Intelligent agricultural internet of things monitoring, fertilizer distribution and irrigation integrated automatic control method and system

Abstract

The present application relates to the field of intelligent agriculture, and more particularly to an intelligent agricultural Internet of Things monitoring, fertilizer distribution and irrigation integrated automatic control method and system, which monitors soil detection data in real time; combines the soil detection data and the planting variety data corresponding to the soil location to determine whether the soil detection data meets the irrigation condition; when the soil detection data meets the irrigation condition, based on the determination result, the soil detection data and the planting variety data are sent to a preset cloud model, the cloud model calculates fertilizer supplement data, and the calculation result is output; according to the fertilizer supplement data, irrigation parameters of the target irrigation location are generated; the irrigation parameters are sent to the irrigation end, and the fertilizer is distributed and the target irrigation location is irrigated through the irrigation end. Through the networking monitoring, the integrated intelligent control system of fertilizer distribution and irrigation optimizes the use of water and fertilizer, greatly reduces the data lag, and quickly adjusts the irrigation and fertilization strategy.

Description

Technical Field

[0001] This application relates to the field of smart agriculture technology, and in particular to a smart agriculture Internet of Things monitoring, fertilizer application and irrigation integrated automatic control method and system. Background Technology

[0002] Smart agriculture refers to an agricultural development model that uses modern information technology, the Internet of Things, big data, artificial intelligence, drones, sensors, and other technologies to improve agricultural production efficiency, precisely manage agricultural resources, reduce human intervention, and optimize agricultural decision-making. Smart agriculture is not merely a technological upgrade of traditional agricultural production methods, but also an intelligent upgrade of all aspects of agricultural ecology, production, management, and marketing.

[0003] Current technologies for soil condition monitoring and fertilizer formulation typically use sensors or soil analysis tools to monitor soil moisture, temperature, pH, nutrient content, and other indicators in real time. Based on this soil condition data, the system calculates the required fertilizer ratio, which the user sets according to soil conditions, plant needs, and irrigation volume. The fertilizer and water are then mixed and applied through the irrigation system. However, soil conditions can change over time, especially during irrigation. Soil moisture and nutrient distribution may change after irrigation, and the user-set fertilizer ratio often has a lag. This lag can lead to fertilizer ratios that do not accurately match the real-time needs of the soil, resulting in too much or too little fertilizer. Secondly, during irrigation, the amount of fertilizer prepared once is often insufficient to meet the needs of the entire area, especially when the irrigated area is large or irregular, and the fertilizer may not be evenly distributed. If fertilizer needs to be re-prepared, the concentration of the re-prepared fertilizer is often difficult to completely match the concentration of the irrigated area, especially in peripheral areas. This can lead to uneven distribution of soil fertilizer concentration, with areas of high concentration due to repeated irrigation and areas of low concentration due to insufficient irrigation, thus reducing crop growth.

[0004] Therefore, existing technologies have shortcomings and need to be improved. Summary of the Invention

[0005] In order to solve one or more problems in the prior art, the main objective of this application is to provide a smart agriculture Internet of Things monitoring, fertilizer application and irrigation integrated automatic control method and system.

[0006] To achieve the aforementioned objectives, this application proposes an integrated automatic control method for smart agriculture IoT monitoring, fertilization, and irrigation, the method comprising:

[0007] Real-time monitoring of soil testing data;

[0008] Based on the soil test data and the planting variety data corresponding to the soil location, determine whether the soil test data meets the irrigation conditions;

[0009] When the soil test data meets the irrigation conditions, based on the judgment result, the soil test data and planting variety data are sent to the preset cloud model, the cloud model calculates the fertilizer supplementation data, and outputs the calculation result;

[0010] Based on the fertilizer replenishment data, irrigation parameters for the target irrigation location are generated;

[0011] The irrigation parameters are sent to the irrigation terminal, which then allocates fertilizer and irrigates the target irrigation location.

[0012] This application also provides an integrated automatic control system for smart agriculture IoT monitoring, fertilization, and irrigation, including:

[0013] The monitoring module is used to monitor soil testing data in real time.

[0014] The judgment module is used to combine the soil test data and the planting variety data corresponding to the soil location to determine whether the soil test data meets the irrigation conditions;

[0015] The calculation module is used to send the soil test data and planting variety data to a preset cloud model based on the judgment result when the soil test data meets the irrigation conditions, calculate fertilizer supplementation data through the cloud model, and output the calculation result.

[0016] The generation module is used to generate irrigation parameters for the target irrigation location based on the fertilizer replenishment data.

[0017] The sending module is used to send the irrigation parameters to the irrigation terminal, through which fertilizer is allocated and irrigation is carried out at the target irrigation location.

[0018] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the methods described above.

[0019] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0020] The intelligent agricultural IoT monitoring, fertilization, and irrigation integrated automatic control method and system of this application tightly integrates irrigation and fertilization systems, automatically providing water and fertilizer to crops through intelligent control. It not only improves the precision of fertilization and irrigation but also allows for real-time adjustment of the water-fertilizer ratio to meet the optimal needs of crop growth. Through a networked monitoring, fertilization, and irrigation integrated intelligent control system, water and fertilizer usage is optimized, significantly reducing data lag. The use of real-time sensors and an intelligent control system reduces lag, enabling the system to respond to environmental changes in real time and quickly adjust irrigation and fertilization strategies. In traditional agricultural management, data lag often leads to untimely adjustments to irrigation and fertilization, affecting crop growth. By reducing lag, farmland management becomes more precise, effectively avoiding growth problems caused by water-fertilizer mismatch, and improving the stability and reliability of agricultural production. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating an embodiment of the smart agriculture IoT monitoring, fertilizer application, and irrigation integrated automatic control method according to this application.

[0022] Figure 2 This is a flowchart illustrating an embodiment of the smart agriculture IoT monitoring, fertilizer application, and irrigation integrated automatic control method according to this application.

[0023] Figure 3 This is a schematic block diagram of the structure of an integrated automatic control system for smart agriculture IoT monitoring, fertilizer application and irrigation according to an embodiment of this application;

[0024] Figure 4 This is a schematic block diagram of the structure of a computer device according to an embodiment of this application.

[0025] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0027] Reference Figure 1 This application provides a smart agriculture IoT monitoring, fertilizer application, and irrigation integrated automatic control method, the method comprising:

[0028] S1. Real-time monitoring of soil testing data;

[0029] S2. Based on the soil test data and the planting variety data corresponding to the soil location, determine whether the soil test data meets the irrigation conditions;

[0030] S3. When the soil test data meets the irrigation conditions, based on the judgment result, the soil test data and planting variety data are sent to the preset cloud model, the fertilizer supplementation data is calculated through the cloud model, and the calculation result is output.

[0031] S4. Based on the fertilizer replenishment data, generate irrigation parameters for the target irrigation location;

[0032] S5. Send the irrigation parameters to the irrigation terminal, and use the irrigation terminal to allocate fertilizer and irrigate the target irrigation location.

[0033] As described in steps S1-S3 above, soil condition data can be collected in real time using sensors or other soil testing equipment (such as sensors for humidity, temperature, pH, and nutrients). This data reflects the current state of the soil, providing basic information for subsequent decision-making. Real-time monitoring accurately captures the immediate needs of the soil, avoiding the delayed judgment of soil condition in traditional methods. Real-time data provides higher accuracy and reliability, reduces human intervention, and improves automation. The system combines soil testing data with crop variety data to determine whether irrigation conditions have been met. For example, soil moisture and temperature data are combined with crop water requirements (based on variety characteristics) to determine whether irrigation is necessary. By combining soil data and crop variety data, crop needs and soil conditions can be accurately matched, ensuring that irrigation is only carried out at the appropriate time, avoiding over-irrigation or under-irrigation. This effectively saves water resources and ensures optimal soil conditions for crop growth. Once it is determined that irrigation conditions are met, the system sends the soil testing data and crop variety data to a preset cloud computing model. Cloud-based models calculate the necessary fertilizer supplementation data, including fertilizer type, quantity, and ratio, based on big data analysis and algorithmic models (which may include machine learning). Through these calculations, fertilizer ratios and the fertilizer-to-water ratio can be determined, avoiding errors inherent in manual operation. The advantage of cloud computing models lies in their ability to handle more complex decision-making factors, such as the fertilizer requirements of different crops, climate change, and soil type, providing personalized fertilizer solutions.

[0034] As described in steps S4-S5 above, based on the fertilizer replenishment data output from the cloud, the system generates corresponding irrigation parameters according to soil and crop needs. These irrigation parameters include specific indicators such as fertilizer concentration, irrigation volume, and irrigation time. These parameters are customized according to the soil conditions and crop needs at different locations, ensuring precise ratios of fertilizer and water for each location. The effect of this step is to flexibly adjust irrigation and fertilizer supply based on real-time changes in soil conditions and crop needs, achieving refined management. The generated irrigation parameters maximize soil fertility and water use efficiency, increase crop yield, and reduce resource waste. Finally, the irrigation parameters are sent to the irrigation equipment. These devices, through sensors and automated systems, adjust the fertilizer ratio and irrigation volume according to the set parameters and accurately spray the mixed water and fertilizer to the designated irrigation locations. This process is typically achieved through an integrated intelligent irrigation system, including intelligent irrigation equipment, valves, pump stations, and other control systems. This stage ensures the precise execution of the irrigation process and controls the amount of fertilizer and water used within the optimal range, avoiding resource waste. Since irrigation parameters are calculated based on real-time monitoring data and crop requirements, the accuracy and effectiveness of fertilization and irrigation processes are greatly improved, which can significantly improve agricultural production efficiency and crop quality.

[0035] As described above, the irrigation and fertilization systems are tightly integrated, and intelligent control automates the supply of water and fertilizer to crops. This not only improves the precision of fertilization and irrigation but also allows for real-time adjustment of the water-fertilizer ratio to meet the optimal needs of crop growth. Through a networked monitoring, fertilizer distribution, and integrated irrigation intelligent control system, water and fertilizer use is optimized, significantly reducing data lag. The use of real-time sensors and an intelligent control system reduces lag, enabling the system to respond to environmental changes in real time and quickly adjust irrigation and fertilization strategies. In traditional agricultural management, data lag often leads to untimely adjustments to irrigation and fertilization, affecting crop growth. By reducing lag, farmland management becomes more precise, effectively avoiding growth problems caused by water-fertilizer mismatch, and improving the stability and reliability of agricultural production.

[0036] To reduce data lag, after the step of distributing fertilizer through the irrigation end and irrigating the target irrigation location, the method further includes:

[0037] Real-time acquisition of soil monitoring data after irrigation;

[0038] Based on the soil testing data after irrigation, the trend of soil testing data change is predicted to assess the irrigation effect and changes in soil condition.

[0039] Based on the prediction results, determine whether the irrigation parameters match the real-time needs of the soil;

[0040] If the irrigation parameters do not match the real-time needs of the soil, the irrigation parameters will be readjusted based on the judgment result.

[0041] Reference Figure 2 In one embodiment, the method for determining whether the soil testing data meets irrigation conditions by combining the soil testing data and the planting variety data corresponding to the soil location includes:

[0042] S21. Analyze the soil test data to obtain soil temperature, humidity, nitrogen, phosphorus, potassium and pH data;

[0043] S22. Obtain the planting variety data corresponding to the soil location;

[0044] S23. Based on the plant variety data, determine whether the soil test data meets the generation standards of the plant variety;

[0045] S24. If the soil test data meets the growth standards of the planted variety, then it is determined that the soil test data does not meet the irrigation conditions.

[0046] S25. When the soil test data does not meet the growth standards of the planted variety, it is determined that the soil test data meets the irrigation conditions.

[0047] As described in the steps above, soil testing data includes several key parameters such as temperature, humidity, nitrogen, phosphorus, potassium, and pH. These data are obtained through sensors or laboratory analysis. Each parameter has a significant impact on crop growth.

[0048] Temperature and humidity affect the evaporation and absorption of water by crops, thus affecting the crop's growth environment.

[0049] Nitrogen: A key nutrient element for plant growth, directly affecting the growth rate and quality of crops.

[0050] Phosphorus: Helps crops develop their root system and form flowers and fruits.

[0051] Potassium: Regulates the water and nutrient balance of plants and enhances the disease resistance of crops.

[0052] pH value: Soil acidity and alkalinity have a significant impact on plant nutrient absorption; excessively acidic or alkaline soils may affect crop growth. Analyzing this data provides a comprehensive understanding of soil health, determining its suitability for specific crop varieties and the need for irrigation or other soil improvement measures. Each soil location may have different geographical characteristics (such as soil type, climate, and topography), and each crop has different growth requirements under different conditions. Therefore, the system matches soil data with suitable crop varieties by acquiring the corresponding crop variety data for the soil location. By locating the data, it ensures that each soil plot corresponds to the most suitable crop variety, thereby providing optimal nutrient and water conditions for crop growth, resulting in improved yield and quality. The criteria for crop variety selection typically include specific soil condition ranges (such as suitable temperature and humidity, nitrogen, phosphorus, and potassium ratios, and pH value), which are established based on the crop's growth needs and the soil's nutrient requirements. By comparing soil testing data with these criteria, the system determines whether the soil is suitable for the variety's growth. This criterion-based selection function helps the system automatically screen suitable crop varieties and ensures that the soil environment matches the crop's needs. If soil conditions do not meet standards, it indicates that crops may not grow healthily in that soil environment, potentially leading to reduced yield or quality. When soil testing data shows that various soil indicators (such as moisture and nutrients) meet the crop's growth requirements, it means the soil is in good condition and no longer needs excessive irrigation. At this point, the system determines that irrigation conditions are not met, meaning the soil does not require additional water, thus avoiding water waste or soil salinization caused by over-irrigation. Reducing unnecessary irrigation not only saves water resources but also avoids the adverse effects of over-irrigation on soil and crops, optimizing agricultural water management and improving water resource utilization efficiency. When soil data fails to meet the growth standards for a specific crop variety, it indicates that certain soil conditions (such as moisture, nitrogen, phosphorus, and potassium content, and pH) are insufficient to support normal crop growth. In this case, the system determines that irrigation is needed to supplement water or adjust soil conditions. Irrigation not only provides crops with the necessary water but can also, in some cases, adjust soil nutrient ratios through integrated water and fertilizer irrigation systems. By promptly determining the need for irrigation, we can ensure that crops receive sufficient water and suitable soil conditions, improve the crop growth environment, reduce growth problems caused by insufficient water or poor soil, and thus improve the stability of agricultural production and crop yield.

[0053] In one embodiment, after the steps of sending the irrigation parameters to the irrigation terminal, dispensing fertilizer through the irrigation terminal, and irrigating the target irrigation location, the method further includes:

[0054] Real-time acquisition of irrigation status data;

[0055] Based on the irrigation status data, analyze whether the fertilizer to be irrigated at the irrigation end is sufficient for the target irrigation location;

[0056] If the fertilizer to be irrigated at the irrigation end is insufficient for the target irrigation location, supplementary parameters are generated based on the analysis results.

[0057] As mentioned above, during irrigation, sensors, remote sensing technology, or IoT devices collect real-time irrigation-related data such as soil moisture, soil temperature, crop growth status, and fertilizer concentration. This data includes, but is not limited to, soil moisture conditions, temperature, fertilizer distribution, and flow rate, providing a comprehensive understanding of the actual irrigation effect. Real-time data acquisition ensures dynamic adjustments during irrigation, preventing ineffective irrigation due to environmental changes, crop needs, or equipment malfunctions. Real-time data provides immediate feedback during irrigation, ensuring crops are always in a suitable growth environment. Real-time monitoring ensures precise water and fertilizer application, avoiding over- or under-irrigation. Timely data feedback allows the irrigation system to react quickly, adjusting water and fertilizer supply to promote healthy crop growth. The collected irrigation data is analyzed by the system, particularly calculating factors such as soil fertilizer concentration, crop fertilizer requirements, and soil fertilizer trends. The system compares this data with the available fertilizer quantity at the irrigation point to determine if it meets the needs of the target irrigation location. This step requires a comprehensive analysis based on specific crop planting requirements, such as the crop's fertilizer needs, the soil's fertilizer absorption capacity, and the remaining fertilizer levels in the soil and crops. By precisely analyzing the match between soil fertilizer supply and crop needs, it ensures that fertilizer is neither excessive nor insufficient, avoiding resource waste or negative impacts on crop growth. It enables precise control of fertilizer input, dynamically adjusting according to demand to improve fertilizer efficiency and achieve better crop growth. This avoids economic losses due to insufficient fertilizer supply or waste, improving the economic benefits of agricultural production. When the system detects that the current fertilizer supply is insufficient to meet the needs of the target irrigation location, it automatically generates supplementary parameters. These parameters include the possible types, quantities, mixing ratios, and timing of supplementary fertilizer. After generating these parameters, the irrigation system automatically adjusts the fertilizer supplementation amount and delivers fertilizer to fertilizer-deficient areas according to the soil and crop needs. This process typically considers the soil's fertilizer retention capacity and the crop's fertilizer absorption rate; the system dynamically adjusts the supplementation strategy based on real-time data. When fertilizer deficiency is detected, the system can autonomously generate supplementation parameters and execute supplementation, reducing the need for manual intervention and improving the automation level of agricultural production. By generating supplementation parameters, the system can accurately calculate the type and quantity of fertilizer lacking, avoiding excessive or insufficient fertilizer supplementation. Supplementation parameter generation is not a one-time process, but a dynamic adjustment process. The system continuously analyzes whether fertilizer supplementation is needed based on real-time changes in irrigation data. If soil conditions, crop requirements, or irrigation effects change, the system will adjust the supplementation parameter generation strategy in real time to more accurately meet current irrigation needs. This mechanism enables irrigation and fertilizer supplementation to adapt to different weather conditions, soil conditions, and crop growth stages, achieving flexible management.Dynamically adjusting and supplementing parameters ensures that fertilizer supply better meets real-time needs, avoiding resource waste or poor crop growth caused by unreasonable static parameter settings. As crop requirements for water and fertilizer change during growth, dynamically adjusting supplementing parameters helps crops achieve optimal growth conditions at each stage.

[0058] In one embodiment, the method for generating supplementary parameters based on the analysis results includes:

[0059] Obtain the irrigation status data, and based on the irrigation status data, confirm the specific area that has been irrigated and the corresponding soil change data;

[0060] Based on the specific areas that have already been irrigated, determine the unirrigated areas;

[0061] Based on the specific irrigated areas and corresponding soil change data, calculate the fertilizer requirements and fertilizer application concentration parameters for the unirrigated areas;

[0062] Supplementary parameters are generated based on the fertilizer demand and fertilizer application concentration parameters of the unirrigated area;

[0063] The supplementary parameters are sent to the irrigation terminal, which then allocates fertilizer and provides supplementary irrigation to the unirrigated areas.

[0064] As mentioned above, real-time monitoring technologies, such as sensors, drones, and remote sensing, collect irrigation status data. This data includes soil moisture, soil temperature, and fertilizer concentration. Based on this data, the system determines which areas have been irrigated and records changes in soil conditions in each area (e.g., changes in moisture, temperature, and fertilizer distribution). This step aims to accurately determine which areas have been irrigated and their soil conditions, providing a basis for subsequent supplementary strategies. It ensures the system knows which areas have received water and fertilizer, avoiding duplicate irrigation of the same areas. Data such as soil moisture and fertilizer concentration provide necessary information for subsequent irrigation and fertilizer allocation, ensuring that subsequent supplementary measures are based on sound principles. This identification process involves spatial data analysis, such as comparing irrigated areas with the geographical information of the entire farmland to determine which areas have not received sufficient irrigation. Identifying unirrigated areas allows for precise replenishment of water and fertilizer to these areas, ensuring balanced irrigation for every area. By accurately identifying unirrigated areas, missed irrigation is avoided, ensuring that every part of the farmland receives sufficient water and fertilizer. By avoiding duplicate irrigation of already irrigated areas, water and fertilizer resources are conserved. Each area receives adequate care, thereby optimizing the overall crop growth environment and promoting efficient planting. By analyzing data such as soil moisture and fertilizer concentration changes in irrigated areas, the system can infer whether the fertilizer in irrigated areas is suitable and calculate the fertilizer requirements for unirrigated areas. Calculating fertilizer requirements requires considering factors such as soil fertilizer reserves, soil type, and crop fertilizer absorption patterns. Simultaneously, the system also needs to calculate fertilizer application concentration parameters to ensure that the amount of fertilizer applied matches the crop's needs, avoiding fertilizer waste and ensuring the crop receives sufficient nutrients. The calculated fertilizer requirements and application concentrations ensure precise application, avoiding over- or under-application and preventing soil and crop problems. By customizing fertilizer application concentrations for different areas based on soil needs, the system ensures that the needs of different soil types and crops are reasonably met. Reasonable calculation of fertilizer requirements and concentrations not only improves fertilizer use efficiency but also reduces soil pollution caused by over-fertilization. After calculating the fertilizer requirements and application concentrations for unirrigated areas, the system generates supplementary parameters. These supplementary parameters specifically include:

[0065] The types of fertilizers that need to be supplemented (e.g., nitrogen fertilizer, phosphorus fertilizer, etc.);

[0066] Fertilizer application amount for each area (based on the calculated requirement);

[0067] The timing and method of fertilization (e.g., fertilization via drip irrigation or sprinkler irrigation).

[0068] The generation of supplementary parameters is dynamic; the system updates in real time based on irrigation data and soil changes to ensure it adapts to the current agricultural environment and crop needs. By automatically generating supplementary parameters, the irrigation system can precisely allocate fertilizer according to real-time demand, thereby improving the intelligence level of agricultural production. The generated supplementary parameters ensure that the type, quantity, concentration, and application method of fertilizer meet the specific needs of the area, avoiding unnecessary waste. Automatically generating supplementary parameters reduces errors and labor intensity from manual judgment, improving the efficiency and accuracy of the entire irrigation management process. Once generated, the supplementary parameters are sent to the irrigation system (e.g., intelligent irrigation equipment or management platform), which then allocates fertilizer and performs irrigation operations based on these parameters. The irrigation equipment adjusts the water-fertilizer mixing ratio according to these parameters and replenishes water and fertilizer to unirrigated areas using appropriate irrigation methods (e.g., drip irrigation, sprinkler irrigation). In practice, the irrigation system also needs to connect to fertilizer supply equipment to ensure that fertilizer is supplied precisely according to the requirements of the supplementary parameters.

[0069] In one embodiment, prior to the step of dispensing fertilizer through the irrigation end and supplementing irrigation to unirrigated areas, the method further includes:

[0070] Identify the edge locations of the irrigated and unirrigated areas;

[0071] Based on the edge location, identify the current soil permeability parameters at the edge location;

[0072] Based on the soil infiltration parameters, calculate supplementary parameters for the edge positions of the irrigated and unirrigated areas;

[0073] Based on the supplementary parameters of the edge position, an edge irrigation strategy is generated, and the supplementary parameters of the edge position and the edge irrigation strategy are sent to the irrigation end.

[0074] As mentioned above, during irrigation, the edge zone is a transitional area between irrigated and unirrigated areas. The soil in this location typically has several characteristics: because it is in contact with both irrigated and unirrigated areas, its water requirements may differ from other areas. If irrigation continues in this edge zone, water may flow from the irrigated area into the unirrigated area, leading to oversaturation in the edge zone while the unirrigated area remains unreplenished. This wastes water resources and can cause excessive soil moisture, negatively impacting crop growth. If the edge zone is not precisely controlled, water may flow into the irrigated area, causing overwatering and further wasting water and adversely affecting crop growth. Because the permeability and moisture conditions of the edge zone may differ from its neighboring soils, implementing the overall irrigation strategy may result in excessive water in the edge zone and insufficient water in the unirrigated area, affecting soil moisture uniformity. Supplementation parameters refer to the calculated supplementary water volume or irrigation intensity within the irrigated edge zone, considering insufficient soil permeability or uneven water distribution. This step, by comprehensively considering soil infiltration parameters, current humidity levels, and meteorological data (such as evaporation and precipitation), uses a water balance model or other agricultural irrigation algorithms to calculate the parameter values ​​requiring additional irrigation. The calculation of supplementary parameters ensures that marginal areas receive sufficient water replenishment, avoiding over- or under-irrigation. In this way, the system can finely adjust the irrigation amount according to the actual needs of the soil, achieving optimal water and soil balance. Based on the calculated supplementary parameters, the system generates specific irrigation strategies. These strategies include irrigation intensity, time intervals, and the distribution of irrigated areas. The process of generating irrigation strategies typically involves optimization algorithms, such as predictions based on simulation or machine learning models, to ensure optimal water distribution under specific environmental and soil conditions. This strategy makes the irrigation process more personalized and efficient. By rationally controlling the irrigation amount and timing, crop water use efficiency can be maximized while avoiding waste or under-irrigation. Marginal irrigation strategies ensure a more natural and uniform transition between the boundary area and the irrigated area.

[0075] In one embodiment, the method for generating an edge irrigation strategy based on supplementary parameters of the edge position includes:

[0076] The edge distance is determined based on the edge positions of the irrigated and unirrigated areas;

[0077] Based on the current soil permeability parameters and edge distance at the edge location, the edge location is divided into multiple small areas;

[0078] Based on the segmentation results, the supplementary parameters at the edge positions are set in segments according to the irrigation gradient method.

[0079] As mentioned above, the boundary distance refers to the width of the transition zone established between irrigated and unirrigated areas. Depending on the irrigation system design, this boundary zone may vary with topography, soil structure, and irrigation methods. The boundary position between irrigated and unirrigated areas can be measured in real time using sensors (such as soil moisture sensors and temperature sensors). The determination of the boundary distance depends on factors such as changes in soil moisture and the coverage area of ​​the irrigation system. Determining the boundary distance helps to accurately delineate the irrigation boundary, allowing the irrigation system to manage water differently according to the actual needs of different areas. This step lays the foundation for subsequent irrigation strategy development. Soil permeability refers to the soil's ability to absorb and drain water, determining the speed at which water moves through the soil. At the boundary, soil permeability may differ between irrigated and unirrigated areas, leading to uneven water distribution. Based on the boundary distance and soil permeability parameters, the system divides the boundary area into multiple smaller zones. The size of these smaller zones depends on variations in soil permeability and boundary distance. For areas with poor permeability, the divided zones may be smaller to allow for finer control of water distribution; for areas with good permeability, the divided zones can be appropriately larger. By segmenting edge zones, irrigation strategies can be optimized based on the soil permeability of each sub-zone. Different soil conditions may require different water supplies, so this segmentation allows for more precise control of water infiltration and flow, thereby improving irrigation efficiency and preventing over- or under-watering. Irrigation gradients refer to setting different irrigation intensities in different areas. Due to their unique characteristics, edge zones may require adjustments to irrigation intensity based on distance and soil permeability to achieve precise control. Based on the characteristics of each sub-zone, the irrigation system segments the supplementary water volume, adjusting the irrigation amount according to the water demand of each segmented zone. For example, the edge near an irrigated area may require less water, while the edge near an unirrigated area may require more. By segmenting the supplementary parameters, the water demand of each sub-zone can be adequately met while avoiding water waste. Especially for edge zones, a gradually transitioning irrigation gradient helps reduce water loss and uneven distribution. This ensures maximum irrigation efficiency while protecting the soil and crop growing environment. Irrigation gradients in different zones can effectively avoid water waste caused by over-irrigation or soil drought caused by under-irrigation.

[0080] In one embodiment, the irrigation end includes a fertilizer storage mechanism, a fertilizer mixing mechanism, and an irrigation valve. The fertilizer storage mechanism is primarily used to store liquid or solid fertilizers, ensuring that fertilizer is supplied as needed during irrigation. The fertilizer storage container is typically a sealed container to maintain fertilizer stability and prevent external contamination. The capacity of the storage container should be designed according to the scale of the irrigation system. The fertilizer storage container can be a large-capacity container or multiple small storage boxes for dispensing different types of fertilizers. Common fertilizers include liquid fertilizers and soluble solid fertilizers. The fertilizer storage mechanism ensures a continuous supply of fertilizer, preventing disruptions to the normal operation of the irrigation system due to fertilizer supply interruptions. The storage container is typically designed for easy monitoring and replenishment of fertilizer, ensuring that fertilizer shortages do not occur during long-term system operation. The main function of the fertilizer mixing mechanism is to properly mix the fertilizer in the storage container with irrigation water. The ratio of fertilizer to water can be precisely controlled according to the needs of different crops, ensuring that the fertilizer is evenly distributed to the soil during each irrigation. Fertilizer can be injected via pump or gravity. The fertilizer and water are evenly mixed using stirring and rotation methods to ensure uniform fertilizer and water concentration. The injection ratio of water and fertilizer is controlled and adjusted according to a preset ratio. The mixing mechanism can also be linked with sensors to dynamically adjust the amount of fertilizer added by monitoring soil moisture or fertilizer concentration in real time. Through the mixing mechanism, fertilizer can be mixed into the irrigation water flow as needed, achieving precise fertilizer application. The mixing mechanism is usually linked with sensors and control systems to automatically adjust the fertilizer-to-water ratio, ensuring that irrigation and fertilization are carried out simultaneously, improving crop growth efficiency. The irrigation valve is mainly used to control the opening and closing of the water flow, ensuring that the irrigation system operates as needed by precisely controlling the water flow time and flow rate. The irrigation valve can be linked with the fertilizer mixing device, so that when the irrigation water flow is turned on, fertilizer is also injected into the water flow simultaneously for irrigation.

[0081] Reference Figure 3 This application also provides an integrated automatic control system for smart agriculture IoT monitoring, fertilizer application, and irrigation, including:

[0082] Monitoring module 1 is used to monitor soil test data in real time;

[0083] The judgment module 2 is used to combine the soil test data and the planting variety data corresponding to the soil location to determine whether the soil test data meets the irrigation conditions;

[0084] The calculation module 3 is used to send the soil test data and planting variety data to a preset cloud model based on the judgment result when the soil test data meets the irrigation conditions, calculate fertilizer supplementation data through the cloud model, and output the calculation result.

[0085] Generation module 4 is used to generate irrigation parameters for the target irrigation location based on the fertilizer replenishment data;

[0086] The sending module 5 is used to send the irrigation parameters to the irrigation terminal, and to allocate fertilizer and irrigate the target irrigation location through the irrigation terminal.

[0087] As described above, it is understood that each component of the integrated automatic control system for smart agriculture IoT monitoring, fertilization and irrigation proposed in this application can realize the function of any of the integrated automatic control methods for smart agriculture IoT monitoring, fertilization and irrigation as described above, and the specific structure will not be repeated.

[0088] Reference Figure 4 This application also provides a computer device, which may be a server, and its internal structure may be as follows: Figure 4 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores monitoring data and other data. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a smart agriculture IoT-based integrated automatic control method for monitoring, fertilization, and irrigation.

[0089] The processor described above executes the integrated automatic control method for smart agriculture IoT monitoring, fertilizer application, and irrigation, including: real-time monitoring of soil testing data; combining the soil testing data and the planting variety data corresponding to the soil location to determine whether the soil testing data meets the irrigation conditions; when the soil testing data meets the irrigation conditions, based on the determination result, sending the soil testing data and planting variety data to a preset cloud model, calculating fertilizer replenishment data through the cloud model, and outputting the calculation result; generating irrigation parameters for the target irrigation location based on the fertilizer replenishment data; sending the irrigation parameters to the irrigation terminal, and distributing fertilizer and irrigating the target irrigation location through the irrigation terminal.

[0090] One embodiment of this application also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements an integrated automatic control method for smart agriculture IoT monitoring, fertilizer application, and irrigation, including the following steps: real-time monitoring of soil testing data; combining the soil testing data and planting variety data corresponding to the soil location to determine whether the soil testing data meets irrigation conditions; when the soil testing data meets irrigation conditions, based on the determination result, sending the soil testing data and planting variety data to a preset cloud model, calculating fertilizer replenishment data through the cloud model, and outputting the calculation result; generating irrigation parameters for the target irrigation location based on the fertilizer replenishment data; sending the irrigation parameters to the irrigation terminal, and distributing fertilizer and irrigating the target irrigation location through the irrigation terminal.

[0091] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media provided in this application and in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0092] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0093] The above description is only a preferred embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural changes made based on the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A smart agriculture IoT-integrated automatic control method for monitoring, fertilization, and irrigation, characterized in that, The method includes: Real-time monitoring of soil testing data; Based on the soil test data and the planting variety data corresponding to the soil location, determine whether the soil test data meets the irrigation conditions; When the soil test data meets the irrigation conditions, based on the judgment result, the soil test data and planting variety data are sent to the preset cloud model, the cloud model calculates the fertilizer supplementation data, and outputs the calculation result; Based on the fertilizer replenishment data, irrigation parameters for the target irrigation location are generated; The irrigation parameters are sent to the irrigation terminal, which then allocates fertilizer and irrigates the target irrigation location. The method for determining whether the soil testing data meets irrigation conditions by combining the soil testing data and the planting variety data corresponding to the soil location includes: The soil test data were analyzed to obtain data on soil temperature, humidity, nitrogen, phosphorus, potassium, and pH. Obtain the planting variety data corresponding to the soil location; Based on the planted variety data, determine whether the soil test data meets the generation standards of the planted variety; If the soil test data meets the growth criteria for the planted variety, then the soil test data is determined not to meet the irrigation conditions. If the soil test data does not meet the growth standards of the planted variety, then the soil test data is determined to meet the irrigation conditions. After the step of sending the irrigation parameters to the irrigation terminal, and then using the irrigation terminal to allocate fertilizer and irrigate the target irrigation location, the method further includes: Real-time acquisition of irrigation status data; Based on the irrigation status data, analyze whether the fertilizer to be irrigated at the irrigation end is sufficient for the target irrigation location; If the fertilizer to be irrigated at the irrigation end is insufficient for the target irrigation location, supplementary parameters are generated based on the analysis results. The method for generating supplementary parameters based on the analysis results includes: Obtain the irrigation status data, and based on the irrigation status data, confirm the specific area that has been irrigated and the corresponding soil change data; Based on the specific areas that have already been irrigated, determine the unirrigated areas; Based on the specific irrigated areas and corresponding soil change data, calculate the fertilizer requirements and fertilizer application concentration parameters for the unirrigated areas; Supplementary parameters are generated based on the fertilizer demand and fertilizer application concentration parameters of the unirrigated area; The supplementary parameters are sent to the irrigation terminal, which then allocates fertilizer and provides supplementary irrigation to the unirrigated areas. Before the step of dispensing fertilizer through the irrigation end and supplementing irrigation to unirrigated areas, the method further includes: Identify the specific irrigated areas and the edges of the unirrigated areas; Based on the edge location, identify the current soil permeability parameters at the edge location; Based on the soil infiltration parameters, supplementary parameters are calculated for the edge positions of the irrigated and unirrigated areas. Based on the supplementary parameters of the edge position, an edge irrigation strategy is generated, and the supplementary parameters of the edge position and the edge irrigation strategy are sent to the irrigation end. Based on supplementary parameters of the edge position, an edge irrigation strategy is generated, the method comprising: The edge distance is determined based on the edge positions of the irrigated and unirrigated areas. Based on the current soil permeability parameters and edge distance at the edge location, the edge location is divided into multiple small areas; Based on the segmentation results, the supplementary parameters at the edge positions are set in segments according to the irrigation gradient method.

2. The integrated automatic control method for smart agriculture IoT monitoring, fertilizer application, and irrigation according to claim 1, characterized in that, The irrigation end includes a fertilizer storage mechanism, a fertilizer mixing mechanism, and an irrigation valve.

3. A smart agricultural IoT-integrated automatic control system for monitoring, fertilization, and irrigation, characterized in that: To implement the integrated automatic control method for smart agriculture IoT monitoring, fertilization, and irrigation as described in claim 1, the method includes: The monitoring module is used to monitor soil testing data in real time. The judgment module is used to combine the soil test data and the planting variety data corresponding to the soil location to determine whether the soil test data meets the irrigation conditions; The calculation module is used to send the soil test data and planting variety data to a preset cloud model based on the judgment result when the soil test data meets the irrigation conditions, calculate fertilizer supplementation data through the cloud model, and output the calculation result. The generation module is used to generate irrigation parameters for the target irrigation location based on the fertilizer replenishment data. The sending module is used to send the irrigation parameters to the irrigation terminal, through which fertilizer is allocated and irrigation is carried out at the target irrigation location.

4. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 2.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 2.

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