Intelligent agricultural water-saving irrigation system and control method

Abstract

The invention discloses an intelligent agricultural water-saving irrigation system and a control method, and relates to the technical field of agricultural irrigation. Comprising the following steps: a land area planning module performs equal-area division on a farmland, sets a detection node array, and monitors water retention conditions of different soil depths; the moisture gradient characterization module calculates the vertical moisture gradient and the direction of the vertical moisture gradient of the evaluation area in real time through edge calculation. And the irrigation water amount correction module fuses and generates a target water-holding capacity based on the leaf stomatal conductance signal and the moisture gradient. The irrigation circulation termination control module uses pulse type infiltrating irrigation, and sets double termination conditions of the water-holding capacity change rate and the permeability. The closed-loop feedback adjustment module detects the soil water content after irrigation, characterizes the water leakage risk and feeds back and adjusts the irrigation water amount, closed-loop control is formed, and effective utilization of water resources is ensured.

Description

Technical Field

[0001] This invention relates to the field of agricultural irrigation technology, specifically to a smart agricultural water-saving irrigation system and control method. Background Technology

[0002] In modern agricultural production, the efficient use of water resources is a crucial issue that urgently needs to be addressed. With the increasing severity of global climate change and water scarcity, traditional irrigation methods often fail to meet the water requirements of crops at different growth stages, leading to water waste and uneven crop growth. Therefore, developing an efficient and intelligent water-saving irrigation system has become an important direction for improving agricultural productivity and sustainable development. Currently, many agricultural irrigation systems still employ fixed irrigation strategies, lacking the ability to dynamically adjust based on changes in soil moisture and crop growth status.

[0003] Most existing technologies focus on simple soil moisture monitoring, lacking in-depth analysis and real-time feedback of soil moisture gradients. Especially in multi-layered soils, water distribution is uneven, with significant differences between deep root zone and surface moisture conditions. Water is often not fully utilized, leading to runoff in deeper soil layers and wasting water resources. Traditional irrigation systems struggle to effectively address these situations. Furthermore, many systems fail to respond promptly to changes in plant needs under water stress, resulting in impaired crop growth and impacting yield and quality.

[0004] In the prior art, CN118252094A discloses a rapid irrigation device for agricultural planting, including: an irrigation area division module, an irrigation data acquisition module, a water pump anomaly monitoring module, a farmland infiltration calculation module, a crop growth detection module, an outlier screening module, an optimal water volume determination module, and a water-saving system adjustment module. It calculates the water pump operation stability coefficient through a water pump monitoring mathematical model, monitors and adjusts the water pump operation status in real time, and deletes abnormal sub-region values ​​based on the farmland infiltration coefficient and crop growth effect index of each monitored sub-region. It then selects the farmland infiltration coefficient corresponding to the sub-region with the highest crop growth effect index to determine the optimal water volume irrigation range. However, this scheme does not consider water loss and crop dynamic respiration; it only determines the optimal water volume irrigation range based on the farmland infiltration coefficient and crop growth effect index. While this can meet crop growth needs, it has poor precision control of water resources, thus reducing the accuracy and effectiveness of the irrigation device.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a smart agricultural water-saving irrigation system and control method to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A smart agricultural water-saving irrigation system, specifically comprising:

[0009] The land area planning module is used to divide the farmland to be irrigated into equal areas, determine several assessment areas, and set up a detection node array in each assessment area. Each node is equipped with several detection units in the vertical direction, which are located at different soil depth ranges.

[0010] The moisture gradient characterization module is used to collect and upload soil water holding data through the detection unit. The edge computing gateway receives soil water holding data from each node. Based on the difference in soil water holding at the first soil depth range and the final soil depth range and the vertical distance between them, it calculates the vertical moisture gradient value of each evaluation area in real time and determines the gradient direction.

[0011] The irrigation water volume correction module is used to randomly sample plants in each assessment area, collect their leaf stomatal conductance physical signals, calculate water stress response factors based on the leaf stomatal conductance physical signals, and couple the water stress response factors, the real-time vertical water gradient value of the assessment area and the soil texture correction coefficient to generate the target water holding capacity through weighted fusion.

[0012] The irrigation cycle termination control module is used to control pulsed seepage irrigation based on the target water holding capacity. When the real-time soil water holding capacity at the first soil depth range is lower than the target water holding capacity requirement, the pulsed irrigation cycle is triggered. At the same time, dual termination conditions of minimum water holding capacity change rate and maximum permeability are set. When one of the termination conditions is met, the irrigation cycle is terminated.

[0013] The closed-loop feedback adjustment module is used to detect the soil moisture content at the final soil depth range after the irrigation cycle is terminated, set a detection time window, and characterize the leakage risk by the change in soil water holding capacity within adjacent detection time windows. At the same time, it adjusts the single irrigation water volume in the irrigation cycle based on the leakage risk, forming a closed-loop feedback water-saving irrigation control.

[0014] Furthermore, each node is equipped with several detection units in the vertical direction, which are positioned at different soil depth ranges, specifically including three soil depth ranges: the depth range of the main water absorption layer of the crop roots, the depth range of the water transport transition layer, and the depth range of the leakage risk monitoring layer. The depth range of the main water absorption layer of the crop roots is denoted as the first soil depth range, the depth range of the water transport transition layer is denoted as the second soil depth range, and the depth range of the leakage risk monitoring layer is denoted as the final soil depth range.

[0015] The irrigated farmland area is divided into equal areas to determine several evaluation areas. The specific method for determining the evaluation areas is as follows: obtain the boundary information of the farmland area and draw the boundary of the farmland. According to the set area, the farmland area is divided into several rectangular or square grids of equal size, and each grid area is used as an evaluation area.

[0016] Each node is equipped with several detection units in the vertical direction, wherein the detection units are used to detect the soil water holding capacity.

[0017] Furthermore, based on the difference in soil water holding capacity within the first soil depth range and the final soil depth range, as well as the vertical distance between them, the vertical moisture gradient value of each node is calculated in real time. First, the soil water holding capacity of each depth range is determined, specifically by using the average soil water holding capacity output by the detection unit within that depth range as the soil water holding capacity for that depth range. The formula used to calculate the vertical moisture gradient value based on the soil water holding capacity of the depth range is as follows:

[0018]

[0019] In the formula, Let θ be the vertical moisture gradient of the i-th evaluation region at time t. xi (t) represents the soil water holding capacity at the first soil depth in the i-th assessment region, θ mi (t) represents the soil water holding capacity at the final soil depth range in the i-th assessment region, L zi L represents the maximum vertical depth of the final soil depth range in the i-th assessment region. xi Let t be the maximum vertical depth of the first soil depth range in the i-th assessment region, where t is the time variable within the water holding capacity detection period, and i is the index of the assessment region.

[0020] In the i-th assessment region, the soil water holding capacity θ at the first soil depth range xi The formula used for the calculation is:

[0021]

[0022] In the formula, This represents the soil water holding capacity of the j-th detection unit within the first soil depth range in the i-th evaluation region at time t, where j is the index of the detection unit within the first soil depth range, n is the total number of detection units within the first soil depth range, j∈[1,n], and the number of detection units set within the same soil depth range is the same in different evaluation regions;

[0023] The logic for determining the gradient direction is as follows: a positive value indicates that water is moving downwards, and a negative value indicates that water is moving upwards.

[0024] Furthermore, the stomatal conductance physical signal of the leaves is collected. Specifically, the stomatal conductance physical signal of the leaves refers to the stomatal conductance potential difference of the leaves. The stomatal conductance potential difference of the leaves is collected by using interdigitated gold electrodes, setting the distance between the electrodes, and attaching them to both sides of the main vein on the back of the crop leaves.

[0025] The water stress response factor is calculated based on the physical signal of the leaf stomatal conductance, and the specific formula used to calculate the water stress response factor is as follows:

[0026]

[0027] In the formula, SP i (t) represents the water stress response factor of the crop in the i-th assessment region at time t, ΔV it Let be the stomatal conductance potential difference of crop leaves in the i-th evaluation region at time t compared to the previous time. denoted by , where k represents the rate of change of stomatal conductance potential difference of crop leaves in the i-th evaluation region, and k is a proportionality constant.

[0028] Furthermore, the water stress response factor, the real-time vertical moisture gradient value of the assessment area, and the soil texture correction coefficient are coupled, wherein the soil texture correction coefficient is specifically determined according to the soil type in the assessment area. A target water holding capacity is generated through weighted fusion, and the specific formula used to calculate the target water holding capacity is as follows:

[0029]

[0030] In the formula, ECI i (t) represents the target water holding capacity of the i-th assessment area at time t, HW 0i Let α be the initial target water holding capacity for the i-th assessment area, α be the soil texture correction coefficient, and ω1 and ω2 be the weighting coefficients of the vertical water gradient and the water stress response factor, respectively, where ω1≤ω2 and both ω1 and ω2 are greater than 0.

[0031] Furthermore, based on the target water holding capacity, pulsed seepage irrigation control is adopted. This pulsed seepage irrigation control specifically includes irrigation periods and intermittent periods. When the real-time soil water holding capacity at the first soil depth range is lower than the target water holding capacity requirement, the controller triggers a pulsed irrigation cycle, opens the solenoid valve to execute the irrigation period, and closes the valve to enter the intermittent period. During the intermittent period, the actual water holding capacity at the first soil depth range within the i-th evaluation area is monitored in real time. If the actual water holding capacity at the first soil depth range monitored during the intermittent period is lower than or equal to ECI... i If (t)*0.7, then the intermittent period ends and the next irrigation cycle begins;

[0032] The irrigation cycle is terminated when either the minimum water holding capacity change rate or the maximum permeability is met. Specifically, several time windows are set to detect the change rate of the actual water holding capacity in the first soil depth range within adjacent time windows. If the change rate is less than the set minimum water holding capacity change rate, the irrigation cycle is terminated.

[0033] Meanwhile, the maximum permeability termination condition specifically refers to: when the actual water holding capacity of the final soil depth range increases more than the set maximum permeability within an adjacent time window, the irrigation cycle is terminated; wherein the duration of the time window is less than the duration of the intermittent period.

[0034] Furthermore, the risk of leakage is characterized by changes in soil water holding capacity, and the formula used to calculate the risk of leakage is as follows:

[0035]

[0036] In the formula, R leaki For the leakage risk of the i-th assessment area, θ mq i (r+s) represents the soil water holding capacity output by the q-th detection unit within the final soil depth range of the i-th evaluation region at time r+s, and θ mq i (r) represents the soil water holding capacity output by the q-th detection unit within the final soil depth range of the i-th evaluation region at time r, and s represents the time interval between adjacent detection time windows. i Let r be the area of ​​the i-th assessment region, where r is the time variable of the leakage risk detection process after the irrigation cycle is terminated, and q is the index of the detection unit in the final soil depth range within the i-th assessment region, q∈[1,Q], where Q is the total number of detection units in the final soil depth range within the i-th assessment region;

[0037] Simultaneously, based on the risk of leakage, the amount of water used for each irrigation cycle is adjusted accordingly. The formula used to calculate the adjusted amount of water used for each irrigation cycle is as follows:

[0038]

[0039] In the formula, wl zi For the i-th assessment area, the adjusted single irrigation water volume is wl. i0 ω3 is the initial single irrigation water volume for the i-th evaluation area, and ω3 is the correction ratio coefficient, which is greater than 0.

[0040] This invention also provides a smart agricultural water-saving irrigation control method, which is used to control the aforementioned smart agricultural water-saving irrigation system, comprising:

[0041] The irrigated farmland area is divided into equal areas to determine several assessment areas. In each assessment area, a detection node array is set up. Each node is equipped with several detection units in the vertical direction and is located at different soil depths.

[0042] Soil water holding data is collected and uploaded by the detection unit. The edge computing gateway receives soil water holding data from each node. Based on the difference in soil water holding capacity between the first soil depth range and the final soil depth range and the vertical distance between them, the vertical water gradient value of each evaluation area is calculated in real time, and the gradient direction is determined.

[0043] Randomly sample plants within each assessment area and collect their leaf stomatal conductance physical signals. Calculate water stress response factors based on these signals. Couple the water stress response factors with the real-time vertical water gradient value of the assessment area and the soil texture correction coefficient, and generate the target water holding capacity through weighted fusion.

[0044] Based on the target water holding capacity, pulsed seepage irrigation control is adopted. When the real-time soil water holding capacity at the first soil depth range is lower than the target water holding capacity requirement, the pulsed irrigation cycle is triggered. At the same time, dual termination conditions of minimum water holding capacity change rate and maximum permeability are set. When one of the termination conditions is met, the irrigation cycle is terminated.

[0045] After the irrigation cycle is terminated, the soil moisture content at the final soil depth is measured, and a measurement time window is set. Within adjacent measurement time windows, the risk of leakage is characterized by changes in soil water holding capacity. At the same time, the amount of water used for each irrigation cycle is adjusted based on the risk of leakage, forming a closed-loop feedback water-saving irrigation control.

[0046] Compared with the prior art, the beneficial effects of the present invention are:

[0047] First, through the equal-area division and detection node array deployment of the land area planning module, the system can achieve comprehensive monitoring of different assessment areas, making soil moisture data collection more accurate. Second, the introduction of the moisture gradient characterization module enables the system to calculate moisture gradient values ​​and determine gradient directions in real time. This allows for the timely detection of potential moisture problems, thus enabling effective adjustments to irrigation strategies. Furthermore, the irrigation water volume correction module, by collecting stomatal conductance signals from plant leaves, accurately reflects the water stress status of plants. Combined with real-time vertical moisture gradient values, the system can generate a more scientific target water holding capacity, ensuring that the water needs of plants at different growth stages are met. The irrigation cycle termination control module, by setting dual termination conditions of minimum water holding capacity change rate and maximum permeability, avoids over-irrigation and reduces water waste. This not only improves irrigation efficiency but also reduces negative environmental impacts, promoting the development of sustainable agriculture. The closed-loop feedback adjustment module provides dynamic adjustment capabilities for irrigation water volume, enabling timely assessment of soil moisture content changes after irrigation and adjustments to subsequent irrigation cycles, thus forming an adaptive water management system. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of the overall system structure of the present invention;

[0049] Figure 2 Statistical graph for calculating vertical moisture gradient in the evaluation area;

[0050] Figure 3 A dotted-line graph showing the relationship between vertical moisture gradient and soil texture correction coefficient;

[0051] Figure 4 Adjust the bar chart to reflect the target water holding capacity;

[0052] Figure 5 A fitted curve of vertical moisture gradient-moisture stress response factor;

[0053] Figure 6 This is a schematic diagram of the overall method flow of the present invention. Detailed Implementation

[0054] 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.

[0055] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0056] Example:

[0057] Please see Figures 1-5 The present invention provides a technical solution:

[0058] A smart agricultural water-saving irrigation system, specifically comprising:

[0059] The land area planning module is used to divide the farmland to be irrigated into equal areas, determine several assessment areas, and set up a detection node array in each assessment area. Each node is equipped with several detection units in the vertical direction, which are located at different soil depth ranges.

[0060] The irrigated farmland area is divided into equal-area subdivisions to determine several assessment areas. The specific method for determining these assessment areas is as follows: Boundary information of the farmland area is obtained using Geographic Information System (GIS) or remote sensing technology, such as satellite imagery, drone photography, or ground surveying. GIS software is used to process the acquired boundary information, drawing the farmland boundary lines to form a closed polygonal region. The total area of ​​the entire farmland area is then calculated based on this boundary information using GIS software or calculation tools. This area serves as the basis for subsequent subdivisions. Target areas for each assessment area are set according to irrigation needs, crop type, and growth characteristics. These target areas can be selected based on crop water requirements, soil characteristics, and ease of operation; for example, each grid area could be 100 square meters, 250 square meters, etc. Based on the set areas, the farmland area is divided into several equally sized rectangular or square grids, with each grid area serving as an assessment area.

[0061] Each node is equipped with several detection units in the vertical direction, which are located at different soil depth ranges. Specifically, there are three soil depth ranges: the depth range of the main water absorption layer of the crop roots, the depth range of the water transport transition layer, and the depth range of the leakage risk monitoring layer. The depth range of the main water absorption layer of the crop roots is referred to as the first soil depth range, the depth range of the water transport transition layer is referred to as the second soil depth range, and the depth range of the leakage risk monitoring layer is referred to as the final soil depth range.

[0062] Each node is equipped with several detection units in the vertical direction, wherein the detection units are used to detect the soil water holding capacity.

[0063] For each detection unit installed vertically at each node, various sensors and detection technologies can be used to monitor soil moisture content in real time. Below are some common detection units and their working principles: Resistive soil moisture sensor: Determines moisture content by measuring soil conductivity. The sensor typically consists of two metal electrodes inserted into the soil; the current flows through the soil's conductivity to calculate the moisture content. Capacitive soil moisture sensor: Measures soil moisture using changes in capacitance. This sensor estimates soil moisture content by emitting and receiving electromagnetic waves and measuring changes in the soil's dielectric constant. Compared to resistive sensors, capacitive sensors are generally more corrosion-resistant and have a longer lifespan.

[0064] The moisture gradient characterization module is used to collect and upload soil water holding data through the detection unit. The edge computing gateway receives soil water holding data from each node and calculates the vertical moisture gradient value of each evaluation area in real time based on the difference in soil water holding at the first soil depth range and the final soil depth range and the vertical distance between them, and determines the gradient direction.

[0065] Based on the difference in soil water holding capacity between the first and final soil depth ranges and the vertical distance between them, the vertical moisture gradient value of each node is calculated in real time. First, the soil water holding capacity of each depth range is determined, specifically by using the average soil water holding capacity output by the detection units within that depth range as the soil water holding capacity for that depth range. The formula used to calculate the vertical moisture gradient value based on the soil water holding capacity of the depth range is as follows:

[0066]

[0067] In the formula, Let θ be the vertical moisture gradient of the i-th evaluation region at time t. xi (t) represents the soil water holding capacity at the first soil depth in the i-th assessment region, θ mi (t) represents the soil water holding capacity at the final soil depth range in the i-th assessment region, L ziL represents the maximum vertical depth of the final soil depth range in the i-th assessment region. xi Let t be the maximum vertical depth of the first soil depth range in the i-th assessment region, where t is the time variable within the water holding capacity detection period, and i is the index of the assessment region.

[0068] It should be noted that at time t, the vertical moisture gradient of the i-th evaluation region... This value is used to characterize the difference in soil water holding capacity between the first soil depth range and the final soil depth range. The larger the value and the more positive the value, the more soil water holding capacity the first soil depth range has in the i-th assessment area. The greater the difference in soil water holding capacity between the first soil depth range and the final soil depth range, the less water resources are wasted.

[0069] By taking soil water holding capacity measurements at both the first and final depth ranges, the moisture state of topsoil and deeper soil layers can be effectively compared. This comparison reveals the distribution of moisture and helps to understand the potential flow of water within the soil, especially after irrigation or rainfall. Therefore, the difference in soil water holding capacity θ xi (t)-θ mi (t) and Proportional.

[0070] Denominator L zi -L xi This represents the vertical distance between depth ranges. This depth difference is a key factor in calculating the moisture gradient because the distribution of moisture in the soil is related not only to water holding capacity but also to soil depth. By dividing the difference in water holding capacity by the depth difference, the rate of change of moisture per unit depth can be obtained.

[0071] In the i-th assessment region, the soil water holding capacity θ at the first soil depth range xi The formula used for the calculation is:

[0072]

[0073] In the formula, Let represent the soil water holding capacity of the j-th detection unit within the first soil depth range in the i-th evaluation region at time t, where j is the index of the detection unit within the first soil depth range, n is the total number of detection units within the first soil depth range, j∈[1,n], and the number of detection units set within the same soil depth range is the same in different evaluation regions.

[0074] In the i-th assessment region, the soil water holding capacity θ at the final soil depth range mi The formula used for the calculation of (t) is:

[0075]

[0076] In the formula, Let represent the soil water holding capacity of the q-th detection unit within the final soil depth range in the i-th evaluation region at time t, where q is the index of the detection unit within the final soil depth range, Q is the total number of detection units within the final soil depth range, and q∈[1,Q].

[0077] The logic for determining the gradient direction is as follows: a positive value indicates that water is moving downwards, and a negative value indicates that water is moving upwards.

[0078] The irrigation water correction module is used to randomly sample plants within each assessment area, collect their leaf stomatal conductance physical signals, calculate a water stress response factor based on the leaf stomatal conductance physical signals, and couple the water stress response factor, the real-time vertical water gradient value of the assessment area, and the soil texture correction coefficient to generate the target water holding capacity through weighted fusion. Table 1 shows some of the statistical data from the calculation of vertical water gradient data.

[0079] Table 1: Statistical data from the calculation of vertical moisture gradient data

[0080]

[0081]

[0082] The data shows a close relationship between the water holding capacity at the first soil depth and the water holding capacity at the final soil depth. For example, in region 2, the water holding capacity at the first soil depth is 25.0, while the final water holding capacity is 18.0, indicating that a higher initial water holding capacity makes water loss less likely. However, in region 5, the water holding capacity at the first soil depth is 18.0, but the final water holding capacity drops to 10.0, reflecting that a lower initial water holding capacity may lead to significant water loss and affect soil moisture stability.

[0083] The stomatal conductance physical signal of the leaves is collected. Specifically, the stomatal conductance physical signal of the leaves refers to the stomatal conductance potential difference of the leaves. The stomatal conductance potential difference of the leaves is collected by using interdigitated gold electrodes. After setting the distance between the electrodes, the electrodes are attached to both sides of the main vein on the back of the crop leaves to collect the stomatal conductance potential difference of the leaves.

[0084] The water stress response factor is calculated based on the physical signal of the leaf stomatal conductance, and the specific formula used to calculate the water stress response factor is as follows:

[0085]

[0086] In the formula, SP i (t) represents the water stress response factor of the crop in the i-th assessment region at time t, ΔV itLet be the stomatal conductance potential difference of crop leaves in the i-th evaluation region at time t compared to the previous time. denoted by , where k represents the rate of change of stomatal conductance potential difference of crop leaves in the i-th evaluation region, and k is a proportionality constant.

[0087] It should be noted that at time t, the water stress response factor SP of the crop in the i-th assessment region is... i (t) is used to represent the water requirement of crops, where SP i The larger the (t) value, the more water-deficient the crop is.

[0088] Among them, healthy crops maintain a relatively stable high potential difference when adequately watered, with stomata open and short-term fluctuations being small. Therefore, when crops are short of water, they exhibit a lower potential difference. In severe water shortage, the potential difference continues to decrease, and stomata remain closed. Thus, the stomatal conductance potential difference ΔV of crop leaves in the i-th evaluation region at time t compared to the previous time is significant. it With SP i (t) is inversely proportional, specifically through... It indicates an inverse relationship.

[0089] When water shortage begins, the potential difference will oscillate violently, stomatal opening and closing will become disordered, indicating a decrease in stomatal conductance and inhibited transpiration. The crop is under water stress; therefore, the more drastic the change in potential difference, the greater the probability that the crop is under water stress. i (t) and The water requirement of crops is directly proportional to the rate of change of potential difference.

[0090] The specific proportionality constant k can be set by consulting relevant scientific research literature, finding known k values ​​under similar crops or experimental conditions as a reference basis, and combining expert experience. Generally, it is set between 0.001 and 0.01.

[0091] The water stress response factor, the real-time vertical water gradient value of the assessment area, and the soil texture correction coefficient are coupled, wherein the soil texture correction coefficient is specifically determined according to the soil type in the assessment area. A target water holding capacity is generated through weighted fusion, and the specific formula used to calculate the target water holding capacity is as follows:

[0092]

[0093] In the formula, ECI i (t) represents the target water holding capacity of the i-th assessment area at time t, HW 0i Let α be the initial target water holding capacity for the i-th assessment area, α be the soil texture correction coefficient, and ω1 and ω2 be the weighting coefficients of the vertical water gradient and the water stress response factor, respectively, where ω1≤ω2 and both ω1 and ω2 are greater than 0.

[0094] It should be noted that at time t, the target water holding capacity ECI of the i-th assessment area is... i (t) is used to represent the amount of soil water required for crop growth.

[0095] Since the effects of vertical water gradient and water stress response factors on soil water holding capacity required for crop growth have already been explained in detail above, they will not be repeated here. The use of ln[1+SP] is also relevant. i The logarithmic function (t) can nonlinearly adjust the response factor, so that the effect of the response factor on the target water holding capacity changes less at low stress levels and more at high stress levels. This nonlinear relationship is more in line with the biological response characteristics.

[0096] Vertical moisture gradient This indicates the vertical variation of soil moisture distribution. A negative moisture gradient means that water flows downwards, which may lead to dryness of the topsoil and thus affect crop water availability. Although it affects target water holding capacity, its impact is relatively small because it primarily reflects the physical migration process of water rather than the physiological response of the crop. Water stress response factor SP i (t) reflects the physiological response of crops to water shortage. When water is insufficient, crop growth and physiological activities are significantly affected, and may even lead to crop yield reduction. Water stress directly affects crop growth potential and water requirement, so ω1≤ω2 is set, and both ω1 and ω2 are greater than 0.

[0097] The soil texture correction factor α is determined based on the soil type in the assessment area, specifically according to publicly available standards: clay: α = 0.15; loam: α = 0.25; sandy soil: α = 0.35. Table 2 shows the statistical data for the target water holding capacity calculation.

[0098] Table 2. Statistical data for the calculation of target water holding capacity

[0099]

[0100] Analysis of the data reveals a certain relationship between the target water holding capacity and other parameters. The data shows a close relationship between the initial target water holding capacity and the target water holding capacity. For example, in region 2, the initial target water holding capacity was 30.0%, and the target water holding capacity reached 26.5%, indicating that a higher initial target water holding capacity helps maintain a higher target water holding capacity. In contrast, in region 5, the initial target water holding capacity was 18.0%, and the target water holding capacity was 15.2%, reflecting water conservation while meeting the crop's respiration requirements.

[0101] The irrigation cycle termination control module is used to control the irrigation cycle by pulsed seepage based on the target water holding capacity. When the real-time soil water holding capacity at the first soil depth is lower than the target water holding capacity requirement, the pulsed irrigation cycle is triggered. At the same time, dual termination conditions of minimum water holding capacity change rate and maximum permeability are set. When one of the termination conditions is met, the irrigation cycle is terminated.

[0102] The pulsed irrigation control method specifically includes: selecting suitable irrigation equipment: choosing appropriate pulsed irrigation equipment, such as micro-sprinklers or drip tape, based on the crop's water requirements and soil characteristics; rationally designing and laying out irrigation pipelines and drip points to ensure uniform water distribution; and setting the duration of pulsed irrigation to avoid over-irrigation leading to excessive soil moisture, generally 2 to 5 minutes per pulse.

[0103] Based on the target water holding capacity, pulsed seepage irrigation control is adopted. This pulsed seepage irrigation control specifically includes irrigation periods and intermittent periods. When the real-time soil water holding capacity at the first soil depth range is lower than the target water holding capacity requirement, the controller triggers a pulsed irrigation cycle, opening the solenoid valve to execute the irrigation period. After the irrigation period ends, the valve closes to enter the intermittent period. During the intermittent period, the actual water holding capacity at the first soil depth range within the i-th evaluation area is monitored in real time. If the actual water holding capacity at the first soil depth range monitored during the intermittent period is lower than or equal to ECI... i If (t) 0.7, then the intermittent period ends and the next irrigation cycle begins; the intermittent period is generally set to be within 15 to 30 minutes.

[0104] The irrigation cycle is terminated when either the minimum water holding capacity change rate or the maximum permeability is met. Specifically, several time windows are set to detect the change rate of the actual water holding capacity within the first soil depth range in adjacent time windows. If the change rate is less than the set minimum water holding capacity change rate, the irrigation cycle is terminated.

[0105] The closed-loop feedback adjustment module is used to detect the soil moisture content at the final soil depth range after the irrigation cycle is terminated, set a detection time window, and characterize the leakage risk by the change in soil water holding capacity within adjacent detection time windows. At the same time, it adjusts the single irrigation water volume in the irrigation cycle based on the leakage risk, forming a closed-loop feedback water-saving irrigation control.

[0106] The risk of leakage is characterized by changes in soil water holding capacity, and the formula used to calculate the risk of leakage is as follows:

[0107]

[0108] In the formula, R leaki For the leakage risk of the i-th assessment area, θ mq i(r+s) represents the soil water holding capacity output by the q-th detection unit within the final soil depth range of the i-th evaluation region at time r+s, and θ mq i (r) represents the soil water holding capacity output by the q-th detection unit within the final soil depth range of the i-th evaluation region at time r, and s represents the time interval between adjacent detection time windows. i Let r be the area of ​​the i-th assessment region, where r is the time variable of the leakage risk detection process after the irrigation cycle is terminated, and q is the index of the detection unit in the final soil depth range within the i-th assessment region, q∈[1,Q], where Q is the total number of detection units in the final soil depth range within the i-th assessment region.

[0109] It should be noted that the leakage risk R of the i-th assessment area leaki This is used to represent the water resource loss situation in the i-th assessment area, where R leaki The larger the value, the more serious the water resource omission in the i-th assessment area.

[0110] Where θ in the formula mq i (r+s)-θ mq i (r) calculates the change in soil water holding capacity over a time interval s. This change is directly related to the assessment of leakage risk, reflecting soil water loss after irrigation; therefore, θ mq i (r+s)-θ mq i (r) and R leaki Proportional.

[0111] Multiply the change in each detection unit by the area A of the region. i This approach extends the impact of soil water holding capacity changes to the entire assessment area. This setup considers the contributions of different monitoring units, making the risk assessment more comprehensive. By summing the leakage risks of all monitoring units, the leakage risk for the entire assessment area is finally obtained. This method comprehensively considers changes in different monitoring units, providing a more accurate overall assessment result.

[0112] Simultaneously, based on the risk of leakage, the amount of water used for each irrigation cycle is adjusted accordingly. The formula used to calculate the adjusted amount of water used for each irrigation cycle is as follows:

[0113]

[0114] In the formula, wl zi For the i-th assessment area, the adjusted single irrigation water volume is wl. i0ω3 is the initial single irrigation water volume for the i-th evaluation area, and ω3 is the correction ratio coefficient, which is greater than 0.

[0115] Among them, the risk of leakage R is assessed. leaki The square root function is used to adjust the amount of irrigation water, reflecting the relationship between irrigation water volume and leakage risk. As the risk of leakage increases, the amount of irrigation water will decrease accordingly to reduce water waste and potential environmental impact. This design ensures that the impact of leakage risk on irrigation water volume exhibits a non-linear relationship. When the leakage risk is low, the adjustment range for irrigation water volume is small; however, as the leakage risk increases, the adjustment range gradually widens. This design takes into account the impact of increased leakage risk on irrigation water volume, making the feedback more sensitive.

[0116] The setting of the correction ratio coefficient ω3 provides flexibility for adjustment. By selecting an appropriate ω3 value, the amount of irrigation water can be finely adjusted according to different regions, crop types, and climatic conditions. It is generally set between 0.01 and 0.02.

[0117] Please see Figure 6 The present invention also provides a smart agricultural water-saving irrigation control method, which is used to control the above-mentioned smart agricultural water-saving irrigation system, comprising:

[0118] Step 1: Divide the farmland to be irrigated into equal areas to determine several assessment areas. Set up an array of detection nodes in each assessment area. Install several detection units in the vertical direction at each node, and position them at different soil depths.

[0119] Step 2: Collect and upload soil water holding data through the detection unit. The edge computing gateway receives soil water holding data from each node. Based on the difference in soil water holding capacity between the first soil depth range and the final soil depth range and the vertical distance between them, it calculates the vertical water gradient value of each evaluation area in real time and determines the gradient direction.

[0120] Step 3: Randomly sample plants in each assessment area and collect their leaf stomatal conductance physical signals. Calculate the water stress response factor based on the leaf stomatal conductance physical signals. Couple the water stress response factor, the real-time vertical water gradient value of the assessment area, and the soil texture correction coefficient, and generate the target water holding capacity through weighted fusion.

[0121] Step 4: Based on the target water holding capacity, pulsed seepage irrigation control is adopted. When the real-time soil water holding capacity at the first soil depth range is lower than the target water holding capacity requirement, the pulsed irrigation cycle is triggered. At the same time, dual termination conditions of minimum water holding capacity change rate and maximum permeability are set. When one of the termination conditions is met, the irrigation cycle is terminated.

[0122] Step 5: After terminating the irrigation cycle, detect the soil moisture content at the final soil depth range, set a detection time window, and characterize the leakage risk by measuring the change in soil water holding capacity within adjacent detection time windows. At the same time, adjust the single irrigation water volume in the irrigation cycle based on the leakage risk to form a closed-loop feedback water-saving irrigation control.

[0123] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0124] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0125] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0126] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A smart agricultural water-saving irrigation system, characterized in that, Specifically, it includes: The land area planning module is used to divide the farmland to be irrigated into equal areas, determine several assessment areas, and set up a detection node array in each assessment area. Each node is equipped with several detection units in the vertical direction, which are located at different soil depth ranges. The moisture gradient characterization module is used to collect and upload soil water holding data through the detection unit. The edge computing gateway receives soil water holding data from each node. Based on the difference in soil water holding at the first soil depth range and the final soil depth range and the vertical distance between them, it calculates the vertical moisture gradient value of each evaluation area in real time and determines the gradient direction. The irrigation water volume correction module is used to randomly sample plants in each assessment area, collect their leaf stomatal conductance physical signals, calculate water stress response factors based on the leaf stomatal conductance physical signals, and couple the water stress response factors, the real-time vertical water gradient value of the assessment area and the soil texture correction coefficient to generate the target water holding capacity through weighted fusion. The irrigation cycle termination control module is used to control pulsed seepage irrigation based on the target water holding capacity. When the real-time soil water holding capacity at the first soil depth range is lower than the target water holding capacity requirement, the pulsed irrigation cycle is triggered. At the same time, dual termination conditions of minimum water holding capacity change rate and maximum permeability are set. When one of the termination conditions is met, the irrigation cycle is terminated. The closed-loop feedback adjustment module is used to detect the soil moisture content at the final soil depth range after the irrigation cycle is terminated, set a detection time window, and characterize the leakage risk by the change in soil water holding capacity within adjacent detection time windows. At the same time, it adjusts the single irrigation water volume in the irrigation cycle based on the leakage risk, forming a closed-loop feedback water-saving irrigation control.

2. The smart agricultural water-saving irrigation system according to claim 1, characterized in that: Each node is equipped with several detection units in the vertical direction, which are located at different soil depth ranges. Specifically, there are three soil depth ranges: the depth range of the main water absorption layer of the crop roots, the depth range of the water transport transition layer, and the depth range of the leakage risk monitoring layer. The depth range of the main water absorption layer of the crop roots is referred to as the first soil depth range, the depth range of the water transport transition layer is referred to as the second soil depth range, and the depth range of the leakage risk monitoring layer is referred to as the final soil depth range. The irrigated farmland area is divided into equal areas to determine several evaluation areas. The specific method for determining the evaluation areas is as follows: obtain the boundary information of the farmland area and draw the boundary of the farmland. According to the set area, the farmland area is divided into several rectangular or square grids of equal size, and each grid area is used as an evaluation area. Each node is equipped with several detection units in the vertical direction, wherein the detection units are used to detect the soil water holding capacity.

3. The smart agricultural water-saving irrigation system according to claim 2, characterized in that: Based on the difference in soil water holding capacity between the first and final soil depth ranges and the vertical distance between them, the vertical moisture gradient value of each node is calculated in real time. First, the soil water holding capacity of each depth range is determined, specifically by using the average soil water holding capacity output by the detection units within that depth range as the soil water holding capacity for that depth range. The formula used to calculate the vertical moisture gradient value based on the soil water holding capacity of the depth range is as follows: In the formula, Let θ be the vertical moisture gradient of the i-th evaluation region at time t. xi (t) represents the soil water holding capacity at the first soil depth in the i-th assessment region, θ mi (t) represents the soil water holding capacity at the final soil depth range in the i-th assessment region, L zi L represents the maximum vertical depth of the final soil depth range in the i-th assessment region. xi Let t be the maximum vertical depth of the first soil depth range in the i-th assessment region, where t is the time variable within the water holding capacity detection period, and i is the index of the assessment region. In the i-th assessment region, the soil water holding capacity θ at the first soil depth range xi The formula used for the calculation is: In the formula, This represents the soil water holding capacity of the j-th detection unit within the first soil depth range in the i-th evaluation region at time t, where j is the index of the detection unit within the first soil depth range, n is the total number of detection units within the first soil depth range, j∈[1,n], and the number of detection units set within the same soil depth range is the same in different evaluation regions; The logic for determining the gradient direction is as follows: a positive value indicates that water is moving downwards, and a negative value indicates that water is moving upwards.

4. The smart agricultural water-saving irrigation system according to claim 3, characterized in that: The stomatal conductance physical signal of the leaves is collected. Specifically, the stomatal conductance physical signal of the leaves refers to the stomatal conductance potential difference of the leaves. The stomatal conductance potential difference of the leaves is collected by using interdigitated gold electrodes. After setting the distance between the electrodes, the electrodes are attached to both sides of the main vein on the back of the crop leaves to collect the stomatal conductance potential difference of the leaves. The water stress response factor is calculated based on the physical signal of the stomatal conductance of the blade, and the specific formula used to calculate the water stress response factor is as follows: In the formula, SP i (t) represents the water stress response factor of the crop in the i-th assessment region at time t, ΔV it Let be the stomatal conductance potential difference of crop leaves in the i-th evaluation region at time t compared to the previous time. denoted by , where k represents the rate of change of stomatal conductance potential difference of crop leaves in the i-th evaluation region, and k is a proportionality constant.

5. The smart agricultural water-saving irrigation system according to claim 4, characterized in that: The water stress response factor, the real-time vertical water gradient value of the assessment area, and the soil texture correction coefficient are coupled, wherein the soil texture correction coefficient is specifically determined according to the soil type in the assessment area. A target water holding capacity is generated through weighted fusion, and the specific formula used to calculate the target water holding capacity is as follows: In the formula, ECI i (t) represents the target water holding capacity of the i-th assessment area at time t, HW 0i Let α be the initial target water holding capacity for the i-th assessment area, α be the soil texture correction coefficient, and ω1 and ω2 be the weighting coefficients of the vertical water gradient and the water stress response factor, respectively, where ω1≤ω2 and both ω1 and ω2 are greater than 0.

6. The smart agricultural water-saving irrigation system according to claim 1, characterized in that: Based on the target water holding capacity, pulsed seepage irrigation control is adopted. This pulsed seepage irrigation control specifically includes irrigation periods and intermittent periods. When the real-time soil water holding capacity at the first soil depth range is lower than the target water holding capacity requirement, the controller triggers a pulsed irrigation cycle, opening the solenoid valve to execute the irrigation period. After the irrigation period ends, the valve closes to enter the intermittent period. During the intermittent period, the actual water holding capacity at the first soil depth range within the i-th evaluation area is monitored in real time. If the actual water holding capacity at the first soil depth range monitored during the intermittent period is lower than or equal to ECI... i If (t)*0.7, then the intermittent period ends and the next irrigation cycle begins; The irrigation cycle is terminated when either the minimum water holding capacity change rate or the maximum permeability is met. Specifically, several time windows are set to detect the change rate of the actual water holding capacity in the first soil depth range within adjacent time windows. If the change rate is less than the set minimum water holding capacity change rate, the irrigation cycle is terminated. Meanwhile, the maximum permeability termination condition specifically refers to: when the actual water holding capacity of the final soil depth range increases more than the set maximum permeability within an adjacent time window, the irrigation cycle is terminated; wherein the duration of the time window is less than the duration of the intermittent period.

7. The smart agricultural water-saving irrigation system according to claim 5, characterized in that: The risk of leakage is characterized by changes in soil water holding capacity, and the formula used to calculate the risk of leakage is as follows: In the formula, R leaki For the leakage risk of the i-th assessment area, θ mq i (r+s) represents the soil water holding capacity output by the q-th detection unit within the final soil depth range of the i-th evaluation region at time r+s, and θ mq i (r) represents the soil water holding capacity output by the q-th detection unit within the final soil depth range of the i-th evaluation region at time r, and s represents the time interval between adjacent detection time windows. i Let r be the area of ​​the i-th assessment region, where r is the time variable of the leakage risk detection process after the irrigation cycle is terminated, and q is the index of the detection unit in the final soil depth range within the i-th assessment region, q∈[1,Q], where Q is the total number of detection units in the final soil depth range within the i-th assessment region; Simultaneously, based on the risk of leakage, the amount of water used for each irrigation cycle is adjusted accordingly. The formula used to calculate the adjusted amount of water used for each irrigation cycle is as follows: In the formula, wl zi For the i-th assessment area, the adjusted single irrigation water volume is wl. i0 ω3 is the initial single irrigation water volume for the i-th evaluation area, and ω3 is the correction ratio coefficient, which is greater than 0.

8. A smart agricultural water-saving irrigation control method, characterized in that: The intelligent agricultural water-saving irrigation control method is used to control the intelligent agricultural water-saving irrigation system according to any one of claims 1-7, comprising: The irrigated farmland area is divided into equal areas to determine several assessment areas. In each assessment area, a detection node array is set up. Each node is equipped with several detection units in the vertical direction and is located at different soil depths. Soil water holding data is collected and uploaded by the detection unit. The edge computing gateway receives soil water holding data from each node. Based on the difference in soil water holding capacity between the first soil depth range and the final soil depth range and the vertical distance between them, the vertical water gradient value of each evaluation area is calculated in real time, and the gradient direction is determined. Randomly sample plants within each assessment area and collect their leaf stomatal conductance physical signals. Calculate water stress response factors based on these signals. Couple the water stress response factors with the real-time vertical water gradient value of the assessment area and the soil texture correction coefficient, and generate the target water holding capacity through weighted fusion. Based on the target water holding capacity, pulsed seepage irrigation control is adopted. When the real-time soil water holding capacity at the first soil depth range is lower than the target water holding capacity requirement, the pulsed irrigation cycle is triggered. At the same time, dual termination conditions of minimum water holding capacity change rate and maximum permeability are set. When one of the termination conditions is met, the irrigation cycle is terminated. After the irrigation cycle is terminated, the soil moisture content at the final soil depth is measured, and a measurement time window is set. Within adjacent measurement time windows, the risk of leakage is characterized by changes in soil water holding capacity. At the same time, the amount of water used for each irrigation cycle is adjusted based on the risk of leakage, forming a closed-loop feedback water-saving irrigation control.

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