the total number of extracted coordinate points. The present invention continuously adjusts the coordinate point positions by using an iterative algorithm to ensure that the area of ​​each region is close to the preset balanced area, effectively improving the accuracy and balance of regional division, and providing a reliable basis for real-time nutrient monitoring and precise fertilization; by setting the iterative step factor and the coordinate point position adjustment range, the flexibility and adjustability of regional division are enhanced, making the division of rice field areas more in line with actual needs, and improving the pertinence and efficiency of nutrient management; by comprehensively considering the distance between each coordinate point and the difference in regional area, the balanced division of rice field areas is achieved, blind spots and repeated monitoring in nutrient monitoring are avoided, and the configuration of nutrient resources is optimized.

[0009] Optionally, constructing a paddy field region comprehensive assessment model for comprehensively assessing differences in nutrients and soil properties in paddy field regions based on the soil data and the nutrient data includes: constructing a paddy field region nutrient assessment model for assessing differences in nutrients in paddy field regions based on the soil data and the nutrient data; constructing a paddy field region soil property assessment model for assessing differences in soil properties in paddy field regions based on the soil data and the nutrient data; constructing a paddy field region comprehensive assessment model for comprehensively assessing differences in nutrients and soil properties in paddy field regions through the paddy field region nutrient assessment model and the paddy field region soil property assessment model. The present invention realizes the comprehensive quantification of the nutrient status of the paddy field area by constructing a paddy field area nutrient assessment model, and effectively improves the accuracy of nutrient management; by constructing a paddy field area soil property assessment model, a scientific basis is provided for paddy field soil improvement and fertility enhancement; by utilizing the paddy field area nutrient assessment model and the paddy field area soil property assessment model, a paddy field area comprehensive assessment model is constructed, which comprehensively and objectively reflects the differences in nutrients and soil properties of the paddy field area, and provides strong decision-making support for the precise management and optimized layout of paddy fields.

[0010] Optionally, the paddy field area nutrient assessment model satisfies the following expression: , in, For the Nutrient assessment index for rice fields, For the Rice field area The standardized value of each nutrient index, For the Rice field area The mean value of nutrient indexes, For the Rice field area The standard deviation of each nutrient index, For the Rice field area The weight of each nutrient index; the paddy field area soil property evaluation model satisfies the following expression: , in, For the Soil property evaluation index for paddy fields, For the Rice field area The measured values ​​of soil characteristic indicators, For the Rice field area The mean value of soil characteristic indexes, For the Rice field area The standard deviation of soil characteristic indexes, For the Rice field area The median of soil characteristic indicators, For the Rice field area The interquartile range of soil characteristic indicators, and For the Rice field area The adjustment parameters corresponding to each soil characteristic index are: For the Rice field area The weight parameter corresponding to the soil characteristic index; the comprehensive evaluation model of paddy field area satisfies the following expression: , in, For the Comprehensive evaluation index of nutrients and soil properties in paddy fields, For the Nutrient assessment index for rice fields, For the Soil property evaluation index for paddy fields, , is the weight coefficient. The present invention realizes the accurate quantification of the nutrient status of paddy fields by constructing a paddy field regional nutrient assessment model. The model comprehensively considers the standardized value, mean, standard deviation and weight of the nutrient index, effectively improves the scientificity and pertinence of nutrient management, and promotes the balanced utilization of paddy field nutrients; by constructing a paddy field regional soil property assessment model, the accurate quantification of paddy field soil properties is realized. The model not only considers the mean and standard deviation of soil property indicators, but also introduces the median, interquartile range and adjustment parameters, so that the assessment results are more comprehensive and accurate, providing strong support for soil improvement and fertility improvement; by constructing a paddy field regional comprehensive assessment model, a comprehensive and objective evaluation of paddy field regional nutrients and soil properties is realized, providing a scientific basis for the accurate management and optimized layout of paddy fields.

[0011] Optionally, the method of classifying the uniform area according to the difference in nutrients and soil characteristics and obtaining the classified area according to the comprehensive evaluation model of the paddy field area includes: setting the nutrient difference threshold of the comprehensive evaluation index of nutrients and soil characteristics of the paddy field area according to the comprehensive evaluation model of the paddy field area; comparing the comprehensive evaluation index of nutrients and soil characteristics of the paddy field area with the nutrient difference threshold to determine the nutrient-rich area and the nutrient-poor area; classifying the uniform area according to the nutrient-rich area and the nutrient-poor area, in combination with the paddy field area soil characteristic evaluation model, and obtaining the classified area, wherein the classified area includes a nutrient-rich area with excellent soil characteristics, a nutrient-rich area with poor soil characteristics, a nutrient-poor area with excellent soil characteristics, and a nutrient-poor area with poor soil characteristics. The present invention realizes the accurate classification of nutrients and soil characteristics of paddy field areas, greatly improving the accuracy and scientificity of classification; it can not only clearly judge the nutrient and soil characteristic status of paddy field areas, but also classify paddy field areas according to the comparison results, so that paddy field management is more refined, which is helpful to adopt differentiated management strategies for different types of paddy field areas.

[0012] Optionally, the construction of a paddy field nutrient demand analysis model based on the classification area includes: determining a fertilization priority ranking of paddy field areas based on the classification area, the fertilization priority ranking being as follows: areas with poor nutrients and poor soil characteristics > areas with poor nutrients but excellent soil characteristics > areas with rich nutrients but poor soil characteristics > areas with rich nutrients and excellent soil characteristics; and constructing a paddy field nutrient demand analysis model based on the fertilization priority ranking. The present invention achieves accurate identification of paddy field nutrient requirements by determining fertilization priority ranking of paddy field areas based on classification areas, and effectively improves the pertinence and efficiency of nutrient management; by placing areas with poor nutrients and poor soil characteristics at the top of the fertilization priority, it ensures that paddy field areas that need nutrients the most can be replenished first, which helps to quickly improve the nutrient status and soil quality of these areas; by constructing a paddy field nutrient demand analysis model, the nutrient requirements of different paddy field areas can be more scientifically predicted and planned, providing a strong basis for formulating reasonable and precise fertilization plans.

[0013] Optionally, the paddy field nutrient demand analysis model satisfies the following expression: , in, For the The rice field area The nutrient requirements of For the A comprehensive evaluation index of nutrients and soil properties in paddy fields. For the The basic requirement coefficient of the nutrient, For the The adjustment coefficient of the nutrient, For the Recommended application rates of nutrients: For the A comprehensive evaluation index of nutrients and soil properties in paddy fields. is the total area of ​​rice fields, For the Rice field area The evaluation index of the nutrients; the precision fertilization model satisfies the following expression: , in, For the The rice field area The amount of fertilizer applied for each nutrient, For the The rice field area The nutrient requirements of For the In the soil of rice fields The existing amount of nutrients, For the Fertilizer utilization rate of plant nutrients, For the Fertilizer The nutrient content of For the Fertilizer in the The preference coefficient of the rice field area, For the A comprehensive evaluation index of nutrients and soil properties in paddy fields. For the A comprehensive evaluation index of nutrients and soil properties in paddy fields. For the Rice field area The present invention realizes the accurate quantification of the nutrient demand of paddy fields by constructing a paddy field nutrient demand analysis model, which greatly improves the scientific nature of nutrient management; by constructing a precise fertilization model, the amount of fertilizer to be applied in each paddy field area is accurately calculated according to the actual nutrient demand of the paddy field and the existing nutrients in the soil, as well as the nutrient content and utilization rate of the fertilizer and other factors, which not only avoids excessive or insufficient application of nutrients, but also improves the utilization rate of fertilizers, reduces nutrient loss and environmental pollution, and provides strong support for the sustainable production of paddy fields and environmental protection.

[0014] Optionally, the method of determining the amount of fertilizer through the precise fertilization model for rice fields and realizing precise graded fertilization includes: determining the specific amount of fertilizer of specific fertilizer corresponding to specific nutrients in a specific area through the precise fertilization model for rice fields, realizing the connection among area, nutrient, fertilizer type and amount of fertilizer, and the connection process includes: obtaining the fertilization priority ranking of rice field area; determining the nutrient requirement in the classified area based on the fertilization priority ranking; determining the type of fertilizer according to the nutrient requirement; determining the amount of fertilizer according to the type of fertilizer and the nutrient requirement; and performing precise graded fertilization according to the amount of fertilizer and the fertilization priority ranking. The present invention determines the amount of fertilizer through the precise fertilization model of rice fields, realizes the precise connection between regions, nutrients, fertilizer types and fertilizer amounts, and significantly improves the accuracy and efficiency of fertilization; by obtaining the fertilization priority ranking of rice field areas, priority management is performed according to the nutrient requirements of different classified areas, ensuring that areas in urgent need of nutrients are supplied in a timely manner; the type of fertilizer is determined by the nutrient demand, and the precise matching of fertilizer and nutrient demand is achieved, avoiding fertilizer waste and mismatch; the amount of fertilizer is determined by the type of fertilizer and nutrient demand, and precise graded fertilization is performed in combination with the fertilization priority ranking, which not only meets the nutrient demand of rice fields, but also optimizes the allocation of fertilizer resources, reduces environmental pollution, and promotes the sustainable development of agricultural production.

[0015] In a second aspect, the present invention provides a precision fertilization system based on real-time monitoring of rice field nutrients, comprising an input device, a processor, an output device and a memory, wherein the input device, the processor, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, the processor is configured to call the program instructions, and the system uses the precision fertilization method based on real-time monitoring of rice field nutrients. The present invention monitors the nutrient content in the rice field in real time and quickly adjusts the fertilization plan to ensure that the rice field obtains just the right nutrient supply, which not only avoids the waste and environmental pollution caused by excess nutrients, but also prevents the growth of crops caused by insufficient nutrients, thereby improving the efficiency and accuracy of fertilization; fertilization is carried out according to the actual nutrient demand of the rice field, avoiding the blindness and excessive application in traditional fertilization methods, which not only significantly improves the utilization rate of fertilizers and reduces nutrient loss, but also reduces production costs and increases the economic benefits of agricultural production; through precise fertilization, the excessive use of chemical fertilizers is reduced, the risk of pollution to soil and water bodies is reduced, which helps to maintain the balance of the rice field ecosystem, promotes soil health, and lays a solid foundation for the long-term sustainable development of agriculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of a precise fertilization method based on real-time monitoring of nutrients in paddy fields according to an embodiment of the present invention; Figure 2 A result diagram of rice field area division according to an embodiment of the present invention; Figure 3 This is a structural schematic diagram of a precision fertilization system based on real-time monitoring of paddy field nutrients according to an embodiment of the present invention; Figure 4 This is an operation flow chart of a precision fertilization system based on real-time monitoring of rice field nutrients according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other examples, in order to avoid confusing the present invention, known circuits, software or methods are not specifically described.

[0018] Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "in one embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and / or subcombination. In addition, it should be understood by those of ordinary skill in the art that the figures provided herein are for illustrative purposes and that the figures are not necessarily drawn to scale.

[0019] See also Figure 1 The embodiment of the present invention provides a precision fertilization method based on real-time monitoring of nutrients in paddy fields, the method comprising the following steps: S1. Acquire map data of a rice field, wherein the map data includes coordinate data and area data.

[0020] In one embodiment, the target paddy field area is first determined, and the accuracy and scope of the required map data are clarified.

[0021] Furthermore, satellite remote sensing image data of the target area is collected, with high-resolution images being given priority.

[0022] Furthermore, the satellite remote sensing images are preprocessed, including radiation correction, geometric correction and denoising.

[0023] Furthermore, the preprocessed images were classified into rice fields and other land objects using image classification algorithms.

[0024] Furthermore, the boundaries of the classified rice field areas are extracted to generate a vector layer of the rice field.

[0025] Furthermore, the vector layer is imported into the Geographic Information System (GIS) software.

[0026] Furthermore, projection conversion tools were used in GIS software to ensure the uniformity of the coordinate system of the rice field data.

[0027] Furthermore, the area calculation function of GIS software was used to calculate the area of ​​the rice field.

[0028] Furthermore, the coordinate data of the paddy field area is extracted, including the coordinates of the boundary points and the coordinates of the center point.

[0029] Furthermore, the coordinate data and area data are verified to ensure the accuracy and consistency of the data.

[0030] S2. Based on the map data, the paddy field is divided into a plurality of uniform areas using a random sampling algorithm and an expansion algorithm.

[0031] In one embodiment, based on step S1, a dynamic balanced area division model is constructed using a random sampling algorithm and an expansion algorithm, and the dynamic balanced area division model satisfies the following expression: , in, For the In the iteration The position of the coordinate point, For the In the iteration The position of the coordinate point, For the In the iteration The position of the coordinate point, is the iteration step factor, is the preset equilibrium area, For the In the iteration The area of ​​the region, for and The distance between is the adjustment range of the coordinate point position, is the total number of coordinate points extracted.

[0032] Furthermore, before each iteration, the coordinate point set obtained in step S1 is randomly sampled to generate a sampling point set. It should be noted that the sampling method can be simple random sampling or sampling according to a certain probability distribution, and the probability distribution includes uniform distribution and normal distribution. The sampling point set can be used for coordinate point position adjustment.

[0033] Furthermore, according to the sampling point set, the paddy field was preliminarily divided into This means that one extracted coordinate point corresponds to one area.

[0034] Furthermore, an iteration process is performed according to the dynamic balanced area division model to obtain new coordinate point positions.

[0035] Further, according to the new coordinate point position, it is checked whether the iteration meets the convergence condition, including whether the deviation between the area of ​​the region and the preset balanced area of ​​the region is within the allowable range and whether the number of iterations reaches the preset upper limit.

[0036] Specifically, after each iteration, it is checked whether the area of ​​each region is closer to the preset equilibrium region area. If the deviation between the areas of all regions and the preset equilibrium region area is within an acceptable range, it is considered that the equilibrium state is reached.

[0037] Further, it is determined whether the number of iterations reaches a preset upper limit. If so, the final coordinate point position is output; if not, the iteration is continued until the convergence condition is met and the final coordinate point position is output.

[0038] Furthermore, according to the positions of the final coordinate points, the paddy field areas are re-divided using GIS software, and the boundaries of each area are updated to obtain a plurality of uniform paddy field areas.

[0039] It should be noted that this embodiment combines the ideas of random sampling and regional expansion, and realizes uniform division of regional areas through a dynamic balanced regional division model. The algorithm realizes dynamic balance of regional areas by dynamically adjusting the location and number of sampling points.

[0040] See also Figure 2 , Figure 2 This is the actual result obtained by dividing the rice field area according to the dynamic equilibrium area division model. The uniform distribution of the square grids in this actual result is extremely high, which reflects the advanced nature of the algorithm.

[0041] S3. According to the uniform area, soil data and nutrient data of the paddy field are obtained; the soil data include data on soil texture, water content, pH, and electrical conductivity; and the nutrient data include data on nitrogen, phosphorus, potassium, and organic matter.

[0042] In one embodiment, based on the uniform area divided in step S2, soil data such as soil texture, water content, pH, conductivity, and nutrient data such as nitrogen, phosphorus, potassium, and organic matter of the paddy field are obtained.

[0043] Specifically, firstly, a soil sampler, a soil moisture meter, a pH meter, a conductivity meter, and a kit or equipment for nutrient analysis are prepared.

[0044] Furthermore, within each evenly divided paddy field area, the locations of the sampling points were evenly distributed according to the size and shape of the area, ensuring that each area had at least enough sampling points to represent the soil characteristics of the area.

[0045] Further, according to the determined sampling point location, soil samples are collected using a soil sampler. Note that the sampling depth should be consistent. Usually, the depth of the surface soil is between 0 and 20 cm to reflect the soil conditions of the main distribution layer of the crop root system.

[0046] Furthermore, the location information of each sampling point was recorded, as well as sampling time, weather conditions and other factors that may affect soil properties.

[0047] Furthermore, the moisture content of the collected soil samples is measured using a soil moisture meter or a drying method, and the moisture content data of each sample is recorded.

[0048] Furthermore, the pH of the soil samples was measured using a pH meter, and the pH value of each sample was recorded.

[0049] Furthermore, the conductivity of the soil samples was measured using a conductivity meter to reflect the salt content of the soil, and the conductivity data of each sample was recorded.

[0050] Furthermore, the soil samples were sent to the laboratory to analyze the soil texture, such as the proportion of sand, silt and clay, by sieving and hydrometer methods, and the soil texture data of each sample was recorded.

[0051] Furthermore, the nitrogen, phosphorus, potassium content, and organic matter content in the soil samples were determined using a test kit or specialized equipment. The nitrogen content was determined by the Kjeldahl method and ultraviolet spectrophotometry; the phosphorus content was determined by the molybdenum blue method and spectrophotometry; the potassium content was determined by flame photometry; and the organic matter content was determined by potassium dichromate oxidation-titration.

[0052] S4. Based on the soil data and the nutrient data, a comprehensive assessment model for paddy field areas is constructed for comprehensively assessing differences in nutrients and soil properties in paddy field areas.

[0053] Among them, S4 includes the following steps: S41. Based on the nutrient data, a paddy field regional nutrient assessment model is constructed for assessing nutrient differences among paddy field regions.

[0054] In one embodiment, based on the nutrient data obtained in step 3, a paddy field regional nutrient assessment model for assessing nutrient differences in paddy fields is constructed, and the paddy field regional nutrient assessment model satisfies the following expression: , in, For the Nutrient assessment index for rice fields, For the Rice field area The standardized value of each nutrient index is obtained by mapping the original data to the specified interval [0, 1] through linear transformation; For the Rice field area The mean value of nutrient indexes, For the Rice field area The standard deviation of each nutrient index, For the Rice field area The weight of each nutrient indicator is determined by data analysis method and meets the following conditions: , S42. Based on the soil data, a paddy field area soil property evaluation model is constructed for evaluating the differences in soil properties among paddy field areas.

[0055] In one embodiment, based on the soil data obtained in step 3, a paddy field region soil property evaluation model for evaluating the difference in soil properties in paddy fields is constructed, and the paddy field region soil property evaluation model satisfies the following expression: , in, For the Soil property evaluation index for paddy fields, For the Rice field area The measured values ​​of soil characteristic indicators, For the Rice field area The mean value of soil characteristic indexes, For the Rice field area The standard deviation of soil characteristic indexes, For the Rice field area The median of soil characteristic indicators, For the Rice field area The interquartile range of soil characteristic indicators, that is, the difference between the third quartile and the first quartile, is used to measure the dispersion of the data; and For the Rice field area The adjustment parameters corresponding to each soil characteristic index are: For the Rice field area The weight parameters corresponding to the soil characteristic indicators; and stated To control the soil properties The degree of influence and the robustness of the data, and meet the following conditions: , ; when When , it indicates a linear influence; when When , it indicates the effect of amplifying the difference; when , indicating the impact of reducing differences.

[0056] S43. A paddy field region comprehensive assessment model for comprehensively assessing differences in paddy field region nutrients and soil properties is constructed through the paddy field region nutrient assessment model and the paddy field region soil property assessment model.

[0057] In one embodiment, based on steps S41 and S42, a paddy field region comprehensive assessment model for comprehensively assessing differences in nutrients and soil properties in paddy fields is constructed, and the paddy field region comprehensive assessment model satisfies the following expression: , in, For the Comprehensive evaluation index of nutrients and soil properties in paddy fields, For the Nutrient assessment index for rice fields, For the Soil property evaluation index for paddy fields, , is the weight coefficient, which reflects the importance of nutrients and soil characteristics in the comprehensive assessment.

[0058] S5. According to the comprehensive assessment model of the paddy field area, the uniform area is classified according to the differences in nutrients and soil properties and classified areas are obtained.

[0059] In one embodiment, firstly, according to the paddy field area comprehensive assessment model, the nutrient difference threshold of the paddy field area nutrient and soil property comprehensive assessment index is set, and the nutrient difference threshold is ; Furthermore, the comprehensive evaluation index With the Perform a comparison to obtain a comparison result, which is as follows: when At that time, The nutrient content of each rice field area is low; when At that time, The nutrient content of the rice field area is high.

[0060] Furthermore, in combination with the paddy field area soil property assessment model in S42, the soil properties when the nutrient content is low and high are determined respectively according to the above threshold comparison method.

[0061] Furthermore, according to the differences in nutrients and soil properties, the uniform areas are classified to obtain classified areas, which include areas with rich nutrients and excellent soil properties, areas with rich nutrients but poor soil properties, areas with poor nutrients but excellent soil properties, and areas with poor nutrients and poor soil properties.

[0062] S6. Based on the classified areas, a rice field nutrient demand analysis model is constructed.

[0063] Wherein, S6 further comprises the following steps: S61. Based on the classified areas, determine the fertilization priority order of the rice field areas.

[0064] In one embodiment, based on the nutrient-rich and excellent soil property area, the nutrient-rich but poor soil property area, the nutrient-poor but excellent soil property area, and the nutrient-poor and poor soil property area obtained in step S5, the fertilization priority ranking of the paddy field area is determined, and the fertilization priority ranking is as follows: Areas with poor nutrient content and soil characteristics Areas with poor nutrients but excellent soil properties Areas with high nutrient content but poor soil characteristics Areas rich in nutrients and with excellent soil properties It should be noted that the fertilization priority is determined by comprehensively considering the nutrient conditions and soil characteristics of the paddy field area as a whole, and its purpose is to preliminarily determine the areas where key fertilization should be applied. Areas with higher priorities need more fertilization than areas with lower priorities.

[0065] S62. Based on the fertilization priority ranking, a rice field nutrient demand analysis model is constructed.

[0066] In one embodiment, based on the fertilization priority ranking determined in step S61, a paddy field nutrient demand analysis model is constructed, and the paddy field nutrient demand analysis model satisfies the following expression: , in, For the The rice field area The nutrient requirements of For the A comprehensive evaluation index of nutrients and soil properties in paddy fields. For the The basic nutrient requirement coefficient reflects the basic nutrient requirement of crops. For the The adjustment coefficient of crop nutrients reflects the impact of soil nutrient content on crop demand. For the Recommended application rates for each nutrient are based on crop type, growth stage, and target yield. For the A comprehensive evaluation index of nutrients and soil properties in paddy fields. is the total rice field area, For the Rice field area Evaluation index of nutrients; Satisfies the following relationship:

[0067] in, For the Rice field area The standardized value of each nutrient index, For the Rice field area The mean value of nutrient indexes, For the Rice field area The standard deviation of each nutrient index.

[0068] It should be noted that the paddy field nutrient demand analysis model can further narrow the area range based on the key fertilization area determined in step S61, and obtain the specific nutrient demand corresponding to the specific sub-area, which is conducive to precise fertilization.

[0069] S7. Construct a rice field precision fertilization model based on the rice field nutrient demand analysis model.

[0070] In one implementation, based on step S62, a rice field precision fertilization model is constructed, and the rice field precision fertilization model satisfies the following expression: , in, For the The rice field area The amount of fertilizer applied for each nutrient, For the The rice field area The nutrient requirements of For the In the soil of rice fields The existing amount of nutrients, For the Fertilizer utilization rate of plant nutrients, For the Fertilizer The nutrient content of For the Fertilizer in the The preference coefficient of the rice field area, For the A comprehensive evaluation index of nutrients and soil properties in paddy fields. For the A comprehensive evaluation index of nutrients and soil properties in paddy fields. For the Rice field area The evaluation index of nutrients. Obtained through soil testing, It reflects the proportion of nutrients in the fertilizer actually absorbed by the rice field, which is determined by the type of fertilizer, soil conditions, and climate conditions. Determined by cost-effectiveness, environmental impact, and soil adaptability, if the preference coefficient is high, it means the first This fertilizer is very suitable for rice field area. Therefore, by determining , you can determine the type of fertilizer.

[0071] It should be noted that the rice field precision fertilization model comprehensively considers multiple factors such as the rice field's demand for nutrients, the existing nutrients in the soil, fertilizer utilization rate, nutrient content in the fertilizer, and fertilizer applicability, aiming to provide accurate fertilization recommendations for the specific rice field area determined in step S6.

[0072] S8. Determine the amount of fertilizer to be applied through the rice field precision fertilization model to achieve precise graded fertilization.

[0073] In one embodiment, the rice field precision fertilization model is used to accurately determine the specific fertilizer amount corresponding to the specific nutrients in the specific area, thereby achieving the precise connection of the area, nutrients, fertilizer type, and fertilizer amount. The precise connection or precision fertilization process includes: Get the fertilizer priority ranking of rice field areas; Based on the fertilization priority ranking, sequentially determining the requirement of each nutrient in the classification area; Determine the type of fertilizer according to the nutrient requirement; Determining the amount of fertilizer to be applied according to the type of fertilizer and the nutrient requirement; According to the fertilization amount and in combination with the fertilization priority ranking, accurate graded fertilization is performed.

[0074] It should be noted that for areas with higher priority, the rice field precision fertilization model should be used first to determine the nutrients, fertilizer types, and fertilizer amounts.

[0075] Compared with the prior art, the present invention has the following significant advantages: First, it realizes the refined division and dynamic balanced management of rice field areas, and improves the accuracy and efficiency of nutrient monitoring; second, it constructs a comprehensive and objective comprehensive evaluation model for rice field areas, which provides a scientific basis for precise management and optimized layout; third, it realizes the accurate quantification and precise fertilization of rice field nutrient demand, significantly improves fertilizer utilization, and reduces nutrient loss and environmental pollution; fourth, it provides real-time monitoring and dynamic adjustment capabilities, can accurately predict and respond to real-time changes in rice field nutrient demand, and provides strong support for sustainable production of rice fields and environmental protection.

[0076] See also Figure 3 , Figure 3 The structure diagram of the precision fertilization system based on real-time monitoring of paddy field nutrients in an embodiment of the present invention is shown in FIG. The system includes an input device, a processor, an output device, and a memory. The input device, the processor, the output device, and the memory are interconnected. The memory is used to store a computer program. The computer program includes program instructions. The processor is configured to call the program instructions. The system uses the precision fertilization method based on real-time monitoring of paddy field nutrients. The specific operation process is as follows: Figure 4 shown.

[0077] In this embodiment, the input device includes a data acquisition module and a user interaction interface. The data acquisition module includes a multi-parameter nutrient sensor and a multi-parameter soil property sensor, which are used to collect data on the nutrient content and soil property parameters of paddy field soil in real time; the user interaction interface is used to input additional non-collection parameters.

[0078] The processor is the core component of the system, consisting of a high-performance microprocessor, including a data processing unit, a control unit and a communication interface. It is used to receive data transmitted by input devices and perform real-time processing and analysis; calculate the nutrient requirements of paddy field soil according to preset algorithms and models, and generate precise fertilization recommendations; and is responsible for controlling the operation of output devices and data exchange with the memory.

[0079] The output device is mainly composed of a fertilization control system, a touch screen control system, and an alarm device. The fertilization control system is connected to the processor through a wire to receive fertilization instructions and control fertilization operations; the touch screen control system is used to display information such as soil nutrient data and fertilization suggestions; the alarm device is used to sound an alarm when soil nutrients are abnormal or the fertilization system fails.

[0080] The memory adopts a high-speed solid-state hard disk, which has the characteristics of fast reading and writing speed, large capacity and high reliability. It is mainly used to store data obtained by the input device and the result data processed by the processor, and can meet the needs of large data storage.

[0081] In summary, the present invention provides a precision fertilization method based on real-time monitoring of rice field nutrients. By utilizing random sampling algorithm and expansion algorithm, combined with rice field map data, the rice field area is evenly divided, which effectively improves the precision and efficiency of rice field management and ensures the accuracy of nutrient demand analysis; by constructing a comprehensive evaluation model for rice field areas, the differences in rice field nutrients and soil characteristics are comprehensively evaluated, providing a scientific basis for precision fertilization; by constructing and utilizing a rice field nutrient demand analysis model and a rice field precision fertilization model for precise graded fertilization, not only the fertilizer utilization rate is significantly improved, nutrient loss and environmental pollution are reduced, but also the sustainable production of rice fields is promoted, providing strong support for the green development of agriculture.

[0082] The present invention provides a precision fertilization system based on real-time monitoring of rice field nutrients. By real-time monitoring of the nutrient content in the rice field, the fertilization plan is quickly adjusted to ensure that the rice field obtains just the right nutrient supply, which not only avoids waste and environmental pollution caused by excess nutrients, but also prevents crop growth from being hindered due to insufficient nutrients, thereby improving the efficiency and accuracy of fertilization; fertilization is carried out according to the actual nutrient demand of the rice field, avoiding the blindness and excessive application in traditional fertilization methods, which not only significantly improves the utilization rate of fertilizers and reduces nutrient loss, but also reduces production costs and increases the economic benefits of agricultural production; through precision fertilization, excessive use of chemical fertilizers is reduced, and the risk of pollution to soil and water bodies is reduced, which helps to maintain the balance of the rice field ecosystem, promotes soil health, and lays a solid foundation for the long-term sustainable development of agriculture.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A precision fertilization method based on real-time monitoring of rice field nutrients, characterized in that: The method comprises the following steps: Acquire map data of the rice field, wherein the map data includes coordinate data and area data; Based on the map data, the paddy field is divided into a plurality of uniform areas by using a random sampling algorithm and an expansion algorithm; According to the uniform area, soil data and nutrient data of the paddy field are obtained; the soil data includes data of soil texture, water content, pH, and conductivity; and the nutrient data includes data of nitrogen, phosphorus, potassium, and organic matter; Based on the soil data and the nutrient data, a rice field regional comprehensive assessment model for comprehensively assessing differences in nutrients and soil properties in rice fields is constructed; According to the rice field area comprehensive assessment model, the uniform area is classified according to the differences in nutrients and soil properties to obtain classified areas; Based on the classified areas, a paddy field nutrient demand analysis model is constructed, and the paddy field nutrient demand analysis model satisfies the following expression: ， in, For the The rice field area The nutrient requirements of For the A comprehensive evaluation index of nutrients and soil properties in paddy fields. For the The basic requirement coefficient of the nutrient, For the The adjustment coefficient of the nutrient, For the Recommended application rates of nutrients: For the A comprehensive evaluation index of nutrients and soil properties in paddy fields. is the total area of ​​rice fields, For the Rice field area Evaluation index of plant nutrients; According to the paddy field nutrient demand analysis model, a paddy field precision fertilization model is constructed, and the paddy field precision fertilization model satisfies the following expression: ， in, For the The rice field area The amount of fertilizer applied for each nutrient, For the The rice field area The nutrient requirements of For the In the soil of rice fields The existing amount of nutrients, For the Fertilizer utilization rate of plant nutrients, For the Fertilizer The nutrient content of For the Fertilizer in the The preference coefficient of the rice field area, For the A comprehensive evaluation index of nutrients and soil properties in paddy fields. For the A comprehensive evaluation index of nutrients and soil properties in paddy fields. For the Rice field area Evaluation index of plant nutrients; Through the rice field precision fertilization model, the specific fertilizer amount of the specific fertilizer corresponding to the specific nutrients in the specific area is determined, and the connection between the area, nutrients, fertilizer type and fertilizer amount is achieved. The connection process includes: Obtaining a fertilization priority ranking for a rice field area; determining the nutrient requirement in the classification area based on the fertilization priority ranking; determining a fertilizer type according to the nutrient requirement; determining a fertilizer amount according to the fertilizer type and the nutrient requirement; and performing graded fertilization according to the fertilizer amount in combination with the fertilization priority ranking.

2. A precision fertilization method based on real-time monitoring of paddy field nutrients according to claim 1, characterized in that: The method of dividing the paddy field into a plurality of uniform areas based on the map data and using a random sampling algorithm and an expansion algorithm includes: Based on the map data, a dynamic equilibrium area division model is constructed using a random sampling algorithm and an expansion algorithm; The paddy field is divided into a plurality of uniform areas by means of the dynamic equilibrium area division model.

3. A precision fertilization method based on real-time monitoring of paddy field nutrients according to claim 2, characterized in that: The dynamic balanced area division model satisfies the following expression: ， in, For the In the iteration The position of the coordinate point, For the In the iteration The position of the coordinate point, For the In the iteration The position of the coordinate point, is the iteration step factor, is the preset equilibrium area, For the In the iteration The area of ​​the region, for and The distance between is the adjustment range of the coordinate point position, is the total number of coordinate points extracted.

4. The method for precise fertilization based on real-time monitoring of nutrients in paddy fields according to claim 1, characterized in that: The step of constructing a rice field regional comprehensive assessment model for comprehensively assessing differences in nutrients and soil properties in rice fields based on the soil data and the nutrient data comprises: Based on the soil data and the nutrient data, constructing a paddy field regional nutrient assessment model for assessing nutrient differences in paddy fields; Based on the soil data and the nutrient data, constructing a paddy field regional soil property assessment model for assessing differences in paddy field regional soil properties; A paddy field region comprehensive assessment model for comprehensively assessing differences in paddy field region nutrients and soil properties is constructed by using the paddy field region nutrient assessment model and the paddy field region soil property assessment model.

5. A precision fertilization method based on real-time monitoring of paddy field nutrients according to claim 4, characterized in that: The paddy field regional nutrient assessment model satisfies the following expression: ， in, For the Nutrient assessment index for rice fields, For the Rice field area The standardized value of each nutrient index, For the Rice field area The mean value of nutrient indexes, For the Rice field area The standard deviation of each nutrient index, For the Rice field area The weight of each nutrient index; the paddy field area soil property evaluation model satisfies the following expression: ， in, For the Soil property evaluation index for paddy fields, For the Rice field area The measured values ​​of soil characteristic indicators, For the Rice field area The mean value of soil characteristic indexes, For the Rice field area The standard deviation of soil characteristic indexes, For the Rice field area The median of soil characteristic indicators, For the Rice field area The interquartile range of soil characteristic indicators, and For the Rice field area The adjustment parameters corresponding to each soil characteristic index are: For the Rice field area The weight parameter corresponding to the soil characteristic index; the comprehensive evaluation model of paddy field area satisfies the following expression: ， in, For the Comprehensive evaluation index of nutrients and soil properties in paddy fields, For the Nutrient assessment index for rice fields, For the Soil property evaluation index for paddy fields, , is the weight coefficient.

6. The method for precise fertilization based on real-time monitoring of nutrients in paddy fields according to claim 1, characterized in that: The step of classifying the uniform area according to the differences in nutrients and soil properties and obtaining the classified areas according to the comprehensive assessment model of the paddy field area comprises: According to the rice field regional comprehensive assessment model, setting the nutrient difference threshold of the comprehensive assessment index of the rice field regional nutrients and soil characteristics; Comparing the comprehensive evaluation index of nutrients and soil properties of the paddy field area with the nutrient difference threshold to determine nutrient-rich areas and nutrient-poor areas; Based on the nutrient-rich areas and the nutrient-poor areas, combined with the paddy field area soil property assessment model, the nutrient-rich areas with excellent soil properties, the nutrient-rich areas with poor soil properties, the nutrient-poor areas with excellent soil properties, and the nutrient-poor areas with poor soil properties are determined.

7. The method for precise fertilization based on real-time monitoring of paddy field nutrients according to claim 1, characterized in that: The step of constructing a paddy field nutrient demand analysis model based on the classified areas includes: Based on the classified areas, the fertilization priority ranking of the paddy field areas is determined, and the fertilization priority ranking is as follows: Areas with poor nutrients and poor soil characteristics > Areas with poor nutrients but good soil characteristics > Areas with rich nutrients but poor soil characteristics > Areas with rich nutrients and good soil characteristics; Based on the fertilization priority ranking, a rice field nutrient demand analysis model is constructed.

8. A precision fertilization system based on real-time monitoring of rice field nutrients, the system using a precision fertilization method based on real-time monitoring of rice field nutrients according to any one of claims 1 to 7, characterized in that: The system includes an input device, a processor, an output device and a memory, wherein the input device, the processor, the output device and the memory are connected to each other, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions.

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