Intelligent water and fertilizer integrated irrigation system based on Internet of Things sensor and large language model

Abstract

The invention discloses an intelligent water and fertilizer integrated irrigation system based on an Internet of Things sensor and a large language model, and relates to the technical field of intelligent agriculture. The system obtains soil, weather and plant growth state data in real time through Internet of Things sensors, including a soil sensor, a weather sensor and a multispectral sensor, deployed in a farmland; the method comprises the following steps: based on a large language model architecture, integrating preset agricultural field knowledge base data, performing field adaptive fine tuning on model parameters by adopting a data migration technology, and constructing an intelligent irrigation control model with a dynamic decision-making function; the control model analyzes sensor data and crop growth requirements, generates a water and fertilizer supply strategy, calls an internet-of-things control interface of the water and fertilizer all-in-one machine, and automatically adjusts irrigation water quantity and proportional supply of nutrient elements such as nitrogen, phosphorus and potassium. According to the invention, Internet of Things perception and large model decision are fused, and different environments are adapted through fine adjustment, so that closed-loop precise control is realized, and the water and fertilizer utilization rate and the crop yield are remarkably improved. Actual measurement shows that the system can improve the water and fertilizer utilization rate by 30% or above, and the crop yield is increased by 15-20%.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent agricultural precision irrigation technology, and provides an intelligent water and fertilizer integrated irrigation system based on an Internet of Things sensor and a large language model. BACKGROUND

[0002] Achieving efficient and precise agricultural production on limited land resources has become the core issue of current agricultural development. Traditional agricultural irrigation often relies on experience to judge water and fertilizer management, which lacks scientificity and precision, and is prone to over-irrigation or insufficient irrigation, which not only wastes water resources, but also may affect the normal growth of crops. Traditional fertilization methods cannot accurately match the soil nutrient status and crop demand, resulting in waste of fertilizer or imbalance of soil nutrients. In addition, manual monitoring and management require a large amount of labor cost and cannot respond to the influence of environmental changes on crop growth in a timely manner. As an important development direction of modern agriculture, intelligent agriculture provides a new idea and method for solving this problem by integrating advanced technologies such as the Internet of Things, artificial intelligence, and big data.

[0003] Although by deploying various Internet of Things sensor devices, soil humidity, temperature, pH, nutrient content, and other key parameters, as well as air temperature and humidity, light intensity, wind speed, and other weather conditions can be monitored in real time, which provides important data support for precision agriculture. However, most existing intelligent irrigation systems still use rule-based control strategies, which can achieve a certain degree of automation, but lack intelligent decision-making capabilities and are difficult to adapt to complex and changing agricultural environments and crop growth needs. Large language models can process multi-modal and multi-dimensional agricultural data and make intelligent decisions in combination with professional knowledge, providing a technical foundation for building an intelligent agricultural decision-making system. The current market intelligent irrigation products mainly include time control-based timing irrigation systems and simple feedback control systems based on soil humidity sensors. They usually only consider a single or a few environmental parameters and lack comprehensive consideration of the whole process of crop growth; on the other hand, the decision-making mechanism is relatively simple and cannot fully utilize professional knowledge and historical data in the agricultural field, making it difficult to achieve true precision irrigation.

[0004] Therefore, based on multi-source Internet of Things sensors and large language models, in combination with real-time environmental conditions, optimal water and fertilizer matching strategies are developed according to the needs of different growth stages of crops, and dynamic optimization and adaptive adjustment of water and fertilizer supply are realized, to build an intelligent water and fertilizer integrated irrigation system with large model intelligent decision-making and precise control execution, which helps to achieve precision agriculture. SUMMARY

[0005] To solve the above technical problems, the present application provides an intelligent water and fertilizer integrated irrigation system based on an Internet of Things sensor and a large language model, the system architecture of which is shown in Figure 2This is to achieve precise irrigation of crops by integrating water and fertilizer.

[0006] According to one aspect of the present invention, an intelligent water and fertilizer integrated irrigation system based on Internet of Things sensors and large language models is provided, comprising the following steps:

[0007] S1: Based on multi-source high-precision IoT sensors deployed in farmland, real-time data on soil parameters, meteorological parameters, and plant growth status are acquired.

[0008] S2: Construct a professional knowledge base for the agricultural field based on crop growth data, crop environmental adaptability, irrigation and fertilization standards, and scientific research results in the field;

[0009] S3: Based on a professional database in the agricultural field, data migration technology is used to adaptively fine-tune the large language model to build a professional knowledge decision-making irrigation model to meet the water and fertilizer supply needs;

[0010] S4: Real-time monitoring and transmission of multi-source IoT sensor data, combined with crop growth stages and environmental conditions, large-scale model makes precise decisions and generates dynamic water and fertilizer replenishment strategies.

[0011] S5: Based on the water and fertilizer supply strategy generated by the large model, the water and fertilizer supply parameters are analyzed, and the water and fertilizer irrigation controller is used to adjust the water and fertilizer integrated machine in real time to achieve precise closed-loop control of irrigation duration, flow rate and fertilizer ratio.

[0012] S6: The system application integrates multi-dimensional data dashboards, anomaly warnings, remote control consoles, manual intervention permissions, strategy optimization suggestions, and automatic report generation functions to achieve intelligent management and data visualization throughout the entire process.

[0013] Furthermore, in step S1, the deployment of multi-source IoT sensors and real-time data monitoring includes:

[0014] S1.1: Deploy soil testing instruments to monitor soil conditions in the irrigation area, including soil temperature, soil moisture, soil EC, nitrogen, phosphorus, potassium, pH value and other soil moisture data;

[0015] S1.2: Deploy small meteorological stations to monitor meteorological conditions in the irrigation area, including: rainfall, wind speed, temperature, air humidity, solar radiation (irradiance), air pressure, PM2.5 and other environmental data;

[0016] S1.3: Deploy multispectral drones to acquire crop growth status data such as vegetation index, chlorophyll content, and water stress index;

[0017] S1.4: Adopts LoRa / NB-IoT hybrid networking, realizes hierarchical data transmission (real-time soil moisture / meteorological aggregation) through edge gateway, and deploys lightweight filtering algorithm preprocessing to ensure efficient and reliable transmission.

[0018] Furthermore, in step S2, the construction of the multi-agricultural domain knowledge base includes:

[0019] S2.1: Construct a crop growth knowledge base, collect and organize the life cycle classification standards, water and nutrient requirements for each growth stage, optimal environmental parameter ranges and yield and quality evaluation indicators for different crop varieties.

[0020] S2.2: Construct an environmental adaptability knowledge base and establish a mapping relationship between soil type, climate conditions and crop suitability, including the water and fertilizer retention characteristics of different soil types, the influence of meteorological factors on crop growth and extreme weather response strategies.

[0021] S2.3: Construct an irrigation and fertilization standard database, integrating standardized irrigation systems, fertilizer application technical procedures, water-fertilizer coupling effect parameters, and water-saving and efficiency-enhancing technical specifications issued by the agricultural sector;

[0022] S2.4: Construct a database of scientific research results, integrating the latest research results from agricultural research institutes, including new irrigation technologies, precision fertilization algorithms, crop model parameters, and field test data;

[0023] S2.5: Establish a dynamic update mechanism for the knowledge base, and connect to the agricultural data platform through the API interface to realize real-time synchronization and version management of knowledge content.

[0024] Furthermore, in step S3, the construction of the professional knowledge decision irrigation model based on the large language model includes:

[0025] S3.1: Employ domain-adaptive pre-training technology to convert agricultural knowledge base data into a structured input format that the model can understand, including sensor data vectorization, crop feature encoding, and environmental parameter standardization;

[0026] S3.2: Based on the LoRA (Low-Rank Adaptation) fine-tuning strategy, while freezing the main model parameters of the large language model, only the adapter parameters of specific layers are trained for the agricultural domain to maintain the model's general reasoning ability, as shown below:

[0027] h = W0x + ΔWx

[0028] Where W0 represents the original weights of the model, and ΔW represents the weights of the low-rank matrix involved in fine-tuning. The influence weights of the LoRA layer are adjusted by setting the adapter parameter α, and the objective function is to minimize the loss between the predicted irrigation strategy and the expert annotation strategy.

[0029] S3.3: Utilizing the Function Calling capabilities of a large language, a smart agent system is built to enable automated interaction and invocation between large models and IoT sensor interfaces. Through structured function descriptions and parameter mapping techniques, the large model can understand and proactively call external API interfaces, achieving a seamless transition from natural language decision-making to specific device control, including:

[0030] The system adopts a standardized Function Calling protocol, encapsulating sensor API interfaces into callable function objects. Each function contains a complete definition of its name, description, parameter types, and return value format. The large model automatically identifies the required sensor interface types by parsing the current irrigation decision requirements and generates corresponding function call instructions.

[0031] The intelligent agent employs a cyclical decision-making framework of perception-reasoning-action. In the perception phase, a function calling mechanism invokes sensor data acquisition interfaces to collect real-time information such as soil moisture, weather conditions, and crop growth status. In the reasoning phase, based on the collected multi-dimensional data and combined with an agricultural knowledge base, comprehensive analysis and reasoning are performed to generate the optimal irrigation strategy. In the action phase, the decision results are converted into specific equipment control commands, and the function calling invokes the control interface of the integrated water and fertilizer machine to achieve automated irrigation execution.

[0032] S3.4: Integrate reinforcement learning mechanisms to construct an interactive learning framework between the intelligent decision-making agent and the farmland environment. With crop yield, water and fertilizer utilization rate, and energy consumption control as the core optimization objectives, design a multi-dimensional reward function and continuously optimize irrigation decision strategies through continuous interaction with the environment to achieve adaptive evolution and performance improvement of the model.

[0033] The system constructs a comprehensive reward function R(t), which integrates three core dimensions: crop yield efficiency, resource utilization efficiency, and energy consumption control, and is expressed as:

[0034] R(t) = α·R yield (t)+β·R e”iciency (t)+γ·R energy (t)

[0035] The reward functions for each component are defined as follows:

[0036] Crop yield reward function:

[0037]

[0038] Water and fertilizer utilization reward function:

[0039]

[0040] Energy consumption control reward function:

[0041]

[0042] Among them, Y predicted To predict output, Y baseline As the baseline output, Y optimal Theoretical optimal yield; WUE and FUE represent water use efficiency and fertilizer use efficiency, respectively; E consumed E represents actual energy consumption. budget α represents the budgeted energy consumption; α, β, and γ are weighting coefficients.

[0043] S3.5: Establish an interpretable output interface for the model to generate explanations of the decision-making basis and confidence assessments, ensuring the traceability and credibility of irrigation decisions.

[0044] Furthermore, in step S4, the multi-source data is input into the professional knowledge decision-making irrigation model, which outputs decision parameters such as irrigation amount and fertilizer ratio, as well as interpretable decision results, including:

[0045] S4.1: Perform time-series analysis on the data recorded by the sensors to obtain soil, meteorological and crop growth status parameters; use an encoding model to fuse multimodal data and construct a query statement, input it into the embedding model to obtain farmland status description vector;

[0046] S4.1: Preprocess the raw data from multimodal IoT sensors:

[0047] The sliding window smoothing algorithm is used to clean and remove sensor noise from the sensor time series data, as shown below:

[0048]

[0049] Where, x i This means that for the data point at time i, the average of the k data points before and after it (a total of 2k+1 points) is taken as the smoothed result.

[0050] Outlier detection and correction utilizes the Z-score method to identify and handle outlier data points exceeding the 3σ range. The formula is as follows:

[0051]

[0052] When the monitored value is greater than 3σ, it is judged as an abnormal data point, the abnormal data is deleted and replaced with data from the adjacent time.

[0053] S4.2: Intelligent integration of pre-processed reliable sensor data, including: contextual information fusion, combining crop growth stages, historical irrigation records, and weather forecast data for the next 72 hours; multi-source data correlation analysis, establishing a soil-meteorological-crop three-element correlation model; and structured decision input generation, converting the fused multi-dimensional data into a structured input format that can be understood by a large language model, including environmental state descriptions, crop demand assessments, and decision constraints.

[0054] S4.3: Multi-step inference prediction based on the fine-tuned large language model, including:

[0055] Actual water requirement of crops (ET) c Calculation: The system is based on the Penman-Monteith model, combined with environmental parameters such as temperature, humidity, wind speed, solar radiation, and air pressure monitored by meteorological stations, to calculate the reference evapotranspiration (ET0). Crop status data such as vegetation index (NDVI) and leaf area index (LAI) acquired by multispectral UAVs are used to dynamically determine the crop coefficient (K). c This allows for the accurate calculation of the actual water requirements of crops, expressed as:

[0056] ET c =ET0×K c ×K s

[0057] Where Ks is the soil moisture stress coefficient. The system automatically identifies the crop growth stage through a deep learning model, matches it with the corresponding crop coefficient database, and dynamically adjusts it based on real-time monitoring data to ensure the accuracy of water requirement calculation.

[0058] Using soil moisture data such as temperature, humidity, electrical conductivity (EC), nitrogen, phosphorus, potassium content, and pH value obtained from soil moisture analyzers, a comprehensive soil water and fertilizer status assessment model was constructed. By establishing a soil nutrient availability evaluation index system, the supply capacity and deficiency degree of soil nitrogen, phosphorus, and potassium were quantitatively analyzed. The system employed multiple regression analysis to establish a correlation model between soil physicochemical properties and crop nutrient uptake efficiency, assessing the bioavailability of each nutrient element under current soil conditions, providing fundamental data support for subsequent fertilizer ratio calculations, as expressed below:

[0059] SNC=f(N soil ,P soil ,K soil ,PH,EC,Tem,Moisture)

[0060] SNC represents soil nutrient supply capacity, and the parameters represent the nitrogen, phosphorus, and potassium content in the soil and environmental factors, respectively.

[0061] Based on crop growth status data such as vegetation indices, chlorophyll content, and water stress indices acquired by UAV multispectral sensors, the system achieves intelligent identification and quantitative assessment of crop growth stress. By calculating various spectral indices such as the Normalized Difference Vegetation Index (NDVI), Simple Ratio Vegetation Index (SR), and Chlorophyll Index (CI), a crop nutrient status diagnostic model is constructed. The system uses machine learning algorithms to train the crop stress identification model, which can accurately determine various growth limiting factors such as nitrogen deficiency, phosphorus and potassium insufficiency, and water stress, and quantify the degree of stress.

[0062] The crop stress index is calculated as follows:

[0063] CSI=αCWSI+βNSI+γPSI+δKSI

[0064] Among them, CSI is the comprehensive stress index, CWSI is the water stress index, NSI, PSI and KSI are the nitrogen, phosphorus and potassium stress indices respectively, and α, β, γ and δ are weighting coefficients.

[0065] By utilizing real-time environmental data from meteorological stations, including rainfall, wind speed, temperature, air humidity, solar radiation, air pressure, and PM2.5, and combining this data with weather forecast information, a short-term environmental condition prediction model is constructed. Using time series analysis, an ARIMA model is established to predict meteorological trends over the next 3-7 days, with particular attention paid to environmental factors that significantly impact irrigation decisions, such as rainfall probability, evapotranspiration intensity, and extreme weather events. The system trains the prediction model using historical meteorological data to achieve forward-looking judgments on irrigation demand timing.

[0066] Based on the above information, calculate the amount of irrigation water and fertilizer needed, as well as the optimal fertilizer ratio; output the irrigation water volume, nitrogen, phosphorus, potassium, and other fertilizer ratio parameters, and the timing of execution.

[0067] S4.4: Generate interpretable decision analysis results, including:

[0068] The text-formatted decision explanation output analyzes whether irrigation and fertilization are needed based on the current farmland soil condition, meteorological conditions, and crop growth status detected by multispectral analysis. It explains the basis for the recommended fertilizer ratio parameter settings, proposes suggestions for soil improvement and vegetation optimization, and elaborates on the positive effects on crop growth.

[0069] Confidence assessment uses the Bayesian uncertainty quantification method to calculate the decision reliability score and outputs a confidence value in the range of 0-1. When the confidence level is lower than 0.8, a manual warning and review are performed.

[0070] Furthermore, in step S5, the precise closed-loop control of water and fertilizer based on large model decision-making includes:

[0071] S5.1: Based on the fertilizer ratio parameters output by the large language model, including key parameters such as nitrogen, phosphorus, and potassium concentrations, total fertilizer application, and application duration, the parameter parsing module converts the abstract decision parameters output by the model into specific hardware control instructions. The system establishes a mapping relationship between fertilizer concentration and solenoid valve opening, achieving precise conversion from nutrient requirements to physical control. A multi-channel GPIO control architecture is adopted, configuring independent control channels for nitrogen, phosphorus, and potassium fertilizer supply systems. Each channel includes PWM (Pulse Width Modulation) signal output, digital switch control, and analog output functions. The system precisely controls the fertilizer pump speed through PWM signals to achieve stepless flow regulation; controls the opening and closing of the solenoid valve through digital I / O to ensure precise timing of fertilizer supply; and adjusts the mixing ratio valve through analog output to achieve precise multi-element fertilizer ratio.

[0072] S5.2: Establish a precise water and fertilizer flow monitoring system, including:

[0073] ① The main pipeline flow meter monitors the total supply flow in real time, and the electromagnetic flow meter ensures the measurement accuracy is ≤±1%; ② Zone flow control, the independent flow regulation of each irrigation zone is realized through proportional integral regulating valves; ③ Precise control of irrigation duration, the optimal irrigation duration is calculated based on crop water requirements and soil water retention capacity; ④ Complete data recording, key parameters such as water volume, fertilizer application, and irrigation time are recorded in real time, and an irrigation archive database is established.

[0074] S5.3: Construct a three-level feedback control system to ensure precise control:

[0075] The real-time feedback layer, based on millisecond-level feedback from flow and pressure sensors, enables real-time monitoring and adjustment of the actuator status.

[0076] The mid-term feedback layer collects soil moisture changes every 15 minutes to assess irrigation effectiveness and dynamically adjust subsequent irrigation parameters;

[0077] The long-term feedback layer conducts periodic evaluations based on crop growth monitoring data and continuously optimizes irrigation strategies through reinforcement learning algorithms, forming a self-learning improvement mechanism.

[0078] Furthermore, in step S6, intelligent management and data visualization include:

[0079] S6.1: Construct a comprehensive data display platform, providing users with an intuitive system operation status monitoring interface by integrating sensor data, equipment operating status, and irrigation progress dynamic visualization through a real-time monitoring dashboard; offer historical trend analysis charts, using multi-dimensional data charts such as water and fertilizer utilization rate change curves, crop growth index trends, and yield prediction analysis to help users gain a deeper understanding of farmland management effectiveness and development trends; integrate a GIS geographic information system, and realize farmland zoning management, equipment positioning, and irrigation coverage area visualization based on a high-precision UAV oblique photography model; in addition, considering the mobile needs of modern agricultural management, the platform adopts a mobile-adaptive interface design, supporting responsive data access on mobile devices such as mobile phones and tablets, ensuring that managers can monitor farmland status anytime, anywhere;

[0080] S6.2: Establish a multi-level intelligent early warning mechanism to monitor key states such as sensor failure, equipment malfunction, and insufficient water and fertilizer resources in real time through anomaly detection and early warning functions, so as to ensure the stable operation of the system. This includes: agricultural meteorological early warning capabilities, which provide early warning of the potential impact of extreme weather on irrigation based on weather forecast data, and provide farmers with timely response strategy suggestions.

[0081] Using multispectral data analysis technology, the system can identify problems such as pests and diseases and nutrient deficiencies in crop growth, realize crop growth early warning function, and help farmers discover and deal with crop health problems in advance.

[0082] To ensure timely delivery of early warning information, the system supports multi-channel alarm push notifications, including SMS, email, APP push notifications, WeChat and other real-time notification methods, ensuring that critical information reaches relevant personnel as soon as possible and ensuring rapid response and handling.

[0083] S6.3: Integrates intelligent analysis and suggestion functions, providing users with customized irrigation plan improvement suggestions based on historical data and real-time status analysis through AI-driven strategy optimization suggestions, continuously optimizing farmland management effectiveness, including:

[0084] The cost-benefit intelligent analysis function automatically calculates and compares the cost of water and fertilizer input with the expected yield and return, helping farmers to scientifically evaluate the input-output ratio and maximize economic benefits.

[0085] The expert knowledge base access function combines the experience of agricultural experts with the latest research results to provide users with professional planting and management advice, transforming scientific research results into practical production guidance and improving the scientific and professional nature of farmland management.

[0086] S6.4: Automated reporting, including the scheduled generation and delivery of daily, weekly, monthly, and quarterly reports, provides managers with regular data analysis and summary reports to support decision-making and performance evaluation;

[0087] S6.5: Establish a comprehensive security protection mechanism to identify equipment anomalies through automatic sensor fault detection and data consistency verification, ensuring the accuracy and reliability of data acquisition;

[0088] The system provides flexible remote control and manual intervention functions, allowing users to manually modify irrigation parameters, start and stop equipment, etc. via the web or mobile terminal. When an abnormal situation is detected, the emergency stop safety mechanism can automatically cut off the power to all actuators or support remote emergency stop operations to ensure the safety of personnel and equipment.

[0089] The system records detailed logs of all operations, including key information such as operation time, operator, and operation content, providing a reliable basis for system management and problem tracking, and providing reliable security for automated farmland management.

[0090] The technical solution provided by this invention has the following beneficial effects:

[0091] This invention enables closed-loop management of the entire process, including intelligent sensing, precise decision-making, and automated control of the farmland environment. The method first acquires key data such as soil moisture, meteorological conditions, and crop growth status in real time through multi-source IoT sensors. Then, it constructs a specialized agricultural decision-making model based on a large language model architecture, employing LoRA fine-tuning technology to maintain the model's general reasoning capabilities while achieving precise adaptation to the agricultural field. For complex multidimensional decision problems, reinforcement learning mechanisms and Function Calling technology are introduced to achieve intelligent transformation from data sensing to equipment control, ultimately generating precise water and fertilizer replenishment strategies and executing them automatically. Field tests have verified that the system proposed in this invention performs excellently overall, improving water and fertilizer utilization by over 30% and increasing crop yield by 15-20%. Comparative analysis of the research results with traditional irrigation methods shows that this invention exhibits significant advantages, particularly in adaptability and decision-making accuracy under complex environmental conditions. Through multi-level feedback control and self-learning optimization mechanisms, the system can continuously improve irrigation strategies, achieving long-term, stable, and efficient agricultural production management. Attached Figure Description

[0092] Figure 1 This is a flowchart of a method for an intelligent water and fertilizer integrated irrigation system based on Internet of Things sensors and a large language model, provided by an embodiment of the present invention.

[0093] Figure 2 This is a system architecture diagram in an embodiment of the present invention;

[0094] Figure 3 This is an irrigation device according to an embodiment of the present invention.

[0095] Figure 4 This is an example of a soil testing instrument of the present invention.

[0096] Figure 5 This is a meteorological station, an example of the present invention.

[0097] Figure 6 This is a system panel of an example of the present invention. Detailed Implementation

[0098] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0099] refer to Figure 1 This invention provides an intelligent water and fertilizer integrated irrigation system based on IoT sensors and a large language model. The system includes the following steps:

[0100] S1: Based on multi-source high-precision IoT sensors deployed in farmland, real-time data on soil parameters, meteorological parameters, and plant growth status are acquired.

[0101] Specifically, this implementation example selects a tubular soil analyzer to acquire real-time soil conditions, including soil temperature, soil moisture, soil EC, nitrogen, phosphorus, potassium, pH value, and other soil moisture data; deploys small meteorological stations to monitor meteorological conditions in the irrigation area, including rainfall, wind speed, air temperature, air humidity, solar radiation (irradiance), air pressure, PM2.5, and other environmental data; deploys a DJI Mavic 3 multispectral drone to acquire crop growth status data such as vegetation index, chlorophyll content, and water stress index; and adopts a LoRa / NB-IoT hybrid network to achieve hierarchical data transmission (real-time soil moisture / meteorological aggregation) through an edge gateway, and deploys a lightweight filtering algorithm for preprocessing to ensure efficient and reliable transmission.

[0102] S2: Construct a professional knowledge base for the agricultural field based on crop growth data, crop environmental adaptability, irrigation and fertilization standards, and scientific research results in the field;

[0103] Specifically, based on crop variety resource data from the National Agricultural Science Data Center and crop growth cycle observation data from agricultural college experimental bases, a crop growth knowledge base, an environmental adaptability knowledge base, an irrigation and fertilization standard database, and a scientific research results database are constructed. A dynamic update mechanism for the knowledge base is established, and the knowledge content is connected to the agricultural data platform through an API interface to achieve real-time synchronous updates and version management.

[0104] S3: Based on a professional database in the agricultural field, data migration technology is used to adaptively fine-tune the large language model to build a professional knowledge decision-making irrigation model to meet the water and fertilizer supply needs;

[0105] Domain-adaptive pre-training technology is used to convert agricultural knowledge base data into a structured input format that the model can understand, including sensor data vectorization, crop feature encoding, and environmental parameter standardization.

[0106] Based on the LoRA (Low-Rank Adaptation) fine-tuning strategy, while freezing the parameters of the main language model, only the adapter parameters of specific layers are trained for the agricultural domain to maintain the model's general inference capabilities. This can be represented as follows:

[0107] h = W0x + ΔWx

[0108] Where w0 represents the original weights of the model, and Δw represents the weights of the low-rank matrix involved in fine-tuning. The influence weights of the LoRA layer are adjusted by setting the adapter parameter α, and the objective function is to minimize the loss between the predicted irrigation strategy and the expert annotation strategy.

[0109] By leveraging the Function Calling capabilities of a large language model, an intelligent agent system is constructed to enable automated interaction between the large model and IoT sensor interfaces. This mechanism, through structured function descriptions and parameter mapping techniques, allows the large model to understand and proactively call external API interfaces, achieving a seamless transition from natural language decision-making to specific device control. This includes:

[0110] The system adopts a standardized Function Calling protocol, encapsulating sensor API interfaces into callable function objects. Each function contains a complete definition of its name, description, parameter types, and return value format. The large model automatically identifies the required sensor interface types by parsing the current irrigation decision requirements and generates corresponding function call instructions.

[0111] The intelligent agent employs a cyclical decision-making framework of perception-reasoning-action. In the perception phase, a function calling mechanism invokes sensor data acquisition interfaces to collect real-time information such as soil moisture, weather conditions, and crop growth status. In the reasoning phase, based on the collected multi-dimensional data and combined with an agricultural knowledge base, comprehensive analysis and reasoning are performed to generate the optimal irrigation strategy. In the action phase, the decision results are converted into specific equipment control commands, and the function calling invokes the control interface of the integrated water and fertilizer machine to achieve automated irrigation execution.

[0112] By integrating reinforcement learning mechanisms, an interactive learning framework between an intelligent decision-making agent and the farmland environment is constructed. With crop yield, water and fertilizer utilization rate, and energy consumption control as the core optimization objectives, a multi-dimensional reward function is designed. Through continuous interaction with the environment, the irrigation decision strategy is continuously optimized, thereby achieving adaptive evolution and performance improvement of the model.

[0113] The system constructs a comprehensive reward function R(t), which integrates three core dimensions: crop yield efficiency, resource utilization efficiency, and energy consumption control, and is expressed as:

[0114] R(t) = α·R yield (t)+β·R e”iciency (t)+γ·R energy (t)

[0115] The reward functions for each component are defined as follows:

[0116] Crop yield reward function:

[0117]

[0118] Water and fertilizer utilization reward function:

[0119]

[0120] Energy consumption control reward function:

[0121]

[0122] Among them, Y predicted To predict output, Y baseline As the baseline output, Y optimal Theoretical optimal yield; WUE and FUE represent water use efficiency and fertilizer use efficiency, respectively; E consumed E represents actual energy consumption. budget α represents the budgeted energy consumption; α, β, and γ are weighting coefficients.

[0123] Furthermore, an interpretable output interface for the model is established to generate explanations of the decision-making basis and confidence assessments, ensuring the traceability and credibility of irrigation decisions.

[0124] S4: Real-time monitoring and transmission of multi-source IoT sensor data, combined with crop growth stages and environmental conditions, large-scale model makes precise decisions and generates dynamic water and fertilizer replenishment strategies.

[0125] Specifically, the data collected by the sensors is preprocessed and fused, and multi-step inference prediction is performed based on the finely tuned DeepSeek model to generate interpretable decision analysis results and water and fertilizer replenishment strategies.

[0126] S5: Based on the water and fertilizer supply strategy generated by the large model, the water and fertilizer supply parameters are analyzed, and the water and fertilizer irrigation controller is used to adjust the water and fertilizer integrated machine in real time to achieve precise closed-loop control of irrigation duration, flow rate and fertilizer ratio.

[0127] Based on fertilizer formulation parameters output from the DeepSeek large model, including key parameters such as nitrogen, phosphorus, and potassium concentrations, total fertilizer application amount, and application duration, the system uses a parameter parsing module to convert the abstract decision parameters output by the model into specific hardware control commands. The system establishes a mapping relationship between fertilizer concentration and solenoid valve opening, achieving precise conversion from nutrient requirements to physical control. It employs a multi-channel GPIO control architecture, configuring independent control channels for nitrogen, phosphorus, and potassium fertilizer supply systems. Each channel includes PWM (Pulse Width Modulation) signal output, digital switch control, and analog output functions. The system precisely controls the fertilizer pump speed through PWM signals, achieving stepless flow regulation; controls the opening and closing of solenoid valves through digital I / O, ensuring precise timing of fertilizer supply; and adjusts the mixing ratio valve through analog outputs, achieving precise multi-element fertilizer formulation.

[0128] Establish a precise water and fertilizer flow monitoring system, including: ① Real-time monitoring of the total supply flow by the main pipeline flow meter, using electromagnetic flow meters to ensure measurement accuracy ≤ ±1%; ② Zoned flow control, using proportional-integral regulating valves to achieve independent flow regulation for each irrigation zone; ③ Precise control of irrigation duration, calculating the optimal irrigation duration based on crop water requirements and soil water retention capacity; ④ Complete data recording, recording key parameters such as water volume, fertilizer application, and irrigation time in real time, and establishing an irrigation archive database.

[0129] A three-tiered feedback control system is constructed to ensure precise control: a real-time feedback layer, based on millisecond-level feedback from flow and pressure sensors, enables real-time monitoring and adjustment of actuator status; a medium-term feedback layer, which collects soil moisture changes every 15 minutes to assess irrigation effectiveness and dynamically adjust subsequent irrigation parameters; and a long-term feedback layer, which conducts periodic assessments based on crop growth monitoring data and continuously optimizes irrigation strategies through reinforcement learning algorithms, forming a self-learning improvement mechanism.

[0130] S6: The system application integrates multi-dimensional data dashboards, anomaly warnings, remote control consoles, manual intervention permissions, strategy optimization suggestions, and automatic report generation functions to achieve intelligent management and data visualization throughout the entire process.

[0131] Specifically, a comprehensive data display platform is constructed, providing users with an intuitive interface for monitoring system operation status through a real-time monitoring dashboard that integrates sensor data, equipment operating status, and irrigation progress. It also offers historical trend analysis charts, using multi-dimensional data charts such as water and fertilizer utilization rate curves, crop growth index trends, and yield prediction analysis to help users gain a deeper understanding of farmland management effectiveness and development trends. Furthermore, it integrates a GIS geographic information system, using a high-precision UAV oblique photography model to achieve farmland zoning management, equipment positioning, and visualization of irrigation coverage areas. Considering the mobile needs of modern agricultural management, the platform adopts a mobile-adaptive interface design, supporting responsive data access on mobile devices such as smartphones and tablets, ensuring that managers can monitor farmland status anytime, anywhere.

[0132] Establish a multi-level intelligent early warning mechanism to monitor key conditions such as sensor failure, equipment malfunction, and insufficient water and fertilizer resources in real time through anomaly detection and early warning functions, so as to ensure the stable operation of the system. This includes: agricultural meteorological early warning capabilities, which provide early warning of the potential impact of extreme weather on irrigation based on weather forecast data, and provide farmers with timely response strategy suggestions.

[0133] Using multispectral data analysis technology, the system can identify problems such as pests and diseases and nutrient deficiencies in crop growth, realize crop growth early warning function, and help farmers discover and deal with crop health problems in advance.

[0134] To ensure timely delivery of early warning information, the system supports multi-channel alarm push notifications, including SMS, email, APP push notifications, WeChat and other real-time notification methods, ensuring that critical information reaches relevant personnel as soon as possible and ensuring rapid response and handling.

[0135] This invention provides an intelligent integrated water and fertilizer irrigation system based on IoT sensors and the DeepSeek large-scale model. This system can independently achieve closed-loop management of the entire process, including multi-source data perception, intelligent decision analysis, and precise automated control in farmland environments. First, the system acquires multi-dimensional environmental data of farmland in real time by deploying IoT devices such as soil sensors, weather stations, and multispectral drones. Then, it constructs an agricultural professional knowledge decision model based on the DeepSeek large-scale language model architecture. LoRA fine-tuning technology and Function Calling mechanism are used to achieve domain adaptation of the model and device control capabilities. For complex multi-objective optimization problems, reinforcement learning algorithms are introduced to construct a comprehensive reward function for strategy optimization. Finally, a precise water and fertilizer replenishment strategy is generated and automatically executed through a three-level feedback control system. Verification results show that the system developed in this invention can significantly improve agricultural production efficiency, increasing water and fertilizer utilization by more than 30% and crop yield by 15-20%. Furthermore, it can continuously optimize irrigation strategies under complex environmental conditions through an adaptive learning mechanism, demonstrating good practicality and reliability in smart agriculture applications.

[0136] 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, method, article, or system 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, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0137] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. In the unit claims listing several devices, several of these devices may be embodied by the same hardware item. The use of the terms first, second, and third, etc., does not indicate any order and can be interpreted as identifiers.

[0138] The above are merely preferred embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A smart water and fertilizer integrated irrigation system based on Internet of Things sensors and large language models, characterized in that, Includes the following steps: S1: Based on multi-source high-precision IoT sensors deployed in farmland, real-time data on soil parameters, meteorological parameters, and plant growth status are acquired; S2: Construct a professional knowledge base for the agricultural field based on crop growth data, crop environmental adaptability, irrigation and fertilization standards, and scientific research results in the field; S3: Based on a professional database in the agricultural field, data migration technology is used to adaptively fine-tune the large language model to build a professional knowledge decision-making irrigation model to meet the water and fertilizer supply needs; S4: Real-time monitoring and transmission of multi-source IoT sensor data, combined with crop growth stages and environmental conditions, large-scale model makes precise decisions and generates dynamic water and fertilizer replenishment strategies. S5: Based on the water and fertilizer supply strategy generated by the large model, the water and fertilizer supply parameters are analyzed, and the water and fertilizer irrigation controller is used to adjust the water and fertilizer integrated machine in real time to achieve precise closed-loop control of irrigation duration, flow rate and fertilizer ratio. S6: The system application integrates multi-dimensional data dashboards, anomaly warnings, remote control consoles, manual intervention permissions, strategy optimization suggestions, and automatic report generation functions to achieve intelligent management and data visualization throughout the entire process.

2. The intelligent water and fertilizer integrated irrigation system based on IoT sensors and large language models according to claim 1, characterized in that, In step S1, the deployment of multi-source IoT sensors and real-time data monitoring include: S1.1: Deploy soil testing instruments to monitor soil conditions in the irrigation area, including soil temperature, soil moisture, soil EC, nitrogen, phosphorus, potassium, pH value and other soil moisture data; S1.2: Deploy small meteorological stations to monitor meteorological conditions in the irrigation area, including: rainfall, wind speed, temperature, air humidity, solar radiation (irradiance), air pressure, PM2.5 and other environmental data; S1.3: Deploy multispectral drones to acquire crop growth status data such as vegetation index, chlorophyll content, and water stress index; S1.4: Adopts LoRa / NB-IoT hybrid networking, realizes hierarchical data transmission (real-time soil moisture / meteorological aggregation) through edge gateway, and deploys lightweight filtering algorithm preprocessing to ensure efficient and reliable transmission.

3. The intelligent water and fertilizer integrated irrigation system based on IoT sensors and large language models according to claim 1, characterized in that, In step S2, the construction of the multi-agricultural domain knowledge base includes: S2.1: Construct a crop growth knowledge base, collect and organize the life cycle classification standards, water and nutrient requirements for each growth stage, optimal environmental parameter ranges and yield and quality evaluation indicators for different crop varieties. S2.2: Construct an environmental adaptability knowledge base and establish a mapping relationship between soil type, climate conditions and crop suitability, including the water and fertilizer retention characteristics of different soil types, the influence of meteorological factors on crop growth and extreme weather response strategies. S2.3: Construct an irrigation and fertilization standard database, integrating standardized irrigation systems, fertilizer application technical procedures, water-fertilizer coupling effect parameters, and water-saving and efficiency-enhancing technical specifications issued by the agricultural sector; S2.4: Construct a database of scientific research results, integrating the latest research results from agricultural research institutes, including new irrigation technologies, precision fertilization algorithms, crop model parameters, and field test data; S2.5: Establish a dynamic update mechanism for the knowledge base, and connect to the agricultural data platform through the API interface to realize real-time synchronization and version management of knowledge content.

4. The intelligent water and fertilizer integrated irrigation system based on IoT sensors and large language models according to claim 1, characterized in that, In step S3, the construction of the professional knowledge decision irrigation model based on the large language model includes: S3.1: Employ domain-adaptive pre-training technology to convert agricultural knowledge base data into a structured input format that the model can understand, including sensor data vectorization, crop feature encoding, and environmental parameter standardization; S3.2: Based on the LoRA (Low-Rank Adaptation) fine-tuning strategy, while freezing the parameters of the large language model, only the adapter parameters of specific layers are trained for the agricultural domain to maintain the model's general reasoning ability, as shown below: h=W0x+ΔWx Where W0 represents the original weights of the model, and ΔW represents the weights of the low-rank matrix involved in fine-tuning. The influence weights of the LoRA layer are adjusted by setting the adapter parameter α, and the objective function is to minimize the loss between the predicted irrigation strategy and the expert annotation strategy. S3.3: Utilizing the Function Calling feature of a large language model, an intelligent agent system is built to enable automated interaction and invocation between the large model and IoT sensor interfaces. This mechanism, through structured function descriptions and parameter mapping techniques, allows the large model to understand and proactively call external API interfaces, achieving a seamless transition from natural language decision-making to specific device control, including: The system adopts a standardized Function Calling protocol, encapsulating sensor API interfaces into callable function objects. Each function contains a complete definition of its name, description, parameter types, and return value format. The large model automatically identifies the required sensor interface types by parsing the current irrigation decision requirements and generates corresponding function call instructions. The intelligent agent employs a cyclical decision-making framework of perception-reasoning-action. In the perception phase, a function calling mechanism invokes sensor data acquisition interfaces to collect real-time information such as soil moisture, weather conditions, and crop growth status. In the reasoning phase, based on the collected multi-dimensional data and combined with an agricultural knowledge base, comprehensive analysis and reasoning are performed to generate the optimal irrigation strategy. In the action phase, the decision results are converted into specific equipment control commands, and the function calling invokes the control interface of the integrated water and fertilizer machine to achieve automated irrigation execution. S3.4: Integrate reinforcement learning mechanisms to construct an interactive learning framework between the intelligent decision-making agent and the farmland environment. With crop yield, water and fertilizer utilization rate, and energy consumption control as the core optimization objectives, design a multi-dimensional reward function and continuously optimize irrigation decision strategies through continuous interaction with the environment to achieve adaptive evolution and performance improvement of the model. The system constructs a comprehensive reward function R(t), which integrates three core dimensions: crop yield efficiency, resource utilization efficiency, and energy consumption control, and is expressed as: R(t)=α·R yield (t)+β·R e”iciency (t)+γ·R energy (t) The reward functions for each component are defined as follows: Crop yield reward function: Water and fertilizer utilization reward function: Energy consumption control reward function: Among them, Y predicted To predict output, Y baseline As the baseline output, Y optimal Theoretical optimal yield; WUE and FUE represent water use efficiency and fertilizer use efficiency, respectively; E consumed E represents actual energy consumption. budget α represents the budgeted energy consumption; α, β, and γ are weighting coefficients. S3.5: Establish an interpretable output interface for the model to generate explanations of the decision-making basis and confidence assessments, ensuring the traceability and credibility of irrigation decisions.

5. The intelligent water and fertilizer integrated irrigation system based on IoT sensors and large language models according to claim 1, characterized in that, In step S4, the multi-source data is input into the professional knowledge decision irrigation model, which outputs decision parameters such as irrigation amount and fertilizer ratio, as well as interpretable decision results, including: S4.1: Perform time-series analysis on the data recorded by the sensors to obtain soil, meteorological and crop growth status parameters; use an encoding model to fuse multimodal data and construct a query statement, input it into the embedding model to obtain farmland status description vector; S4.1: Preprocessing raw data from multimodal IoT sensors: The sliding window smoothing algorithm is used to clean and remove sensor noise from the sensor time series data, as shown below: Where, x i This means that for the data point at time i, the average of the k data points before and after it (a total of 2k+1 points) is taken as the smoothed result. Outlier detection and correction utilizes the Z-score method to identify and handle outlier data points exceeding the 3σ range. The formula is as follows: When the monitored value is greater than 3σ, it is judged as an abnormal data point, the abnormal data is deleted and replaced with data from the adjacent time. S4.2: Intelligent integration of pre-processed reliable sensor data, including: contextual information fusion, combining crop growth stages, historical irrigation records, and weather forecast data for the next 72 hours; multi-source data correlation analysis, establishing a soil-meteorological-crop three-element correlation model; and structured decision input generation, converting the fused multi-dimensional data into a structured input format that can be understood by a large language model, including environmental state descriptions, crop demand assessments, and decision constraints. S4.3: Multi-step inference prediction based on a fine-tuned large language model, including: Actual water requirement of crops (ET) c Calculation: The system is based on the Penman-Monteith model, combined with environmental parameters such as temperature, humidity, wind speed, solar radiation, and air pressure monitored by meteorological stations, to calculate the reference evapotranspiration (ET0). Crop status data such as vegetation index (NDVI) and leaf area index (LAI) acquired by multispectral UAVs are used to dynamically determine the crop coefficient (K). c This allows for the accurate calculation of the actual water requirements of crops, expressed as: AND c <ET0×K c ×K s Wherein, Ks is the soil moisture stress coefficient. The system automatically identifies the crop growth stage through a deep learning model, matches it with the corresponding crop coefficient database, and dynamically adjusts it based on real-time monitoring data to ensure the accuracy of water requirement calculation. Using soil moisture data such as temperature, humidity, electrical conductivity (EC), nitrogen, phosphorus, potassium content, and pH value obtained from soil moisture analyzers, a comprehensive soil water and fertilizer status assessment model was constructed. By establishing a soil nutrient availability evaluation index system, the supply capacity and deficiency degree of soil nitrogen, phosphorus, and potassium were quantitatively analyzed. The system employed multiple regression analysis to establish a correlation model between soil physicochemical properties and crop nutrient uptake efficiency, assessing the bioavailability of each nutrient element under current soil conditions, providing fundamental data support for subsequent fertilizer ratio calculations, as expressed below: SNC=f(N soil ,P soil ,K soil ,PH,EC,Tem,Moisture) SNC represents soil nutrient supply capacity, and the parameters represent the nitrogen, phosphorus, and potassium content in the soil and environmental factors, respectively. Based on crop growth status data such as vegetation indices, chlorophyll content, and water stress indices acquired by UAV multispectral sensors, the system achieves intelligent identification and quantitative assessment of crop growth stress. By calculating various spectral indices such as the Normalized Difference Vegetation Index (NDVI), Simple Ratio Vegetation Index (SR), and Chlorophyll Index (CI), a crop nutrient status diagnostic model is constructed. The system uses machine learning algorithms to train the crop stress identification model, which can accurately determine various growth limiting factors such as nitrogen deficiency, phosphorus and potassium insufficiency, and water stress, and quantify the degree of stress. The crop stress index is calculated as follows: CSI=αCWSI+βNSI+γPSI+δKSI Among them, CSI is the comprehensive stress index, CWSI is the water stress index, NSI, PSI and KSI are the nitrogen, phosphorus and potassium stress indices respectively, and α, β, γ and δ are weighting coefficients. By utilizing real-time environmental data from meteorological stations, including rainfall, wind speed, temperature, air humidity, solar radiation, air pressure, and PM2.5, and combining this data with weather forecast information, a short-term environmental condition prediction model is constructed. Using time series analysis, an ARIMA model is established to predict meteorological trends over the next 3-7 days, with particular attention paid to environmental factors that significantly impact irrigation decisions, such as rainfall probability, evapotranspiration intensity, and extreme weather events. The system trains the prediction model using historical meteorological data to achieve forward-looking judgments on irrigation demand timing. Based on the above information, calculate the amount of irrigation water and fertilizer needed, as well as the optimal fertilizer ratio; output the irrigation water volume, nitrogen, phosphorus, potassium, and other fertilizer ratio parameters, and the timing of execution. S4.4: Generate interpretable decision analysis results, including: The text-formatted decision explanation output analyzes whether irrigation and fertilization are needed based on the current farmland soil condition, meteorological conditions, and crop growth status detected by multispectral analysis. It explains the basis for the recommended fertilizer ratio parameter settings, proposes suggestions for soil improvement and vegetation optimization, and elaborates on the positive effects on crop growth. Confidence assessment uses the Bayesian uncertainty quantification method to calculate the decision reliability score and outputs a confidence value in the range of 0-1. When the confidence level is lower than 0.8, a manual warning and review are performed.

6. The intelligent water and fertilizer integrated irrigation system based on IoT sensors and large language models according to claim 1, characterized in that, In step S5, the precise closed-loop control of water and fertilizer based on large language model decision-making includes: S5.1: Based on the fertilizer ratio parameters output by the large language model, including key parameters such as nitrogen, phosphorus, and potassium concentrations, total fertilizer application, and application duration, the parameter parsing module converts the abstract decision parameters output by the model into specific hardware control instructions. The system establishes a mapping relationship between fertilizer concentration and solenoid valve opening, achieving precise conversion from nutrient requirements to physical control. A multi-channel GPIO control architecture is adopted, configuring independent control channels for nitrogen, phosphorus, and potassium fertilizer supply systems. Each channel includes PWM (Pulse Width Modulation) signal output, digital switch control, and analog output functions. The system precisely controls the fertilizer pump speed through PWM signals to achieve stepless flow regulation; controls the opening and closing of the solenoid valve through digital I / O to ensure precise timing of fertilizer supply; and adjusts the mixing ratio valve through analog output to achieve precise multi-element fertilizer ratio. S5.2: Establish a precise water and fertilizer flow monitoring system, including: ① The main pipeline flow meter monitors the total supply flow in real time, and an electromagnetic flow meter is used to ensure measurement accuracy ≤ ±1%; ②Zoned flow control: Independent flow regulation of each irrigation zone is achieved through proportional-integral control valves; ③Precise control of irrigation duration: Optimal irrigation duration is calculated based on crop water requirements and soil water retention capacity; ④Complete data recording: Key parameters such as water volume, fertilizer application, and irrigation time are recorded in real time to establish an irrigation archive database. S5.3: Construct a three-level feedback control system to ensure precise control: The real-time feedback layer, based on millisecond-level feedback from flow and pressure sensors, enables real-time monitoring and adjustment of the actuator status. The mid-term feedback layer collects soil moisture changes every 15 minutes to assess irrigation effectiveness and dynamically adjust subsequent irrigation parameters; The long-term feedback layer conducts periodic evaluations based on crop growth monitoring data and continuously optimizes irrigation strategies through reinforcement learning algorithms, forming a self-learning improvement mechanism.

7. The intelligent water and fertilizer integrated irrigation system based on IoT sensors and a large language model according to claim 1, characterized in that, In step S6, intelligent management and data visualization include: S6.1: Construct a comprehensive data display platform, providing users with an intuitive system operation status monitoring interface by integrating sensor data, equipment operating status, and irrigation progress dynamic visualization through a real-time monitoring dashboard; offer historical trend analysis charts, using multi-dimensional data charts such as water and fertilizer utilization rate change curves, crop growth index trends, and yield prediction analysis to help users gain a deeper understanding of farmland management effectiveness and development trends; integrate a GIS geographic information system, using a high-precision UAV oblique photography model to achieve farmland zoning management, equipment positioning, and irrigation coverage area visualization; furthermore, considering the mobile needs of modern agricultural management, the platform adopts a mobile-adaptive interface design, supporting responsive data access on mobile devices such as smartphones and tablets, ensuring that managers can monitor farmland status anytime, anywhere; S6.2: Establish a multi-level intelligent early warning mechanism to monitor key states such as sensor failure, equipment malfunction, and insufficient water and fertilizer resources in real time through anomaly detection and early warning functions, so as to ensure the stable operation of the system. This includes: agricultural meteorological early warning capabilities, which provide early warning of the potential impact of extreme weather on irrigation based on weather forecast data, and provide farmers with timely response strategy suggestions. Using multispectral data analysis technology, the system can identify problems such as pests and diseases and nutrient deficiencies in crop growth, realize crop growth early warning function, and help farmers discover and deal with crop health problems in advance. To ensure timely delivery of early warning information, the system supports multi-channel alarm push notifications, including SMS, email, APP push notifications, WeChat and other real-time notification methods, ensuring that critical information reaches relevant personnel as soon as possible and ensuring rapid response and handling. S6.3: Integrates intelligent analysis and suggestion functions, providing users with customized irrigation plan improvement suggestions based on historical data and real-time status analysis through AI-driven strategy optimization suggestions, continuously optimizing farmland management effectiveness, including: The cost-benefit intelligent analysis function automatically calculates and compares the cost of water and fertilizer input with the expected yield and return, helping farmers to scientifically evaluate the input-output ratio and maximize economic benefits. The expert knowledge base access function combines the experience of agricultural experts with the latest research results to provide users with professional planting and management advice, transforming scientific research results into practical production guidance and improving the scientific and professional nature of farmland management. S6.4: Automated reporting, including the scheduled generation and delivery of daily, weekly, monthly, and quarterly reports, provides managers with regular data analysis and summary reports to support decision-making and performance evaluation; S6.5: Establish a comprehensive security protection mechanism to identify equipment anomalies through automatic sensor fault detection and data consistency verification, ensuring the accuracy and reliability of data acquisition; The system provides flexible remote control and manual intervention functions, allowing users to manually modify irrigation parameters, start and stop equipment, etc. via the web or mobile terminal. When an abnormal situation is detected, the emergency stop safety mechanism can automatically cut off the power to all actuators or support remote emergency stop operations to ensure the safety of personnel and equipment. The system records detailed logs of all operations, including key information such as operation time, operator, and operation content, providing a reliable basis for system management and problem tracking, and providing reliable security for automated farmland management.

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