An intelligent irrigation management system based on soil and crop moisture condition identification

Abstract

This invention discloses a smart irrigation management system based on soil and crop moisture status identification, belonging to the field of crop moisture status monitoring technology. The system includes: acquiring leaf electrophysiological signal sequences and multi-depth water potential parameters of the rhizosphere microdomain of the target crop; determining the onset time of water stress and its corresponding first characteristic frequency based on the leaf electrophysiological signal sequences; determining the active water-uptake layer and its corresponding second characteristic water potential based on the multi-depth water potential parameters; performing a coupled comparison based on the first characteristic frequency and the second characteristic water potential, and determining whether irrigation triggering conditions are met based on the comparison results. If the irrigation triggering conditions are met, a differentiated irrigation quota is determined for different irrigation areas. This invention determines irrigation triggering conditions by comparing the consistency and degree of deviation between the first and second deviation directions, avoiding premature or late irrigation caused by misjudgment based on a single indicator.

Description

Technical Field

[0001] This invention relates to the field of crop moisture status monitoring technology, specifically to a smart irrigation management system based on soil and crop moisture status identification. Background Technology

[0002] Accurate sensing of crop water status and reasonable determination of irrigation timing are the core aspects of farmland water management. Existing crop water stress monitoring methods mainly include obtaining root zone water content based on soil moisture sensors, obtaining crop water physiological signals based on stem flow meters or stem diameter change sensors, and obtaining leaf water status based on leaf stomatal conductance or leaf water potential measurements.

[0003] Soil moisture sensors reflect soil water supply conditions, but there is a lag between soil moisture content and the actual water perception of crops, which is determined by root distribution, soil texture and atmospheric evaporation. Relying solely on soil moisture thresholds to trigger irrigation can easily lead to irrigation timing that is delayed or advanced before the actual water demand of crops. Crop stem flow meters or stem diameter change sensors can reflect the dynamic changes in crop transpiration or internal water balance, but their signal response usually appears after water stress has developed to a certain extent, and the installation location is limited to single plants or a few plants, making it difficult to achieve regional representative monitoring. Although leaf stomatal conductance meters or pressure chamber leaf water potential measurements can directly reflect leaf water status, operation depends on manual labor and measurement intervals are long, making it difficult to achieve continuous automated monitoring. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a smart irrigation management system based on soil and crop moisture status identification.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a smart irrigation management system based on soil and crop moisture status identification, specifically comprising: Leaf physiological signal acquisition module: acquires leaf electrophysiological signal sequences of the target crop and multi-depth water potential parameters of the rhizosphere microdomain; Water potential parameter acquisition module: Based on the leaf electrophysiological signal sequence, determine the onset time of water stress of the crop and the corresponding first characteristic frequency; Coupling determination module: Based on the multi-depth water potential parameters, construct a vertical gradient distribution map of rhizosphere water potential and determine the active water absorption layer and its corresponding second characteristic water potential; Irrigation decision module: Based on the first characteristic frequency and the second characteristic water potential, a coupled comparison is performed, and the comparison result is used to determine whether the irrigation triggering condition is met. If the irrigation triggering condition is met, a differentiated irrigation quota is determined for different irrigation areas.

[0006] Preferably, the leaf physiological signal acquisition module is equipped with micro water potential sensing elements at the first, second, third and fourth depths of the rhizosphere micro-domain of the target crop. The first depth corresponds to the top of the shallow tillage layer where the crop's fibrous roots are mainly distributed, the second depth corresponds to the middle of the tillage layer where the fibrous root density is the highest, the third depth corresponds to the transition layer between fibrous roots and lateral roots, and the fourth depth corresponds to the deep soil layer where the lateral roots are mainly distributed. Water potential readings at corresponding depths are collected by each micro water potential sensing element at a second sampling frequency to form a first depth water potential sequence, a second depth water potential sequence, a third depth water potential sequence, and a fourth depth water potential sequence, which together serve as multi-depth water potential parameters. At the same sampling time, calculate the first interlayer water potential difference between the first depth water potential value and the second depth water potential value, calculate the second interlayer water potential difference between the second depth water potential value and the third depth water potential value, and calculate the third interlayer water potential difference between the third depth water potential value and the fourth depth water potential value. The interlayer water potential difference, the interlayer water potential difference, and the interlayer water potential difference are compared, and the depth range corresponding to the interlayer water potential difference with the largest value is taken as the active water absorption layer. The water potential value at the midpoint depth of the active layer is extracted, or the arithmetic mean of the water potential values ​​at the upper and lower depths of the active layer is extracted, is used as the second characteristic water potential.

[0007] Preferably, the water potential parameter acquisition module divides the leaf electrophysiological signal sequence into multiple continuous time segments according to the division step size. The division step size is taken as an integer multiple of the average value of the peak-valley transition cycle of the crop under normal water conditions, and adjacent time segments are connected end to end on the time axis. For each time segment, identify the effective peak and effective valley values ​​in the signal data. During identification, calculate the first-order difference sequence of the signal data. The zero-crossing point where the first-order difference changes from positive to negative and the change amplitude exceeds the dynamic fluctuation tolerance is taken as the effective peak value. The zero-crossing point where the first-order difference changes from negative to positive and the change amplitude exceeds the dynamic fluctuation tolerance is taken as the effective valley value. The dynamic fluctuation tolerance is taken as a fixed percentage of the standard deviation of the signal data in the corresponding time segment. The time interval between the effective peak value and the subsequent effective trough value is recorded as the first half-cycle, and the time interval between the effective trough value and the subsequent effective peak value is recorded as the second half-cycle. The sum of the first half-cycle and the second half-cycle is a peak-trough transition cycle. The arithmetic mean of all peak-trough transition cycles within a time segment is taken as the representative value of the peak-trough transition cycle of that time segment. Based on the chronological order of time segments, the difference between the representative value of the peak-valley transition period of the later time segment and the representative value of the peak-valley transition period of the previous time segment is calculated as the extended increment. When multiple consecutive extension increments are all positive and exceed the judgment reference range determined by the statistical distribution of extension increments during the stable period, the starting point of the time segment corresponding to the first extension increment among the multiple consecutive extension increments is marked as the start time of water stress. The reciprocal of the peak-valley transition period of the time segment in which the water stress begins is determined as the first characteristic frequency.

[0008] Preferably, the coupling determination module acquires multi-depth water potential parameters, which include water potential values ​​collected at the first, second, third, and fourth depths of the rhizosphere microdomain of the target crop. At the same sampling time, the water potential value at the first depth is taken as the first water potential value, the water potential value at the second depth is taken as the second water potential value, the water potential value at the third depth is taken as the third water potential value, and the water potential value at the fourth depth is taken as the fourth water potential value. The first water potential value, the second water potential value, the third water potential value and the fourth water potential value are connected in order from shallow to deep to form a data chain of water potential change with depth. The difference between the second water potential value and the first water potential value is calculated as the first water potential drop; the difference between the third water potential value and the second water potential value is calculated as the second water potential drop; and the difference between the fourth water potential value and the third water potential value is calculated as the third water potential drop. By comparing the values ​​of the first, second, and third water potential drops, the depth range corresponding to the water potential drop with the largest negative sign and the largest absolute value is identified as the active water-absorbing layer. Determine the upper and lower limits of the active water-absorbing layer, calculate the arithmetic mean of the upper and lower limits as the midpoint depth, perform linear interpolation between the water potential value corresponding to the upper and lower limits to obtain the water potential value at the midpoint depth, and use the water potential value at the midpoint depth as the second characteristic water potential.

[0009] Preferably, the irrigation decision module acquires the reference frequency of leaf electrophysiological signals and the reference value of rhizosphere water potential of the target crop under normal water supply conditions. The reference frequency of leaf electrophysiological signals is the average value of the reciprocal of the peak-to-valley transition period of leaf electrophysiological signal sequences collected over several consecutive days after full irrigation. The reference value of rhizosphere water potential is the average value of the water potential at the midpoint of the active water absorption layer collected during the stable period of field water holding capacity after full irrigation. Calculate the first deviation direction and the first deviation degree of the first characteristic frequency relative to the reference frequency of the leaf electrophysiological signal. The first deviation direction is determined by the numerical relationship between the first characteristic frequency and the reference frequency, and the first deviation degree is determined by the ratio of the difference between the reference frequency and the first characteristic frequency to the reference frequency. Calculate the second deviation direction and the second deviation degree of the second characteristic water potential relative to the root zone water potential reference value. The second deviation direction is determined by the numerical relationship between the second characteristic water potential and the reference value, and the second deviation degree is determined by the ratio of the difference between the reference value and the second characteristic water potential to the reference value. When the first deviation direction is the frequency reduction direction and the second deviation direction is the water potential reduction direction, and the comparison between the first deviation degree and the second deviation degree falls into the first judgment interval corresponding to the current growth stage of the crop, it is determined that the irrigation triggering condition is met. When the irrigation triggering conditions are met, obtain the vegetation cover density parameters corresponding to different irrigation areas; For areas where the vegetation cover density is greater than the first reference value, the first irrigation quota correction coefficient is determined based on the absolute value of the frequency difference between the first characteristic frequency and the reference frequency of the leaf electrophysiological signal. The benchmark irrigation quota is multiplied by the first irrigation quota correction coefficient to obtain the increased irrigation quota. For areas where the vegetation cover density is less than the second reference value, the second irrigation quota correction coefficient is determined based on the absolute value of the difference between the root zone water potential reference value and the second characteristic water potential. The adjusted irrigation quota is obtained by multiplying the benchmark irrigation quota by the second irrigation quota correction coefficient.

[0010] This invention provides a smart irrigation management system based on soil and crop moisture status identification, which has the following beneficial effects: This invention determines the onset time of water stress and the first characteristic frequency by extracting the unidirectional extension time of the peak-valley transition cycle of the leaf electrophysiological signal. At the same time, it identifies the active water-absorbing layer and determines the second characteristic water potential by calculating the maximum negative jump of the water potential difference between adjacent depths. Thus, it simultaneously captures the occurrence and development of water stress from two dimensions: the electrophysiological response of the aboveground part of the crop and the water supply of the underground part. It can provide response signals in the early stage of water stress and avoids the masking of the water status of the actual water absorption area of ​​the root system by the average soil moisture content of the whole layer. This application performs a coupled comparison between the first characteristic frequency and the second characteristic water potential, and determines the irrigation triggering condition based on the consistency of the first deviation direction and the second deviation direction and the comparison of the degree of deviation. This avoids premature or late irrigation caused by misjudgment of a single indicator. After the irrigation triggering condition is met, differentiated irrigation correction coefficients are determined based on the first characteristic frequency offset and the decrease in the second characteristic water potential, taking into account the differences in vegetation cover density in different irrigation areas. This achieves precise spatial distribution of irrigation water and reduces the problem of local over- or under-irrigation caused by uniform irrigation. Attached Figure Description

[0011] Figure 1 This is a system block diagram of the present invention. Detailed Implementation

[0012] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] Please see Figure 1 This invention provides a smart irrigation management system based on soil and crop moisture status identification, comprising: Leaf physiological signal acquisition module: acquires leaf electrophysiological signal sequences of the target crop and multi-depth water potential parameters of the rhizosphere microdomain; In this embodiment of the invention, the leaf physiological signal acquisition module needs to be specifically described. The leaf physiological signal acquisition module has micro water potential sensing elements deployed at the first, second, third and fourth depths of the rhizosphere micro-domain of the target crop. The first depth corresponds to the top of the shallow tillage layer where the crop's fibrous roots are mainly distributed, the second depth corresponds to the middle of the tillage layer where the fibrous root density is the highest, the third depth corresponds to the transition layer between fibrous roots and lateral roots, and the fourth depth corresponds to the deep soil layer where the lateral roots are mainly distributed. Water potential readings at corresponding depths are collected by each micro water potential sensing element at a second sampling frequency to form a first depth water potential sequence, a second depth water potential sequence, a third depth water potential sequence, and a fourth depth water potential sequence, which together serve as multi-depth water potential parameters. At the same sampling time, calculate the first interlayer water potential difference between the first depth water potential value and the second depth water potential value, calculate the second interlayer water potential difference between the second depth water potential value and the third depth water potential value, and calculate the third interlayer water potential difference between the third depth water potential value and the fourth depth water potential value. The interlayer water potential difference, the interlayer water potential difference, and the interlayer water potential difference are compared, and the depth range corresponding to the interlayer water potential difference with the largest value is taken as the active water absorption layer. The water potential value at the midpoint depth of the active layer is extracted, or the arithmetic mean of the water potential values ​​at the upper and lower depths of the active layer is extracted, is used as the second characteristic water potential.

[0014] It should be noted that the rhizosphere microzone refers to the soil microzone extending a few millimeters outward from the surface of the crop root system. The soil moisture status in this area is directly affected by the water absorption activities of the root system, and its water potential changes can reflect in real time the ease or difficulty of crop water absorption and the development process of root zone water deficit. When deploying the miniature water potential sensing element, the deployment point is determined by taking the base of the main stem of the target crop as the center and extending radially outward to two-thirds of the radius of the main root distribution area. At the deployment point, the miniature water potential sensing element is buried at the first, second, third and fourth depths by pre-drilling holes. The first depth corresponds to the top of the shallow tillage layer where the crop's fibrous roots are mainly distributed, the second depth corresponds to the middle of the tillage layer where the fibrous root density is the highest, the third depth corresponds to the transition layer between fibrous roots and lateral roots, and the fourth depth corresponds to the deep soil layer where the lateral roots are mainly distributed. Miniature water potential sensing elements at each depth are connected to a ground data acquisition terminal via buried cables. The data acquisition terminal polls and reads the pressure signals output by each depth sensing element at a second sampling frequency, and converts the pressure signals into water potential values ​​expressed in pressure units or energy units, forming a water potential reading sequence at each depth location over time. The water potential reading sequence is the multi-depth water potential parameter. After obtaining the multi-depth water potential parameters, it is necessary to construct the vertical gradient distribution of rhizosphere water potential based on the multi-depth water potential parameters and determine the active water uptake layer and the second characteristic water potential corresponding to the layer. The vertical gradient distribution of rhizosphere water potential refers to the arrangement of differences in soil water potential values ​​at different depths along the vertical direction. The water potential readings output by the micro water potential sensing elements at each depth are obtained at the same sampling time. With depth as the vertical axis and water potential value as the horizontal axis, the water potential values ​​corresponding to the first depth, the second depth, the third depth, and the fourth depth are connected in order from shallow to deep to form a broken line of water potential change with depth at that sampling time. A connection operation is performed at each sampling time within the daily cycle to obtain the rhizosphere water potential vertical gradient distribution that changes dynamically over time. In the rhizosphere water potential vertical gradient distribution, the water potential difference between different depths reflects the magnitude of the driving force for water flow to the roots within that depth range. When the water potential difference of a certain depth range remains the maximum value among all depth ranges for multiple consecutive sampling times, it indicates that the soil water migration flux to the roots is the largest within that depth range, and the water uptake activity of the crop roots is mainly concentrated within that depth range, which is the water uptake active layer. The specific method for determining the active water-absorbing layer is as follows: For the water potential difference between adjacent depths, calculate the first interlayer water potential difference between the first depth and the second depth, the second interlayer water potential difference between the second depth and the third depth, and the third interlayer water potential difference between the third depth and the fourth depth; compare the first interlayer water potential difference, the second interlayer water potential difference, and the third interlayer water potential difference, and take the depth interval corresponding to the interlayer water potential difference with the largest value as the active water-absorbing layer. After determining the active water-absorbing layer, the water potential value at the midpoint of the depth range is extracted, or the arithmetic mean of the water potential values ​​at the upper and lower limits of the depth range is extracted as the second characteristic water potential. The second characteristic water potential characterizes the actual water energy state of the main water absorption area of ​​the crop root system. The lower the value, the less effective water the root zone can absorb from the crop, and the more severe the water stress.

[0015] Water potential parameter acquisition module: Based on the leaf electrophysiological signal sequence, determine the onset time of water stress of the crop and the corresponding first characteristic frequency; In this embodiment of the invention, the water potential parameter acquisition module needs to be specifically described. The water potential parameter acquisition module divides the leaf electrophysiological signal sequence into multiple continuous time segments according to the division step size. The division step size is taken as an integer multiple of the average value of the peak-valley transition cycle of the crop under normal water conditions. Adjacent time segments are connected end to end on the time axis. For each time segment, identify the effective peak and effective valley values ​​in the signal data. During identification, calculate the first-order difference sequence of the signal data. The zero-crossing point where the first-order difference changes from positive to negative and the change amplitude exceeds the dynamic fluctuation tolerance is taken as the effective peak value. The zero-crossing point where the first-order difference changes from negative to positive and the change amplitude exceeds the dynamic fluctuation tolerance is taken as the effective valley value. The dynamic fluctuation tolerance is taken as a fixed percentage of the standard deviation of the signal data in the corresponding time segment. The time interval between the effective peak value and the subsequent effective trough value is recorded as the first half-cycle, and the time interval between the effective trough value and the subsequent effective peak value is recorded as the second half-cycle. The sum of the first half-cycle and the second half-cycle is a peak-trough transition cycle. The arithmetic mean of all peak-trough transition cycles within a time segment is taken as the representative value of the peak-trough transition cycle of that time segment. Based on the chronological order of time segments, the difference between the representative value of the peak-valley transition period of the later time segment and the representative value of the peak-valley transition period of the previous time segment is calculated as the extended increment. When multiple consecutive extension increments are all positive and exceed the judgment reference range determined by the statistical distribution of extension increments during the stable period, the starting point of the time segment corresponding to the first extension increment among the multiple consecutive extension increments is marked as the start time of water stress. The reciprocal of the peak-valley transition period of the time segment in which the water stress begins is determined as the first characteristic frequency.

[0016] It should be noted that when crops are not under water stress, the electrophysiological activity of leaves follows a regular rhythm synchronized with the photoperiod. The intervals between action potential firing and the fluctuation period of surface potential are relatively stable. When crops begin to experience water stress, the stomatal conductance of leaf guard cells decreases due to a drop in turgor pressure, weakening transpiration pull and disrupting the leaf cell membrane potential maintenance mechanism. Consequently, the fluctuation period of electrophysiological signals undergoes detectable changes, specifically a decrease in the frequency of action potential firing and a prolongation of the surface potential fluctuation period. Dividing the leaf electrophysiological signal sequence into multiple continuous time segments, the method for dividing these time segments... The formula is as follows: A partitioning step size is determined based on the statistical value of the time required for a crop electrophysiological signal to complete a full fluctuation cycle under normal conditions. This step size is typically an integer multiple of the average peak-to-trough transition cycle under normal conditions, ensuring that each time segment contains at least several complete signal fluctuation cycles. Starting from the beginning of the leaf electrophysiological signal sequence, and using the partitioning step size as the interval, signal data within each time segment is sequentially extracted. Adjacent time segments are connected end-to-end on the time axis without overlap, thus dividing the entire leaf electrophysiological signal sequence into multiple continuous time segments with the same time span. If the total duration of the leaf electrophysiological signal sequence is not divisible by the partitioning step size, the remaining portion less than one partitioning step size is treated as a separate time segment or merged with adjacent segments. After completing the time segment division, the peak-valley transition period of the signal fluctuation within each time segment is extracted. The peak-valley transition period refers to the time interval from one signal peak to the next signal trough and then from that trough to the next adjacent peak in the leaf electrophysiological signal sequence, that is, the duration of a complete fluctuation cycle. For each time segment of signal data, the peak and valley points are first identified. This is done by performing a first-order difference calculation on the signal data within the time segment. The zero-crossing point in the first-order difference sequence where a positive value changes to a negative value corresponds to the signal peak, and the zero-crossing point where a negative value changes to a positive value corresponds to the signal valley. To avoid misjudging peaks or valleys due to minor residual fluctuations in the signal, a dynamic fluctuation tolerance is introduced during the identification process. The dynamic fluctuation tolerance is a specific proportion of the standard deviation of the signal data within the time segment. Only when the change in the difference value on both sides of the zero-crossing point of the first-order difference exceeds this dynamic fluctuation tolerance is the zero-crossing point confirmed as a valid peak or valley. After identifying all valid peaks and valleys within a time segment, the time intervals between adjacent peaks and valleys are recorded sequentially according to time order. The time interval from a peak to a subsequent valley is recorded as the first half-cycle, and the time interval from a valley to a subsequent peak is recorded as the second half-cycle. The sum of the first half-cycle and the second half-cycle is a peak-valley transition cycle. The arithmetic mean of all peak-valley transition cycles occurring within a time segment is calculated, and the average value is used as the representative value of the peak-valley transition cycle corresponding to that time segment. After obtaining the representative values ​​of the peak-valley transition period for each time segment, the extension increment of the peak-valley transition period between adjacent time segments is monitored. The extension increment refers to the increase in the peak-valley transition period value of the later time segment relative to the peak-valley transition period value of the earlier time segment. According to the order of the time segments, the peak-valley transition period value of the second time segment is subtracted from the peak-valley transition period value of the first time segment to obtain the first extension increment. The peak-valley transition period value of the third time segment is subtracted from the peak-valley transition period value of the second time segment to obtain the second extension increment, and so on until the last time segment is calculated. The sign of the extended increment reflects the direction of change in the peak-to-trough transition cycle. When the extended increment is positive, it indicates that the fluctuation rhythm of the electrophysiological signal in the later time segment has slowed down compared to the previous time segment, that is, the peak-to-trough transition cycle has been prolonged. When the extended increment is negative, it indicates that the fluctuation rhythm has accelerated, that is, the peak-to-trough transition cycle has been shortened. When the absolute value of the extended increment is less than a judgment reference range determined by the statistical distribution of the extended increment of the leaf electrophysiological signal sequence in the stable period, it indicates that the fluctuation rhythm has not changed substantially. During the monitoring process, the focus should be on observing the occurrence of consecutive positive extended increments and the number of time segments in which they last. When monitoring reveals that the extension increment is unidirectionally extended in several consecutive time segments, the starting point of the time segment in which the first unidirectional extension occurs is marked as the onset time of water stress. The starting point of the time segment in which the first unidirectional extension occurs refers to the start time of the first time segment in the continuous unidirectional extension sequence. This moment marks the critical point at which the crop electrophysiological signal transitions from the normal rhythm state to the water stress response state, and therefore it is marked as the onset time of water stress. While marking the onset time of water stress, the representative value of the peak-valley transition period corresponding to the time segment at that time is obtained. The reciprocal of the representative value of the peak-valley transition period is calculated, and the resulting value is the first characteristic frequency. The first characteristic frequency reflects the speed of the fluctuation of the electrophysiological signal of crop leaves at the onset time of water stress. The lower the frequency value, the slower the signal fluctuation and the more obvious the electrophysiological inhibition caused by water stress.

[0017] Coupling determination module: Based on the multi-depth water potential parameters, construct a vertical gradient distribution map of rhizosphere water potential and determine the active water absorption layer and its corresponding second characteristic water potential; In this embodiment of the invention, the coupling determination module needs to be specifically described. The coupling determination module acquires multi-depth water potential parameters, which include water potential values ​​collected at the first, second, third, and fourth depths of the rhizosphere microdomain of the target crop. At the same sampling time, the water potential value at the first depth is taken as the first water potential value, the water potential value at the second depth is taken as the second water potential value, the water potential value at the third depth is taken as the third water potential value, and the water potential value at the fourth depth is taken as the fourth water potential value. The first water potential value, the second water potential value, the third water potential value and the fourth water potential value are connected in order from shallow to deep to form a data chain of water potential change with depth. The difference between the second water potential value and the first water potential value is calculated as the first water potential drop; the difference between the third water potential value and the second water potential value is calculated as the second water potential drop; and the difference between the fourth water potential value and the third water potential value is calculated as the third water potential drop. By comparing the values ​​of the first, second, and third water potential drops, the depth range corresponding to the water potential drop with the largest negative sign and the largest absolute value is identified as the active water-absorbing layer. Determine the upper and lower limits of the active water-absorbing layer, calculate the arithmetic mean of the upper and lower limits as the midpoint depth, perform linear interpolation between the water potential value corresponding to the upper and lower limits to obtain the water potential value at the midpoint depth, and use the water potential value at the midpoint depth as the second characteristic water potential.

[0018] It should be noted that the multi-depth water potential parameters are collected by miniature water potential sensing elements deployed at different depths in the rhizosphere microdomain of the target crop. These include a first depth water potential sequence, a second depth water potential sequence, a third depth water potential sequence, and a fourth depth water potential sequence. Each depth water potential sequence records the continuous reading of soil water potential changes over time at the corresponding depth location. Based on the multi-depth water potential parameters, the water potential values ​​of each sampling point are arranged along the vertical depth direction to form a data chain of water potential changes with depth. Select the water potential readings output by the micro water potential sensing elements at each depth at the same sampling time, and record the water potential value corresponding to the first depth as the first water potential value, the water potential value corresponding to the second depth as the second water potential value, the water potential value corresponding to the third depth as the third water potential value, and the water potential value corresponding to the fourth depth as the fourth water potential value. Using vertical depth as the arrangement axis, the first depth and its corresponding first water potential value are taken as the first node of the data chain, the second depth and its corresponding second water potential value are taken as the second node of the data chain, the third depth and its corresponding third water potential value are taken as the third node of the data chain, and the fourth depth and its corresponding fourth water potential value are taken as the fourth node of the data chain. The first, second, third, and fourth nodes are connected sequentially from shallow to deep to form a data chain of water potential changes with depth at the sampling time. The data chain reflects the vertical distribution of water potential values ​​from the shallow tillage layer to the deep soil layer. The higher the water potential value, the closer the soil water energy state is to free water, and the easier it is for crop roots to absorb water. The lower the water potential value, the stronger the soil water is bound by the soil matrix, and the greater the resistance that crop roots need to overcome to absorb water. After forming a data chain of water potential changes with depth, the water potential difference between adjacent depth sampling points is calculated. The water potential difference between adjacent depth sampling points refers to the difference in water potential values ​​between two adjacent sampling points along the vertical depth direction. The magnitude and positive or negative direction of the difference reflect the degree and direction of the jump in soil moisture energy state between the two depths. The difference between the second water potential value corresponding to the second depth and the first water potential value corresponding to the first depth is recorded as the first water potential drop. The first water potential drop corresponds to the interlayer water potential change between the first depth and the second depth. The difference between the third water potential value corresponding to the third depth and the second water potential value corresponding to the second depth is recorded as the second water potential drop. The second water potential drop corresponds to the interlayer water potential change between the second depth and the third depth. The difference between the fourth water potential value corresponding to the fourth depth and the third water potential value corresponding to the third depth is recorded as the third water potential drop. The third water potential drop corresponds to the interlayer water potential change between the third depth and the fourth depth. If the value of the water potential difference is negative, it indicates that the water potential value decreases along the direction of increasing depth, that is, the water potential of the deep soil is lower than that of the shallow soil. If the value of the water potential difference is positive, it indicates that the water potential value increases along the direction of increasing depth, that is, the water potential of the deep soil is higher than that of the shallow soil. This situation may occur in a scenario where the shallow soil loses water due to evaporation, while the deep soil still maintains a high water content. If the absolute value of the water potential difference is close to zero, it indicates that the soil moisture and energy state is basically uniform in adjacent depth intervals, and there is no obvious stratification of water consumption or replenishment. After calculating the water potential difference between adjacent depth sampling points, the depth interval where the water potential difference shows the largest negative jump is identified as the active water absorption layer. The largest negative jump refers to the water potential difference with the largest negative value among the first, second, and third water potential differences at the same sampling time. For each sampling time, the values ​​of the first, second, and third water potential differences are compared, and the water potential difference with the negative sign and the absolute value is selected. The depth range corresponding to the water potential difference is the layer where crop root water uptake activity is most intense at the sampling time. When the water potential value of a certain depth range is significantly lower than that of the depth ranges above and below it, it indicates that soil moisture in that depth range is being concentratedly extracted by crop roots, and the rapid drop in water potential is the driving force for water flow to the roots. The depth range where the largest negative jump occurs is identified as the water uptake active layer. The upper limit depth of the water uptake active layer is the sampling depth on the shallower side of the depth range, and the lower limit depth is the sampling depth on the deeper side of the depth range. After identifying the active water-absorbing layer, the water potential value at the midpoint depth of the active water-absorbing layer is extracted as the second feature water potential. The midpoint depth of the active water-absorbing layer refers to the middle position between the upper and lower depth limits of the depth range, and its value is equal to the sum of the upper and lower depth limits divided by two.

[0019] Irrigation decision module: Based on the first characteristic frequency and the second characteristic water potential, a coupled comparison is performed, and the comparison result is used to determine whether the irrigation triggering condition is met. If the irrigation triggering condition is met, a differentiated irrigation quota is determined for different irrigation areas.

[0020] In this embodiment of the invention, the irrigation decision module needs to be specifically described. The irrigation decision module obtains the reference frequency of leaf electrophysiological signals and the reference value of rhizosphere water potential of the target crop under normal water supply conditions. The reference frequency of leaf electrophysiological signals is the average value of the reciprocal of the peak-to-valley transition period of leaf electrophysiological signal sequences collected over several consecutive days after full irrigation. The reference value of rhizosphere water potential is the average value of the water potential at the midpoint of the active water absorption layer collected during the stable period of field water holding capacity after full irrigation. Calculate the first deviation direction and the first deviation degree of the first characteristic frequency relative to the reference frequency of the leaf electrophysiological signal. The first deviation direction is determined by the numerical relationship between the first characteristic frequency and the reference frequency, and the first deviation degree is determined by the ratio of the difference between the reference frequency and the first characteristic frequency to the reference frequency. Calculate the second deviation direction and the second deviation degree of the second characteristic water potential relative to the root zone water potential reference value. The second deviation direction is determined by the numerical relationship between the second characteristic water potential and the reference value, and the second deviation degree is determined by the ratio of the difference between the reference value and the second characteristic water potential to the reference value. When the first deviation direction is the frequency reduction direction and the second deviation direction is the water potential reduction direction, and the comparison between the first deviation degree and the second deviation degree falls into the first judgment interval corresponding to the current growth stage of the crop, it is determined that the irrigation triggering condition is met. When the irrigation triggering conditions are met, obtain the vegetation cover density parameters corresponding to different irrigation areas; For areas where the vegetation cover density is greater than the first reference value, the first irrigation quota correction coefficient is determined based on the absolute value of the frequency difference between the first characteristic frequency and the reference frequency of the leaf electrophysiological signal. The benchmark irrigation quota is multiplied by the first irrigation quota correction coefficient to obtain the increased irrigation quota. For areas where the vegetation cover density is less than the second reference value, the second irrigation quota correction coefficient is determined based on the absolute value of the difference between the root zone water potential reference value and the second characteristic water potential. The adjusted irrigation quota is obtained by multiplying the benchmark irrigation quota by the second irrigation quota correction coefficient.

[0021] It should be noted that after obtaining the reference frequency and reference value, the first deviation direction and the first deviation degree of the first characteristic frequency relative to the reference frequency of the leaf electrophysiological signal are calculated. The first deviation direction refers to whether the value of the first characteristic frequency is higher or lower than the value of the reference frequency. If the first characteristic frequency is less than the reference frequency, the deviation direction is the direction of frequency slowing down, indicating that the crop electrophysiological fluctuation rhythm has slowed down relative to the normal state. This is a typical manifestation of water stress leading to stomatal closure, reduced transpiration, and decreased cell metabolic activity at the electrophysiological level. If the first characteristic frequency is greater than the reference frequency, the deviation direction is the direction of frequency acceleration. This is usually related to the crop not yet entering water stress or being in the recovery stage after stress. The first deviation is calculated as follows: subtract the first characteristic frequency from the reference frequency to obtain the frequency difference, and calculate the ratio of the frequency difference to the reference frequency. This ratio is the first deviation. The larger the first deviation, the more significant the deviation of the crop's electrophysiological activity from the normal state. Calculate the second deviation direction and the second deviation degree of the second characteristic water potential relative to the rhizosphere water potential reference value. The second deviation direction refers to whether the value of the second characteristic water potential is higher or lower than the reference value. If the second characteristic water potential is lower than the reference value, the deviation direction is the direction of decreasing water potential, indicating that the soil water energy state in the root zone has decreased relative to the state of sufficient water supply, and the crop roots need to overcome greater soil matrix suction to absorb water. If the second characteristic water potential is greater than the reference value, the direction of deviation is the direction of water potential increase. The second degree of deviation is calculated as follows: subtract the second characteristic water potential from the reference value to obtain the water potential difference, and calculate the ratio of the water potential difference to the reference value. This ratio is the second degree of deviation. The larger the second degree of deviation, the more severe the water deficit in the root zone. After completing the calculation of the first deviation direction and the first deviation degree, and the second deviation direction and the second deviation degree, the combination of deviation direction and deviation degree is used to determine whether the irrigation triggering condition is met. When the frequency of the first deviation direction indicator decreases and the water potential of the second deviation direction indicator decreases, it indicates that the aboveground part of the crop has shown a detectable electrophysiological stress response, and at the same time, the water supply capacity of the underground root zone has clearly decreased. Further examination of the relationship between the first and second deviation degrees is conducted by numerically comparing them to determine the difference or ratio. Different parts of the crop are sensitive to water stress at different growth stages. During the vegetative growth stage, the sensitivity of leaf electrophysiological responses is higher than that of root zone water potential changes. At this time, the first deviation degree will increase significantly before the second deviation degree. During the reproductive growth stage, the decline in root zone water potential has a more direct impact on yield formation, making the change in the second deviation degree more critical. When the relationship between the first and second deviation degrees falls within the first judgment interval, the irrigation triggering condition is deemed met. When the first deviation degree exceeds the first limit and the second deviation degree exceeds the second limit, the relationship is considered to fall within the first judgment interval. During the reproductive growth stage, when the second deviation degree exceeds the third limit and the first deviation degree reaches the fourth limit, the relationship is considered to fall within the first judgment interval. The values ​​of the first, second, third, and fourth limits are all derived from the statistical distribution of the degree of deviation of crops during the same growth period in history when they experienced mild water stress and required irrigation intervention. When the comparison relationship falls into the first judgment interval, an irrigation trigger signal is output, and the irrigation quota determination process begins. After determining that the irrigation trigger conditions are met, differentiated irrigation quotas for different irrigation areas are determined.

[0022] 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 in 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. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0023] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0024] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0025] 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; that is, 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 according to actual needs.

[0026] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations 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. Therefore, the scope of protection of this application should be determined by the scope of protection of the described technical solution.

Claims

1. A smart irrigation management system based on soil and crop moisture status identification, characterized in that, include: Leaf physiological signal acquisition module: acquires leaf electrophysiological signal sequences of the target crop and multi-depth water potential parameters of the rhizosphere microdomain; Water potential parameter acquisition module: Based on the leaf electrophysiological signal sequence, determine the onset time of water stress of the crop and the corresponding first characteristic frequency; Coupling determination module: Based on the multi-depth water potential parameters, construct a vertical gradient distribution map of rhizosphere water potential and determine the active water absorption layer and its corresponding second characteristic water potential; Irrigation decision module: Based on the first characteristic frequency and the second characteristic water potential, a coupled comparison is performed, and the comparison result is used to determine whether the irrigation triggering condition is met. If the irrigation triggering condition is met, a differentiated irrigation quota is determined for different irrigation areas.

2. The intelligent irrigation management system based on soil and crop moisture status identification according to claim 1, characterized in that, The leaf physiological signal acquisition module deploys micro water potential sensing elements at the first, second, third and fourth depths of the rhizosphere micro-domain of the target crop. The first depth corresponds to the top of the shallow tillage layer where the crop's fibrous roots are mainly distributed, the second depth corresponds to the middle of the tillage layer where the density of fibrous roots is the highest, the third depth corresponds to the transition layer between fibrous roots and lateral roots, and the fourth depth corresponds to the deep soil layer where lateral roots are mainly distributed. Water potential readings at corresponding depths are collected by each micro water potential sensing element at a second sampling frequency, forming a first depth water potential sequence, a second depth water potential sequence, a third depth water potential sequence, and a fourth depth water potential sequence, which together serve as multi-depth water potential parameters.

3. The intelligent irrigation management system based on soil and crop moisture status identification according to claim 2, characterized in that, At the same sampling time, calculate the first interlayer water potential difference between the first depth water potential value and the second depth water potential value, calculate the second interlayer water potential difference between the second depth water potential value and the third depth water potential value, and calculate the third interlayer water potential difference between the third depth water potential value and the fourth depth water potential value. The interlayer water potential difference, the interlayer water potential difference, and the interlayer water potential difference are compared, and the depth range corresponding to the interlayer water potential difference with the largest value is taken as the active water absorption layer. The water potential value at the midpoint depth of the active layer is extracted, or the arithmetic mean of the water potential values ​​at the upper and lower depths of the active layer is extracted, is used as the second characteristic water potential.

4. The intelligent irrigation management system based on soil and crop moisture status identification according to claim 1, characterized in that, The water potential parameter acquisition module divides the leaf electrophysiological signal sequence into multiple continuous time segments according to the division step size. The division step size is taken as an integer multiple of the average value of the peak-valley transition cycle of the crop under normal water conditions. Adjacent time segments are connected end to end on the time axis. For each time segment, identify the effective peak and effective valley values ​​in the signal data. During identification, calculate the first-order difference sequence of the signal data. The point where the first-order difference changes from positive to negative and the change amplitude exceeds the dynamic fluctuation tolerance is taken as the effective peak value. The point where the first-order difference changes from negative to positive and the change amplitude exceeds the dynamic fluctuation tolerance is taken as the effective valley value. The dynamic fluctuation tolerance is a fixed percentage of the standard deviation of the signal data within the corresponding time segment.

5. A smart irrigation management system based on soil and crop moisture status identification according to claim 4, characterized in that, The time interval between the effective peak value and the subsequent effective trough value is recorded as the first half-cycle, and the time interval between the effective trough value and the subsequent effective peak value is recorded as the second half-cycle. The sum of the first half-cycle and the second half-cycle is a peak-trough transition cycle. The arithmetic mean of all peak-trough transition cycles within a time segment is taken as the representative value of the peak-trough transition cycle of that time segment. Based on the chronological order of time segments, the difference between the representative value of the peak-valley transition period of the later time segment and the representative value of the peak-valley transition period of the previous time segment is calculated as the extended increment. When multiple consecutive extension increments are all positive and exceed the judgment reference range determined by the statistical distribution of extension increments during the stable period, the starting point of the time segment corresponding to the first extension increment among the multiple consecutive extension increments is marked as the start time of water stress. The reciprocal of the peak-valley transition period of the time segment in which the water stress begins is determined as the first characteristic frequency.

6. The intelligent irrigation management system based on soil and crop moisture status identification according to claim 1, characterized in that, The coupling determination module acquires multi-depth water potential parameters, which include water potential values ​​collected at the first, second, third, and fourth depths of the rhizosphere microdomain of the target crop. At the same sampling time, the water potential value at the first depth is taken as the first water potential value, the water potential value at the second depth is taken as the second water potential value, the water potential value at the third depth is taken as the third water potential value, and the water potential value at the fourth depth is taken as the fourth water potential value. The first water potential value, the second water potential value, the third water potential value and the fourth water potential value are connected in order from shallow to deep to form a data chain of water potential change with depth. The difference between the second water potential value and the first water potential value is calculated as the first water potential drop; the difference between the third water potential value and the second water potential value is calculated as the second water potential drop; and the difference between the fourth water potential value and the third water potential value is calculated as the third water potential drop. By comparing the values ​​of the first, second, and third water potential drops, the depth range corresponding to the water potential drop with the largest negative sign and the largest absolute value is identified as the active water-absorbing layer.

7. A smart irrigation management system based on soil and crop moisture status identification according to claim 6, characterized in that, Determine the upper and lower limits of the active water-absorbing layer, calculate the arithmetic mean of the upper and lower limits as the midpoint depth, perform linear interpolation between the water potential value corresponding to the upper and lower limits to obtain the water potential value at the midpoint depth, and use the water potential value at the midpoint depth as the second characteristic water potential.

8. The intelligent irrigation management system based on soil and crop moisture status identification according to claim 1, characterized in that, The irrigation decision module obtains the reference frequency of leaf electrophysiological signals and the reference value of rhizosphere water potential of the target crop under normal water supply conditions. The reference frequency of leaf electrophysiological signals is the average value of the leaf electrophysiological signal sequence collected over several consecutive days after full irrigation and the reciprocal of the peak-to-valley transition period of the corresponding time period. The reference value of rhizosphere water potential is the average value of the water potential at the midpoint of the active water absorption layer collected during the stable period of field water holding capacity after full irrigation.

9. A smart irrigation management system based on soil and crop moisture status identification according to claim 8, characterized in that, Calculate the first deviation direction and the first deviation degree of the first characteristic frequency relative to the reference frequency of the leaf electrophysiological signal. The first deviation direction is determined by the numerical relationship between the first characteristic frequency and the reference frequency, and the first deviation degree is determined by the ratio of the difference between the reference frequency and the first characteristic frequency to the reference frequency. Calculate the second deviation direction and the second deviation degree of the second characteristic water potential relative to the root zone water potential reference value. The second deviation direction is determined by the numerical relationship between the second characteristic water potential and the reference value, and the second deviation degree is determined by the ratio of the difference between the reference value and the second characteristic water potential to the reference value. When the first deviation direction is the direction of frequency reduction and the second deviation direction is the direction of water potential reduction, and the comparison between the degree of the first deviation and the degree of the second deviation falls into the first judgment interval corresponding to the current growth stage of the crop, the irrigation triggering condition is determined to be met.

10. A smart irrigation management system based on soil and crop moisture status identification according to claim 9, characterized in that, When the irrigation triggering conditions are met, obtain the vegetation cover density parameters corresponding to different irrigation areas; For areas where the vegetation cover density is greater than the first reference value, the first irrigation quota correction coefficient is determined based on the absolute value of the frequency difference between the first characteristic frequency and the reference frequency of the leaf electrophysiological signal. The benchmark irrigation quota is multiplied by the first irrigation quota correction coefficient to obtain the increased irrigation quota. For areas where the vegetation cover density is less than the second reference value, the second irrigation quota correction coefficient is determined based on the absolute value of the difference between the root zone water potential reference value and the second characteristic water potential. The adjusted irrigation quota is obtained by multiplying the benchmark irrigation quota by the second irrigation quota correction coefficient.

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