Intelligent irrigation decision-making method for garden plants

Abstract

The invention belongs to the technical field of artificial intelligence and agricultural intellectualization, and particularly relates to an intelligent irrigation decision-making method for garden plants. The method aims at solving the problems that a traditional irrigation system is large in sensing data noise, poor in decision model adaptability and lack of dynamic optimization capacity. Multi-source data synchronous acquisition of soil humidity, meteorological parameters and plant canopy temperature is realized by arranging a distributed sensor network, and data reliability is improved by combining timestamp alignment and adaptive Kalman filtering; constructing a water demand prediction model fusing a Penman-Monteith model and a soil moisture kinetic equation, and generating a dynamic irrigation demand benchmark; and a lightweight convolutional neural network is introduced to carry out fusion discrimination on the multi-modal features. Through data driving and mechanism model collaboration, the accuracy and adaptive capacity of irrigation decision making are remarkably improved, and the multiple purposes of water saving, energy saving and healthy growth of plants are achieved.

Description

Technical Field

[0001] This application belongs to the field of artificial intelligence and intelligent agriculture technology, specifically relating to an intelligent irrigation decision-making method for garden plants. Background Technology

[0002] With the deep integration of smart agriculture and IoT technology, landscape management is gradually developing towards intelligence and precision. Efficient water resource utilization plays a central role in urban landscape maintenance. Traditional irrigation methods often rely on manual experience or timed control strategies, lacking the dynamic perception of plants' actual water needs, leading to delayed irrigation decisions and low water use efficiency. Especially in landscape settings with complex climates and diverse vegetation configurations, the interplay of multiple factors such as soil moisture, meteorological conditions, and plant physiological parameters places higher demands on the environmental adaptability and scientific decision-making of irrigation systems.

[0003] Among them, intelligent irrigation systems have achieved an initial transformation from "experience-driven" to "data-driven" by deploying environmental sensors and automated control equipment. These systems aim to dynamically adjust irrigation plans based on environmental monitoring data to improve water resource utilization and ensure healthy plant growth. However, existing technologies generally perform simple threshold comparisons or linear weighting on multi-source sensing data, failing to fully explore the nonlinear correlations and spatiotemporal evolution patterns between data points. This results in weak generalization ability of decision-making models, making it difficult to adapt to personalized irrigation needs under different plant species, growth stages, and microclimate variations.

[0004] Current technologies for intelligent irrigation decision-making in garden plants still face multiple technical bottlenecks. First, environmental sensing data suffers from significant noise interference and asynchronous sampling, making it difficult for traditional filtering and fusion methods to guarantee the reliability and timeliness of input data, thus affecting the accuracy of subsequent decisions. Second, most systems use static rule bases for decision-making reasoning, lacking the ability to dynamically model physiological and ecological processes such as plant transpiration and soil water-holding characteristics, thus failing to accurately predict water demand. Furthermore, the irrigation strategy generation process neglects the execution feedback loop, failing to incorporate post-irrigation soil moisture response and plant state changes into the model's iterative optimization mechanism, leading to over-irrigation or under-irrigation phenomena during long-term system operation. Therefore, there is an urgent need for an intelligent irrigation decision-making method for garden plants that can integrate multi-source heterogeneous sensing information and possess dynamic learning and closed-loop optimization capabilities. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent irrigation decision-making method for garden plants, which can effectively solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A smart irrigation decision-making method for garden plants includes the following specific steps: Step (1) Multi-source heterogeneous sensing data acquisition and synchronization processing: A distributed sensor network is deployed in the garden area to collect data on soil moisture, air temperature and humidity, light intensity, wind speed, rainfall and plant canopy temperature in real time. The sensor network uses timestamp alignment and sliding window interpolation algorithms to sample and synchronize multi-source data, eliminating the data asynchrony problem caused by device response delay, and uses adaptive Kalman filtering to suppress noise in the original observation values, outputting a calibrated environmental state vector; Step (2) Based on Dynamic modeling of water demand based on physiological and ecological mechanisms: Construct a coupled model integrating the Penman-Monteith evapotranspiration model and the soil moisture dynamics equation. Input the environmental state vector processed in step (1) and the preset plant type parameters, growth stage coefficients, and root distribution depth. Calculate the instantaneous potential evapotranspiration and the effective soil water deficit to generate a dynamic water demand prediction value. Step (3) Multimodal data fusion and irrigation demand discrimination: Combine the water demand prediction value output in step (2) with the plant canopy temperature gradient change rate and soil moisture gradient using multi-dimensional features to form a high-dimensional decision. The policy input vector is input into a lightweight convolutional neural network model trained with historical data. The neural network extracts local features through convolutional layers and achieves nonlinear mapping through fully connected layers, outputting irrigation demand level judgment results, including four categories: no demand, low demand, medium demand and high demand; Step (4) closed-loop feedback driven irrigation strategy generation and optimization: an initial irrigation strategy is generated based on the judgment results of step (3), including irrigation start and end time, target irrigation volume and control area. After the irrigation operation is executed, the soil moisture response curve and plant physiological state change data of the irrigation area are continuously collected, the actual water replenishment efficiency and plant stress response index are calculated, and the above feedback information is input into the online reinforcement learning module. The Q learning algorithm is used to update the decision model parameters to realize the dynamic iterative optimization of the irrigation strategy; Step (5) multi-objective collaborative optimization and instruction issuance: taking into account the regional water resource quota, power load peak and valley periods, meteorological forecast precipitation probability and plant community competition relationship, the irrigation strategies of multiple sub-regions are prioritized and time staggered to generate the global optimal irrigation control instruction sequence, and issued to the electric valve and water pump controller through the wireless communication module to complete the precise irrigation execution.

[0008] Preferably, in step (1), the distributed sensor network consists of at least 32 nodes, with a deployment spacing of 15 to 25 meters between each node. The soil moisture sensors are buried at depths of 20 cm, 40 cm, and 60 cm in three layers. The air temperature and humidity sensors are installed at a height of 1.5 meters. The plant canopy temperature is obtained by scanning with an infrared thermal imaging sensor every 10 minutes. The timestamp alignment accuracy is controlled within ±50 milliseconds. The sliding window length is set to 5 sampling periods. The state transition matrix of the adaptive Kalman filter is adjusted in real time according to the dynamic response characteristics of the sensor. The process noise covariance ranges from 0.01 to 0.08, and the observation noise covariance ranges from 0.02 to 0.1, ensuring a balance between the filtering convergence speed and stability.

[0009] Preferably, in step (2), the surface resistance term of the Penman-Monteith model is dynamically corrected according to the empirical formula of plant stomatal conductance. The input plant type parameters include broad-leaved trees, coniferous trees, or turfgrass. The growth stage coefficients are divided into seedling stage (0.3), growth stage (0.7), maturity stage (1.0), and dormancy stage (0.2). The root distribution depth is set to 30 cm to 120 cm according to the plant species. The soil moisture dynamics equation adopts the simplified form of the Richards equation. The effective water storage capacity is calculated by combining field water holding capacity and wilting point data. The instantaneous potential evapotranspiration calculation time step is 1 minute. The effective soil water deficit is defined as the difference between the current water content and the field water holding capacity. When this value is greater than 15%, the water replenishment mechanism is triggered.

[0010] Preferably, the high-dimensional decision input vector in step (3) contains 7 dimensions, namely: the filtered soil surface humidity value, the humidity change rate of the middle layer, the humidity gradient of the deep layer, the relative humidity of the air, the net radiation intensity, the square term of the wind speed, and the first-order difference value of the plant canopy temperature. The lightweight convolutional neural network contains 2 convolutional layers and 1 max pooling layer. The convolutional kernel size is 1×3. The activation function is ReLU. The number of neurons in the fully connected layer is 64. The output layer uses the Softmax function to realize the four-class classification decision. The model training uses the historical dataset of the past 12 months, with a total sample size of more than 80,000. The cross-entropy loss function and the Adam optimizer are used during the training process. The initial learning rate is set to 0.001, the batch size is 32, and after training convergence, the model has a classification accuracy of more than 96% and an F1-score of more than 0.94 on the validation set.

[0011] Preferably, in step (4), the actual water replenishment efficiency is defined as the ratio of the increase in effective soil moisture content after irrigation to the theoretical irrigation amount. The plant stress response index is calculated by weighting the canopy temperature rise rate and the transpiration inhibition rate, with weighting coefficients of 0.6 and 0.4, respectively. The state space of the reinforcement learning module includes the current soil moisture status, weather trend, and plant health score. The action space is the irrigation amount adjustment level. The reward function is designed as a weighted objective function of comprehensive water saving rate, plant growth health and system energy consumption. The discount factor is set to 0.9 and the learning rate is set to 0.1. The model parameters are updated once after each complete irrigation cycle to ensure that the decision-making strategy is continuously optimized with environmental changes.

[0012] Preferably, in step (5), the water resource quota is allocated on a daily basis, with a daily total not exceeding 50 cubic meters per hectare. The peak and valley periods of the power load are divided into peak (8:00-11:00, 18:00-21:00), flat and low (23:00-6:00) periods based on local power grid data. Non-emergency irrigation tasks are automatically postponed when the weather forecast has a precipitation probability greater than 40%. The plant community competition relationship is calculated by the difference in the distance between neighboring plants and the transpiration rate. The priority ranking adopts a multi-attribute decision algorithm. After the irrigation control command sequence is generated, conflict detection and resource occupation verification are performed to ensure that no concurrent operation occurs on the same pipeline branch. The command is issued using an encrypted communication protocol with a transmission delay of less than 200 milliseconds.

[0013] Preferably, it also includes an edge computing gateway device, deployed at the garden management site, for performing localized data processing and model inference in steps (1) to (4). The gateway has a built-in dual-core processor with a main frequency of not less than 1.2GHz, a memory capacity of not less than 2GB, a storage space of not less than 16GB, supports Modbus and MQTT protocol conversion, can run the decision process independently in offline state, and enables a local caching strategy when the network is interrupted, and synchronizes the data to the cloud management platform after the connection is restored.

[0014] Preferably, it also includes a visualization monitoring module based on digital twins, which constructs a three-dimensional geographic information model of the garden area, maps the location of each sensor, the soil moisture distribution heat map, the plant health status identifier and the irrigation execution trajectory in real time, supports the retrospective of historical decision-making processes by time axis, and allows managers to set plant type labels, adjust model parameter thresholds or manually intervene in irrigation instructions through the interactive interface. All operation logs are automatically recorded and used for subsequent model retraining.

[0015] Preferably, it also includes an anomaly detection and fault tolerance mechanism, which performs triple verification of the continuity, numerical rationality and spatial consistency of sensor data. If the data of a certain node deviates from the mean of neighboring nodes by more than 3 times the standard deviation and the duration is greater than 10 minutes, it is determined to be a faulty node. The system automatically activates an alternative data source based on spatial interpolation and historical trend prediction, sends an alarm message to the operation and maintenance terminal, and freezes the weight of the node in the decision-making until the repair is confirmed.

[0016] Compared with the prior art, the present invention has the following beneficial effects:

[0017] Improving decision-making accuracy and environmental adaptability: By using adaptive Kalman filtering and timestamp alignment technology, noise interference and asynchronous sampling problems of multi-source sensing data are effectively eliminated, improving the reliability of input data to over 98%; The coupled water demand model constructed by combining the Penman-Monteith model and the soil moisture dynamics equation fully reflects the dynamic relationship between plant transpiration and soil water holding capacity, reducing the prediction error by 42% compared to the traditional static threshold method; The introduction of a lightweight convolutional neural network enables multimodal feature fusion and nonlinear discrimination, significantly enhancing the adaptability to complex microclimates and diverse vegetation configurations.

[0018] Achieving dynamic learning and closed-loop optimization: Through the online reinforcement learning module, the soil response and plant status feedback after irrigation are continuously absorbed, and the decision model parameters are dynamically adjusted to form a complete closed loop of "perception-decision-execution-feedback". This effectively avoids over-irrigation or insufficient water supply in long-term operation. After 3 months of system operation, the water saving rate increased by 28% and the plant health index increased by an average of 19%.

[0019] Ensuring system robustness and maintainability: The edge computing gateway supports local independent operation, ensuring that basic decision-making functions can still be maintained in the event of network anomalies; the digital twin visualization module provides intuitive operation monitoring and manual intervention channels, improving management efficiency; the anomaly detection mechanism realizes automatic identification and data compensation of sensor faults, ensuring long-term stable operation of the system and reducing operation and maintenance costs by 35%.

[0020] Achieving multi-objective collaborative optimization: Taking into account water resource constraints, energy costs, meteorological conditions and plant ecological relationships, the system generates globally optimal irrigation instructions through priority ranking and time-staggered scheduling, avoiding concentrated pipeline load and resource waste. Under the premise of ensuring plant water needs, the daily average electricity load peak-valley difference is reduced by 22%, and the water resource utilization efficiency reaches over 91%. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the overall technical solution architecture of the intelligent irrigation decision-making method for garden plants proposed in this invention;

[0022] Figure 2 This is a schematic diagram of the core principle framework for multimodal irrigation demand discrimination based on the fusion of physiological and ecological mechanisms and lightweight convolutional neural networks in this invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0024] Currently, in response to the above-mentioned technical problems, this invention proposes a solution and applies it to an intelligent irrigation decision-making method for garden plants.

[0025] In the aforementioned intelligent irrigation decision-making method for garden plants, step (1), multi-source heterogeneous sensing data acquisition and synchronization processing, aims to achieve high-precision and high-reliability real-time sensing of complex environmental parameters within the garden area. Specifically, step (1) includes deploying a distributed sensor network consisting of at least 32 sensor nodes within the garden area. The nodes are networked using a wireless communication protocol, with a deployment spacing of 15 to 25 meters, forming a gridded monitoring system covering the entire management area. Each sensor node integrates multiple sensor modules to synchronously collect multi-source heterogeneous data such as soil moisture, air temperature and humidity, light intensity, wind speed, rainfall, and plant canopy temperature. Among them, the soil moisture sensor adopts the frequency domain reflectance (FDR) principle and is buried at three depths of 20 cm, 40 cm and 60 cm underground to obtain the vertical profile of moisture distribution; the air temperature and humidity sensor is installed at a standard meteorological observation position 1.5 meters above the ground to avoid interference from ground heat radiation; the light intensity sensor uses silicon photovoltaic cell elements with a range of 0 to 200,000 lux (lx) and a resolution of not less than 10 lx; ​​the wind speed sensor uses an ultrasonic anemometer with a measurement range of 0 to 60 meters per second (m / s) and an initial wind speed not exceeding 0.5 m / s; the rainfall sensor uses a tipping bucket rain gauge with a resolution of 0.2 mm; the plant canopy temperature is obtained through a non-contact infrared thermal imaging sensor with a resolution of 320×240 pixels, a temperature measurement range of -20°C to 80°C, an accuracy of ±1.5°C, and a scanning frequency set to once every 10 minutes to ensure the capture of thermodynamic changes during the daytime transpiration peak.

[0026] To address the asynchronous data sampling issue caused by differences in response times among different sensors, the sensor network employs a collaborative processing mechanism combining timestamp alignment and sliding window interpolation algorithms. All sensor data is appended with a timestamp generated by a high-precision real-time clock (RTC) module during acquisition, with the time base unified to UTC+8 standard time, and the timestamp alignment accuracy controlled within ±50 milliseconds. When the system receives raw data streams from different nodes, it first sorts and aggregates them according to their timestamps. For minor time offsets caused by transmission delays or inconsistent sampling periods, linear interpolation compensation is performed using a sliding window with a length of 5 sampling periods. For example, if a node reports data at five times: t=10:00:00, 10:00:60, 10:01:00, 10:01:60, and 10:02:00, while neighboring nodes only report at even-numbered minutes, the system performs linear interpolation at 10:01:30 using two valid data points before and after the timest to generate an estimate of the intermediate time, thus ensuring the continuity and consistency of the time series input to the subsequent model.

[0027] To further improve data quality and eliminate the influence of inherent sensor noise and external electromagnetic interference, step (1) employs an adaptive Kalman filter to suppress noise in the original observations. The state transition matrix of the filter is adjusted in real time according to the dynamic response characteristics of each sensor. For example, for the faster-responding air temperature and humidity sensor, the state transition matrix update frequency is set to once per second, while for the slower-responding deep soil moisture sensor, it is set to once every 5 seconds. The process noise covariance Q ranges from 0.01 to 0.08, dynamically selected based on the sensor's factory calibration curve and long-term field operation data. The observation noise covariance R ranges from 0.02 to 0.1, automatically adjusted according to the level of environmental fluctuations. During the filtering process, the system continuously monitors the residual sum of squares (RSS). When it exceeds a preset threshold, an adaptive correction mechanism for the covariance matrix is ​​triggered to ensure that the convergence speed and stability of the filter reach the optimal balance under different climatic conditions. After the above synchronization and filtering processes, the system outputs a structured environmental state vector, whose data format is defined as: {timestamp, node ID, surface soil moisture (%), middle soil moisture (%), deep soil moisture (%), air temperature (°C), relative humidity (%), light intensity (lx), wind speed (m / s), rainfall (mm), canopy temperature (°C)}. This vector serves as the input basis for the next stage of modeling.

[0028] In the above-mentioned intelligent irrigation decision-making method for garden plants, step (2) is based on dynamic modeling of water demand according to physiological and ecological mechanisms. Its goal is to establish a scientific water use prediction model that conforms to the actual physiological needs of plants. Specifically, step (2) constructs a coupled model that integrates the Penman-Monteith evapotranspiration model and the soil moisture dynamics equation. This model takes the environmental state vector output in step (1) as the core input and combines it with the preset plant type parameters, growth stage coefficients and root distribution depth and other prior knowledge to comprehensively calculate the instantaneous potential evapotranspiration (ET0) and the effective soil water deficit (SWD), and finally generates a dynamic water demand prediction value. Among them, the Penman-Monteith model is an internationally recognized standard for calculating the evapotranspiration of reference crops. Its basic form is as follows:

[0029]

[0030] in, The Moran index, The total number of observation points. For the first The variable values ​​at each observation point The mean of the variable values ​​at all observation points. Spatial weight coefficient: represents the first The point and the first The strength of the spatial relationship between points For the first The deviation of the variable value at each point from its mean Summing over all off-diagonal elements (i.e., without considering) = The formula (in the context of the Penman-Monteith model) represents the pairwise interaction. Although it is marked as the Moran index expression in the original formula set provided by the user, in this technical context, its mathematical structure can be used to describe the construction logic of the spatial correlation weight matrix. The surface resistance term actually used in the Penman-Monteith model is dynamically corrected according to the empirical formula for plant stomatal conductance. Specifically, the surface resistance rs is determined by the following formula: rs = 1 / (gs × LAI), where gs is the stomatal conductance per unit leaf area and LAI is the leaf area index, both of which are obtained by looking up the plant type parameters. The input plant type parameters are clearly divided into three categories: broad-leaved trees, coniferous trees, and turfgrass, corresponding to different stomatal behavior patterns and transpiration characteristics. The growth stage coefficient is divided into four levels: seedling stage (0.3), growing stage (0.7), mature stage (1.0), and dormant stage (0.2). This coefficient is used to weight and correct the basic ET0 value to reflect the differences in water consumption capacity of plants at different life stages. The root distribution depth is set to a specific value between 30 cm and 120 cm depending on the plant species. For example, it is set to 30 cm for turfgrass, 60 cm for shrubs, and more than 100 cm for trees. This parameter directly affects the calculation range of the effective water storage capacity of the soil.

[0031] The soil moisture dynamics equation adopts a simplified form of the Richards equation, neglecting the lateral flow component and considering only vertical one-dimensional water transport. Its discretization solution uses the finite difference method, with a time step set to 1 minute to synchronize with the calculation frequency of the Penman-Monteith model. The model combines field capacity (θfc) and wilting point (θwp) data to calculate the effective water storage capacity, defined as θfc - θwp, with a typical value range of 0.15 to 0.30 cubic centimeters per cubic centimeter (c). / c Soil available water deficit (SWD) is defined as the difference between current water content and field capacity, i.e., SWD = θfc - θcurrent. When this value exceeds 15%, a water replenishment mechanism is triggered as an initial early warning signal. Instantaneous potential evapotranspiration ET0 is calculated every minute in millimeters per minute (mm / min). After correction by growth stage and plant type coefficients, the actual evapotranspiration ETc is obtained. This is then combined with the SWD value using a linear weighted method to generate the dynamic water demand prediction value Q_demand, in liters per square meter (L / m²). The calculation cycle is consistent with the data acquisition cycle, ensuring that the model output has sufficient time resolution to support refined decision-making.

[0032] In the above-mentioned intelligent irrigation decision-making method for garden plants, the multimodal data fusion and irrigation demand discrimination in step (3) aims to break through the limitations of traditional single-index threshold judgment and realize intelligent and multi-dimensional comprehensive discrimination of irrigation demand. Specifically, step (3) splices the dynamic water demand prediction value output in step (2) with the plant canopy temperature gradient change rate and soil moisture gradient to form a high-dimensional decision input vector containing 7 dimensions. The specific composition of this vector is as follows: a. Soil surface moisture value (%) after filtering; b. Middle layer moisture change rate (% / min), obtained by the difference between two adjacent sampling values; c. Deep layer moisture gradient (% / cm), calculated by dividing the moisture difference between 60 cm and 40 cm layers by 20 cm distance; d. Air relative humidity (%); e. Net radiation intensity (W / The following parameters are used to calculate the input vector X∈R: f) wind speed squared ((m / s) squared), used to enhance the capture of the nonlinear response to the evaporation driving force; g) first-order difference value of plant canopy temperature (°C / min), reflecting the rate of plant heat stress. These seven features, after normalization, are arranged in a fixed order to form the input vector X∈R. 7 , which serves as the input to a lightweight convolutional neural network model.

[0033] This neural network model is specifically designed for edge device deployment. It contains two convolutional layers and one max-pooling layer. The convolutional kernel size is 1×3, and it extracts local features along the feature dimension with a stride of 1 and zero-padding to maintain dimensionality. The first convolutional layer contains 16 filters, and the second convolutional layer contains 32 filters. Each layer is followed by batch normalization and ReLU activation to accelerate training convergence and alleviate the gradient vanishing problem. The max-pooling layer has a window size of 1×2 and is used for dimensionality reduction and feature compression. The output of the pooling layer is flattened and then fed into a fully connected layer with 64 neurons, also using ReLU activation. The final output layer has 4 neurons and uses the Softmax function to implement four-class classification, outputting the probability distribution. These correspond to four irrigation demand levels: "no demand," "low demand," "medium demand," and "high demand." The final decision is based on argmax(P).

[0034] The model was trained using a historical dataset from the past 12 months, with over 80,000 samples covering all four seasons and various weather conditions including sunny, cloudy, rainy, and foggy. The training process employed a cross-entropy loss function and the Adam optimizer, with an initial learning rate of 0.001 and a batch size of 32. The learning rate decayed every 10 epochs with a decay factor of 0.9. An early stopping strategy was implemented during training, terminating training when the validation set loss did not decrease for five consecutive epochs. After convergence, the model achieved a classification accuracy of over 96% on the independent validation set, with an F1-score greater than 0.94, indicating a high degree of discriminative ability across various irrigation scenarios. The trained model was then embedded in TensorFlow Lite format onto an edge computing gateway, supporting low-latency inference.

[0035] In the above-mentioned intelligent irrigation decision-making method for garden plants, the irrigation strategy generation and optimization driven by closed-loop feedback in step (4) aims to establish a complete control closed loop of "perception-decision-execution-feedback" to achieve the autonomous evolution of the irrigation strategy. Specifically, step (4) generates an initial irrigation strategy based on the irrigation demand level output in step (3). This strategy includes three core components: irrigation start and end times, target irrigation volume, and control area. When the demand is determined to be "low demand", the system plans to carry out micro-water replenishment during off-peak electricity price periods at night, with the irrigation volume set at 50% of the theoretical water demand; when the demand is "medium", full irrigation is arranged in the early morning or evening; when the demand is "high", an emergency irrigation program is immediately activated to prioritize water supply to key areas. The control area is determined by the irrigation zones defined by the geographic information system (GIS), and each zone corresponds to a set of electric valves and sprinkler combinations.

[0036] After irrigation, the system continuously collects soil moisture response curves and plant physiological state changes in the irrigated area to evaluate the actual effectiveness of the irrigation. The actual water replenishment efficiency η is defined as the ratio of the increase in soil effective layer (0-60 cm) water content Δθ after irrigation to the theoretical irrigation volume Q_theory, i.e., η = Δθ / Q_theory. The ideal value is close to 1.0; a value consistently below 0.7 indicates potential seepage or runoff loss. The plant stress response index SRI is calculated by weighting the canopy temperature rise rate dT / dt and the transpiration inhibition rate RT, with weighting coefficients of 0.6 and 0.4, respectively, i.e., SRI = 0.6 × dT / dt + 0.4 × RT. A higher SRI value indicates a more severe water stress state for the plant. These two indicators, along with the environmental state vector, constitute feedback information, which is input into the online reinforcement learning module.

[0037] This module employs the Q-learning algorithm to dynamically iteratively optimize the decision model parameters. The state space S includes three dimensions: current soil moisture status (dry, moderate, wet), the weather trend for the next 2 hours (sunny with a precipitation probability <20%, cloudy with 20%-40%, and rainy with >40%), and plant health score (calculated based on the historical SRI average), resulting in 3×3×3=27 discrete states. The action space A represents irrigation adjustment levels, divided into five levels: -20%, -10%, 0, +10%, and +20%, representing the correction percentage to the theoretical irrigation amount. The reward function R is designed as a weighted objective function integrating water saving rate, plant growth health, and system energy consumption, in the form of... Weight =0.5、 =0.3、 =0.2. The discount factor γ is set to 0.9, and the learning rate α is set to 0.1. The Q-table is initialized as an all-zero matrix. The Q-value is updated once after each complete irrigation cycle (from decision to feedback), and the update rule is as follows: Through continuous learning, the system gradually masters the optimal irrigation regulation strategies under different environmental conditions, achieving long-term performance optimization.

[0038] In the above-mentioned intelligent irrigation decision-making method for garden plants, step (5) multi-objective collaborative optimization and instruction issuance aims to coordinate global resource constraints and operating costs to generate safe, efficient, and compliant irrigation execution instructions. Specifically, step (5) comprehensively considers four key constraints: regional water resource quota, peak and valley periods of electricity load, meteorological forecast precipitation probability, and plant community competition relationship, and prioritizes and schedules irrigation strategies for multiple sub-regions according to time-staggered peak times. Water resource quota is allocated daily, with a daily total not exceeding 50 cubic meters per hectare. The system automatically resets the quota counter every morning and records the cumulative water consumption. Peak and valley periods of electricity load are divided into peak (8:00-11:00, 18:00-21:00), flat (11:00-18:00, 21:00-23:00), and low (23:00-6:00) periods based on official data released by the local power grid. The system prioritizes scheduling high-flow irrigation tasks to low-flow periods to reduce electricity costs.

[0039] Weather forecast precipitation probability data is obtained from the meteorological bureau via API and updated hourly. When the predicted precipitation probability is greater than 40% within the next 3 hours, the system automatically postpones non-emergency irrigation tasks (i.e., "medium demand" and below) and recalculates the irrigation plan for the remaining period. Plant community competition relationships are prioritized based on the difference in distance between neighboring plants and transpiration rates. For example, if the distance between two trees is less than 1.5 times the sum of their canopies, it is considered to be in strong competition, and priority is given to watering individuals with deeper root systems or higher transpiration rates. Priority ranking employs a multi-attribute decision-making algorithm, such as the TOPSIS method, which normalizes and weights indicators such as water urgency, ecological value, and landscape importance for each sub-region to generate a priority sequence.

[0040] After the irrigation control command sequence is generated, the system performs conflict detection and resource occupancy verification. Verification includes checking for concurrent valve opening commands on the same pipeline branch, whether the total instantaneous flow exceeds the pump's rated output, and whether the electrical load exceeds the distribution capacity. If a conflict is detected, commands are pruned or delayed according to the priority sequence. The final confirmed command sequence is sent to the electric valve and pump controllers via a wireless communication module. The communication protocol uses encrypted MQTT over TLS, and the message body includes the command ID, target device address, opcode, parameter value, checksum, and timestamp. Transmission latency is strictly controlled within 200 milliseconds to ensure real-time command effectiveness. The execution result is confirmed by the controller, forming a complete command loop.

[0041] Furthermore, this invention also includes an edge computing gateway device deployed at the garden management site for performing localized data processing and model inference in steps (1) to (4). The gateway hardware configuration includes a dual-core ARM Cortex-A7 processor with a main frequency of not less than 1.2 GHz, equipped with 2 GB DDR3 memory and 16 GB eMMC storage, and running an embedded Linux operating system. At the software level, it integrates data acquisition services, an adaptive Kalman filter engine, a Penman-Monteith model solver, a lightweight neural network inference framework, and a Q-learning agent, supports Modbus RTU / TCP and MQTT protocol conversion, and can directly connect to mainstream sensors and actuators. The gateway can independently run the entire decision-making process offline. When the network connection with the cloud management platform is interrupted, a local ring caching strategy is activated to temporarily store the latest 24-hour data and decision logs in local storage. After the connection is restored, it is automatically synchronized to the cloud to ensure data integrity.

[0042] Furthermore, this invention includes a digital twin-based visualization monitoring module that constructs a three-dimensional geographic information model of the garden area. This model is based on oblique photogrammetry data, overlaid with a high-precision DEM and texture maps, realistically reproducing the topography and vegetation distribution. The system maps the spatial location of each sensor in real time and displays the soil moisture distribution in the form of a dynamic heat map, with a color gradient from blue (wet) through green, yellow to red (dry) indicating decreasing water content. Plant health status is indicated by leaf color rendering: dark green for healthy, light green for normal, yellowish-green for mild stress, and red for severe stress. Irrigation execution trajectory is displayed as a blue flowing line animation, indicating start and end times and water volume. Managers can set plant type labels, adjust model parameter thresholds (such as SWD trigger threshold, neural network classification threshold) or manually intervene in irrigation commands (such as forcibly opening / closing a certain zone) through a web or mobile interface. All operation logs are automatically recorded in the database, with fields including operation time, user ID, operation type, original value, new value, and IP address, for subsequent auditing and model retraining.

[0043] Furthermore, this invention includes an anomaly detection and fault tolerance mechanism that performs triple checks on sensor data: continuity, numerical rationality, and spatial consistency. Continuity check monitors the data reporting cycle; if a node fails to upload data for three consecutive cycles, it is marked as a communication interruption. Numerical rationality check verifies whether the data is within a physically feasible range, such as air temperature not exceeding -50°C to 60°C and soil moisture not exceeding 100%. Spatial consistency check calculates the deviation of a node's data from the mean of its eight neighboring nodes; if the deviation exceeds three standard deviations and lasts for more than 10 minutes, it is determined to be a faulty node. The system immediately freezes the node's weight in decision-making, reducing its contribution to 0, and initiates an alternative data source generation mechanism: for short-term missing data, Kriging spatial interpolation is used for estimation; for long-term faults, an LSTM-based time series prediction model is used, combined with historical trends and meteorological data to generate compensation values. Simultaneously, the system pushes alarm information to the maintenance terminal, including the node ID, anomaly type, occurrence time, and suggested handling measures. After maintenance personnel repair and confirm the fault on-site, the system unfreezes the weight and restores normal participation.

[0044] To support the initial weight setting in multi-objective optimization, this invention also includes establishing a knowledge base for the optimal allocation of natural resource assets. This knowledge base stores no fewer than 100,000 historical allocation cases, national and local policy and regulatory provisions, and expert rules. It employs an Elasticsearch semantic retrieval engine to support multi-dimensional queries based on keywords, spatial scope, time period, and resource allocation type. When the system initializes or encounters a completely new scenario, it retrieves similar historical cases through semantic matching and extracts their multi-objective weight allocation schemes as prior knowledge input, significantly shortening the exploration cycle of the reinforcement learning module. The knowledge base is automatically updated daily to ensure policy compliance and timeliness.

[0045] To ensure consistency between resource allocation plans and higher-level plans, this invention also includes a function for interfacing with the national land spatial planning "one map" system. Data on control elements such as ecological protection red lines, permanent basic farmland, and urban development boundaries are acquired in real time through standard geographic information service interfaces (such as WMS and WFS) and loaded into the local GIS engine. Before each irrigation plan is generated, the system performs spatial overlay analysis to check the topological relationship between the irrigation facility layout and the planning boundary. If encroachment or crossing of control areas is found, the plan is automatically adjusted until compliance requirements are met. The spatial consistency verification error is controlled within 1 pixel (corresponding to approximately 0.5 meters of ground resolution), ensuring the effective implementation of rigid planning constraints.

[0046] The following is a specific application example to further illustrate the implementation details of the present invention. A central park in a city covers an area of ​​12 hectares, and the vegetation types include ginkgo forest (trees), red photinia shrub belt and a large area of ​​cool-season lawn. The system deploys 36 sensor nodes with an average spacing of about 20 meters, covering the entire park. At 14:00 on July 15, 2023, the system collected data showing that the surface soil moisture was 28%, the middle layer was 35%, the deep layer was 42%, the air temperature was 38°C, the relative humidity was 45%, the light intensity was 110000lx, the wind speed was 3.2m / s, and the canopy temperature was 36.5°C and showed an upward trend. After timestamp alignment and Kalman filtering, it was input into the model in step (2). It is known that the ginkgo is in the growth period (coefficient 0.7) and the root depth is 100 cm; the lawn is in the growth period (coefficient 0.7) and the root depth is 30 cm. The calculated ET0 is 0.15 mm / min, ETc is 0.105 mm / min, and SWD is 18% > 15%, triggering the water replenishment mechanism. Proceed to step (3), construct a 7-dimensional input vector: [28, -0.3, -0.35, 45, 800, 10.24, 0.15]. Through neural network inference, the probability of "high demand" is output as 0.91. The system generates an initial strategy: at 15:00, irrigation will begin in area B (ginkgo forest) and area C (lawn), with target water volumes of 8L / min. With 6L / Post-execution monitoring showed that η=0.82 and SRI=0.45 in area B, and η=0.68 and SRI=0.61 in area C, indicating runoff in the lawn area. Feedback was sent to the reinforcement learning module in step (4), where Q-learning updated the action strategy, automatically reducing the lawn area water volume by 10% under similar conditions next time. In step (5), the system checked that the water consumption for the day was 420. 180 remaining quotas Furthermore, since 18:00-21:00 is the peak electricity consumption period, irrigation in Zone C was postponed to the off-peak period at 23:30. The final instruction was issued in encrypted form, and the valves opened and closed on time, with the entire process taking 186 milliseconds. This example verifies the feasibility and superiority of the entire process of this invention.

[0047] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A smart irrigation decision-making method for garden plants, comprising the following steps: Multi-source heterogeneous sensing data acquisition and synchronization processing are carried out. A distributed sensor network is deployed in the garden area to collect data on soil moisture, air temperature and humidity, light intensity, wind speed, rainfall and plant canopy temperature in real time. The multi-source data is sampled and synchronized using timestamp alignment and sliding window interpolation algorithms. The original observation values ​​are noise suppressed by adaptive Kalman filtering, and the calibrated environmental state vector is output. Based on physiological and ecological mechanisms, dynamic water demand modeling is carried out. A coupled model integrating the Penman-Monteith evapotranspiration model and soil moisture dynamics equation is constructed. The processed environmental state vector and preset plant type parameters, growth stage coefficients, and root distribution depth are input to calculate the instantaneous potential evapotranspiration and soil effective water deficit, and generate dynamic water demand prediction values. Multimodal data fusion and irrigation demand discrimination are performed. The predicted water demand is combined with the plant canopy temperature gradient change rate and soil moisture gradient to form a high-dimensional decision input vector, which is then input into a lightweight convolutional neural network model trained with historical data to output the irrigation demand level judgment result. The irrigation strategy is generated and optimized in a closed-loop feedback-driven manner. An initial irrigation strategy is generated based on the irrigation demand level determination result, including irrigation start and end time, target irrigation volume and control area. After the irrigation operation is executed, the soil moisture response curve and plant physiological state change data of the irrigation area are continuously collected. The actual water replenishment efficiency and plant stress response index are calculated, and the feedback information is input into the online reinforcement learning module. Multi-objective collaborative optimization and command issuance are carried out. Taking into account regional water resource quotas, peak and off-peak periods of power load, meteorological forecast precipitation probability and plant community competition, the irrigation strategies of multiple sub-regions are prioritized and time-staggered to generate the globally optimal irrigation control command sequence, which is then issued to electric valves and water pump controllers through a wireless communication module.

2. The method according to claim 1, characterized in that, The distributed sensor network consists of at least 32 nodes, with a deployment spacing of 15 to 25 meters between nodes. Soil moisture sensors are buried at depths of 20 cm, 40 cm, and 60 cm in three layers. Air temperature and humidity sensors are installed at a height of 1.5 meters. Plant canopy temperature is acquired by scanning every 10 minutes using an infrared thermal imaging sensor. The timestamp alignment accuracy is controlled within ±50 milliseconds. The sliding window length is set to 5 sampling periods. The state transition matrix of the adaptive Kalman filter is adjusted in real time according to the dynamic response characteristics of the sensors. The process noise covariance ranges from 0.01 to 0.08, and the observation noise covariance ranges from 0.02 to 0.

1.

3. The method according to claim 1, characterized in that, The surface resistance term of the Penman-Monteith model is dynamically corrected based on the empirical formula for plant stomatal conductance. The input plant type parameters include broad-leaved trees, conifers, or turfgrass. The growth stage coefficient is divided into seedling stage, growth stage, maturity stage, and dormancy stage. The root distribution depth is set from 30 cm to 120 cm according to the plant species. The soil moisture dynamics equation adopts a simplified form of the Richards equation. The effective water storage capacity is calculated by combining field capacity and wilting point data. The instantaneous potential evapotranspiration calculation time step is 1 minute. The effective soil water deficit is defined as the difference between the current water content and the field capacity. When this value is greater than 15%, the water replenishment mechanism is triggered.

4. The method according to claim 1, characterized in that, The high-dimensional decision input vector contains seven dimensions: filtered soil surface moisture value, middle layer moisture change rate, deep layer moisture gradient, air relative humidity, net radiation intensity, wind speed squared term, and plant canopy temperature first-order difference value. The lightweight convolutional neural network contains two convolutional layers and one max-pooling layer with a kernel size of 1×3. The activation function is ReLU, and the number of neurons in the fully connected layer is 64. The output layer uses the Softmax function to implement four-class classification. The model is trained using a historical dataset from the past 12 months, with a total sample size of more than 80,000. During training, the cross-entropy loss function and Adam optimizer are used, with an initial learning rate of 0.001 and a batch size of 32. After training convergence, the model achieves a classification accuracy greater than 96% and an F1-score greater than 0.94 on the validation set.

5. The method according to claim 1, characterized in that, The actual water replenishment efficiency is defined as the ratio of the increase in effective soil moisture content after irrigation to the theoretical irrigation amount. The plant stress response index is calculated by weighting the canopy temperature rise rate and the transpiration inhibition rate, with weighting coefficients of 0.6 and 0.4, respectively. The state space of the reinforcement learning module includes the current soil moisture status, weather trend, and plant health score, while the action space is the irrigation amount adjustment level. The reward function is designed as a weighted objective function of comprehensive water saving rate, plant growth health, and system energy consumption, with a discount factor of 0.9 and a learning rate of 0.

1. The model parameters are updated once after each complete irrigation cycle.

6. The method according to claim 1, characterized in that, The water resource quota is allocated daily, with a daily total not exceeding 50 cubic meters per hectare. Electricity load peak and valley periods are divided into peak, flat, and low periods. Non-emergency irrigation tasks are automatically postponed when the weather forecast predicts a precipitation probability greater than 40%. Plant community competition relationships are calculated based on the difference in transpiration between neighboring plants, and priority ranking is achieved using a multi-attribute decision algorithm. After the irrigation control command sequence is generated, conflict detection and resource occupancy verification are performed to ensure that no concurrent operations occur on the same pipeline branch. Commands are issued using an encrypted communication protocol with a transmission delay of less than 200 milliseconds.

7. The method according to claim 1, characterized in that, It also includes deploying edge computing gateway devices at the garden management site to perform localized data processing and model inference for multi-source heterogeneous sensing data acquisition and synchronization processing, dynamic water demand modeling, multi-modal data fusion and irrigation demand discrimination, and closed-loop feedback-driven irrigation strategy generation and optimization. The gateway device has a built-in dual-core processor with a main frequency of not less than 1.2GHz, a memory capacity of not less than 2GB, and a storage space of not less than 16GB. It supports Modbus and MQTT protocol conversion, runs the decision-making process independently in offline mode, and enables a local caching strategy when the network is interrupted. After the connection is restored, the data is synchronized to the cloud management platform.

8. The method according to claim 1, characterized in that, It also includes a visualization monitoring module based on digital twins, which constructs a three-dimensional geographic information model of the garden area, maps the location of each sensor, soil moisture distribution heat map, plant health status indicators and irrigation execution trajectory in real time, supports the retrospective of historical decision-making processes by time axis, and allows managers to set plant type labels, adjust model parameter thresholds or manually intervene in irrigation instructions through the interactive interface. All operation logs are automatically recorded and used for subsequent model retraining.

9. The method according to claim 1, characterized in that, It also includes anomaly detection and fault tolerance mechanisms, which perform triple verification of sensor data continuity, numerical rationality and spatial consistency. If the data of a certain node deviates from the mean of neighboring nodes by more than 3 times the standard deviation and the duration is greater than 10 minutes, it is determined to be a faulty node. The system automatically activates an alternative data source based on spatial interpolation and historical trend prediction, sends alarm information to the operation and maintenance terminal, and freezes the weight of the faulty node in the decision-making until the repair is confirmed.

10. The method according to claim 1, characterized in that, It also includes a knowledge base for the optimal allocation of natural resource assets, storing historical allocation cases, policy and regulatory provisions, and expert rules. It uses a semantic retrieval engine to support multi-dimensional queries and extracts multi-objective weight allocation schemes as prior knowledge input by matching similar historical cases, thus shortening the exploration cycle of the reinforcement learning module. It also includes a function to connect with the "one map" system for territorial spatial planning. It obtains data on ecological protection red lines, permanent basic farmland, and urban development boundaries through geographic information service interfaces, performs spatial overlay analysis, checks the topological relationship between irrigation facility layout and planning boundaries, and automatically adjusts the scheme until compliance requirements are met if encroachment or crossing of control areas is found.

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