Partitioned variable sprinkling irrigation system based on soil dielectric constant real-time atlas

Abstract

The invention discloses a partitioned variable sprinkling irrigation system based on a soil dielectric constant real-time map. The partitioned variable sprinkling irrigation system comprises a sprinkling irrigation machine, a central controller, and an optimized soil moisture sensor network, a fixed canopy temperature sensor, a canopy temperature sensor network, an automatic weather station and a variable execution mechanism which are respectively in communication connection with the central controller, a soil dielectric constant sensor array is arranged outside the sprinkling machine and is used for periodically acquiring a real-time atlas of the dielectric constant of soil covering the whole field in the moving process of the sprinkling machine; the central controller is internally provided with a dynamic partition and decision module, and the dynamic partition and decision module divides dynamic comprehensive moisture management partitions, generates a variable irrigation prescription map and sends the variable irrigation prescription map to the variable execution mechanism; the dynamic comprehensive moisture management subarea is divided based on the static water holding characteristic subarea of the soil texture and the real-time soil moisture space distribution based on the soil dielectric constant real-time atlas.

Description

Technical Field

[0001] This invention relates to the field of irrigation technology, and in particular to a zoned variable sprinkler irrigation system based on a real-time spectrum of soil dielectric constant. Background Technology

[0002] The development of modern precision agriculture urgently requires scientific irrigation management methods. By real-time monitoring of the dynamic changes in soil dielectric constant to generate high-precision maps of soil moisture, salinity, and structural parameters, and then combining crop water requirements with soil characteristic differences, precise decision-making for zoned variable sprinkler irrigation can be achieved. Utilizing the strong correlation between soil dielectric constant and physical properties such as moisture content, salinity concentration, and porosity, a real-time feedback mechanism can be constructed to dynamically adjust the irrigation volume in each area. This avoids water waste, soil compaction, and nutrient loss caused by over-irrigation, while preventing crop growth stagnation caused by insufficient irrigation. Ultimately, through scientific decision-making, integrated water and fertilizer management can be optimized, improving water resource utilization efficiency, reducing agricultural non-point source pollution, and promoting healthy crop growth and improved yield and quality. This has significant implications for sustainable agricultural development, aligning with the modern agricultural development concepts of prioritizing water conservation and green ecology. Furthermore, by controlling zoned variables, it can adapt to the differentiated needs of different soil types and crop water requirements, achieving a leapfrog transformation from "experience-based irrigation" to "data-driven irrigation." This will promote the intelligent, precise, and resource-saving development of agricultural production, providing key technical support for alleviating water shortage pressures, ensuring food security, and promoting ecological civilization.

[0003] Existing technologies rely solely on discrete soil sampling and historical texture for static zoning. This static zoning cannot capture the real-time spatial redistribution of soil moisture after irrigation and rainfall, resulting in a disconnect between zoning boundaries and actual moisture conditions, leading to delayed decision-making. Therefore, a zoning variable sprinkler irrigation system based on real-time soil dielectric constant maps is proposed. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a zoned variable sprinkler irrigation system based on real-time soil dielectric constant maps.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A zoned variable sprinkler irrigation system based on real-time soil dielectric constant maps includes a sprinkler, a central controller, and an optimized soil moisture sensor network, a fixed canopy temperature sensor, a canopy temperature sensor network, an automatic weather station, and a variable actuator, all of which are communicatively connected to the central controller. The sprinkler is equipped with a soil dielectric constant sensor array on its exterior. The soil dielectric constant sensor array is connected to the central controller and is used to periodically acquire real-time soil dielectric constant maps covering the entire field during the movement of the sprinkler. The central controller is equipped with a dynamic partitioning and decision-making module. This module receives and integrates the real-time soil dielectric constant map, the data from the optimized soil moisture sensor network, the data from the canopy temperature sensor network, and the meteorological data provided by the automatic weather station. Based on the integrated data, the module dynamically divides the dynamic integrated water management zones and generates a variable irrigation prescription map, which is then sent to the variable execution mechanism. The division of the dynamic integrated water management zones is based on the static water-holding characteristics of the soil texture and the real-time spatial distribution of soil moisture based on the real-time soil dielectric constant map.

[0006] The above technical solution further includes: Furthermore, the soil dielectric constant sensor array is formed by multiple multi-band microwave reflectometers arranged at preset intervals along the truss direction of the sprinkler irrigation machine, and the soil dielectric constant sensor array and the sensors of the canopy temperature sensor network are arranged correspondingly or alternately in spatial position to achieve coordinated acquisition of soil dielectric properties and canopy temperature at the same spatial location.

[0007] Furthermore, the soil dielectric constant sensor array is equipped with a unified power supply and data acquisition synchronization controller. The controller enables all sensors in the array to acquire data under the same time reference and associates and binds them with the real-time position and attitude information of the sprinkler machine through the central controller. During the movement of the sprinkler machine to perform irrigation or special surveying operations, the soil dielectric constant sensor array is activated. Each sensor in the soil dielectric constant sensor array periodically emits electromagnetic wave signals to the ground surface and receives reflected signals at a preset acquisition frequency to obtain raw voltage or phase data reflecting the dielectric properties of the soil. At the same time, the geographical coordinates of the sprinkler machine corresponding to each acquisition are recorded. The acquired raw voltage or phase data is transmitted to the central controller. In the central controller, the raw voltage or phase data is first filtered to eliminate environmental noise and mechanical vibration interference. Then, using the sensor-specific calibration model, the preprocessed data is inverted into the soil dielectric constant value of the corresponding measurement point. Each data point is accompanied by its corresponding geographic coordinates, forming a discrete dielectric constant point dataset with geographic information. Based on the discrete dielectric constant point dataset with geographic information, a spatial interpolation algorithm is used to spatially estimate the soil dielectric constant across the entire field, generating a rasterized real-time soil dielectric constant map covering the entire sprinkler control area. This map represents the spatial distribution differences of soil dielectric constant within the field in the form of an image or a digital matrix.

[0008] The generated real-time soil dielectric constant map is compared and calibrated with measured soil moisture data from points located in different soil texture areas acquired by the optimized soil moisture sensor network during the same period. The map is verified and corrected as necessary by establishing a regional dielectric constant-soil volumetric water content relationship model. Finally, the calibrated real-time soil dielectric constant map is output to the dynamic zoning and decision module for dynamic irrigation decision-making.

[0009] Furthermore, the dynamic zoning and decision-making module divides static management zones based on historical soil particle size data to characterize differences in soil water-holding capacity. Then, it uses a spatiotemporal clustering algorithm to process the continuous real-time soil dielectric constant spectrum data to identify dynamic regions with similar soil moisture conditions. Finally, it spatially superimposes and reclassifies the static management zones and the dynamic regions to form a dynamic integrated water management zoning for guiding current irrigation decisions.

[0010] Furthermore, in the irrigation decision-making process before the crop jointing stage, the dynamic zoning and decision-making module prioritizes the spatial distribution of soil volumetric water content obtained by inverting the real-time soil dielectric constant map, and determines the irrigation triggering conditions and calculates the irrigation amount within the dynamic integrated water management zone. The calculation of the irrigation amount integrates the field water holding capacity information provided by the static management zone, the real-time soil water content, and the crop evapotranspiration from the short-term weather forecast.

[0011] Furthermore, in the irrigation decision-making process after the crop jointing stage, the dynamic zoning and decision-making module first performs a weighted fusion of the normalized relative canopy temperature index calculated based on canopy temperature data and the soil moisture deficit index calculated based on the real-time soil dielectric constant spectrum to generate a comprehensive crop water stress index. Then, based on the comprehensive crop water stress index, it further delineates sub-zones with different irrigation priorities within the dynamic comprehensive water management zoning, and dynamically calculates the variable irrigation quota for each sub-zone by integrating the real-time soil moisture deficit and the future rainfall probability forecast.

[0012] Furthermore, when making irrigation decisions under semi-arid climate conditions, the dynamic zoning and decision-making module assigns a higher weight to the normalized relative canopy temperature index during the generation of the crop comprehensive water stress index. When making irrigation decisions under semi-humid climate conditions, the dynamic zoning and decision-making module assigns a higher weight to the soil moisture deficit index to ensure the reliability of the decision-making basis for adaptive adjustment according to different climate conditions.

[0013] Furthermore, the dynamic zoning and decision-making module integrates a data assimilation model, which couples the real-time soil dielectric constant spectrum, canopy temperature data, meteorological monitoring data, and crop growth model to simulate and predict the dynamic changes in soil moisture in the crop root zone in real time. The prediction results are then fed back to optimize the boundaries of the dynamic integrated water management zoning and the timing and amount of the next irrigation decision.

[0014] Furthermore, the variable actuator is installed on the solenoid valves and flow regulators of each sprinkler head of the sprinkler machine. The variable actuator receives the variable irrigation prescription map and controls the opening and closing time and water flow of each sprinkler head above different dynamic integrated water management zones to achieve precise variable irrigation of water volume.

[0015] The present invention has the following beneficial effects: In this invention, the understanding of field water heterogeneity is periodically updated based on the real-time spectrum of soil dielectric constant. This enables the dynamic integrated water management zoning used for irrigation decisions to reflect the real spatial distribution of current soil moisture in real time, ensuring that the zoning on which irrigation instructions are based is always synchronized with the latest field conditions, thereby greatly improving the targeting and accuracy of variable irrigation. Attached Figure Description

[0016] Figure 1 This is a system block diagram of a zoned variable sprinkler irrigation system based on real-time soil dielectric constant spectrum proposed in this invention. Detailed Implementation

[0017] 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.

[0018] Please see Figure 1 As shown, the present invention is a zoned variable sprinkler irrigation system based on real-time soil dielectric constant spectrum, including a sprinkler, a central controller, and an optimized soil moisture sensor network, a fixed canopy temperature sensor, a canopy temperature sensor network, an automatic weather station, and a variable actuator, all of which are respectively connected to the central controller in communication. The sprinkler is equipped with a soil dielectric constant sensor array on its exterior. The soil dielectric constant sensor array is connected to the central controller and is used to periodically acquire real-time soil dielectric constant maps covering the entire field during the movement of the sprinkler. The central controller is equipped with a dynamic partitioning and decision-making module. This module receives and integrates the real-time soil dielectric constant map, the data from the optimized soil moisture sensor network, the data from the canopy temperature sensor network, and the meteorological data provided by the automatic weather station. Based on the integrated data, the module dynamically divides the dynamic integrated water management zones and generates a variable irrigation prescription map, which is then sent to the variable execution mechanism. The division of the dynamic integrated water management zones is based on the static water-holding characteristics of the soil texture and the real-time spatial distribution of soil moisture based on the real-time soil dielectric constant map.

[0019] In one embodiment, the soil dielectric constant sensor array is formed by multiple multi-band microwave reflectometers arranged at preset intervals along the truss direction of the sprinkler irrigation machine, and the soil dielectric constant sensor array and the sensors of the canopy temperature sensor network are arranged correspondingly or alternately in spatial position to achieve coordinated acquisition of soil dielectric properties and canopy temperature at the same spatial location.

[0020] In one embodiment, the soil dielectric constant sensor array is configured with a unified power supply and data acquisition synchronization controller. The controller enables all sensors in the array to acquire data at the same time reference and associates and binds them with the real-time position and attitude information of the sprinkler through a central controller. During the movement of the sprinkler machine to perform irrigation or special surveying operations, the soil dielectric constant sensor array is activated. Each sensor in the soil dielectric constant sensor array periodically emits electromagnetic wave signals to the ground surface and receives reflected signals at a preset acquisition frequency to obtain raw voltage or phase data reflecting the dielectric properties of the soil. At the same time, the geographical coordinates of the sprinkler machine corresponding to each acquisition are recorded. The acquired raw voltage or phase data is transmitted to the central controller. In the central controller, the raw voltage or phase data is first filtered to eliminate environmental noise and mechanical vibration interference. Then, using the sensor-specific calibration model, the preprocessed data is inverted into the soil dielectric constant value of the corresponding measurement point. Each data point is accompanied by its corresponding geographic coordinates, forming a discrete dielectric constant point dataset with geographic information. Based on the discrete dielectric constant point dataset with geographic information, a spatial interpolation algorithm is used to spatially estimate the soil dielectric constant across the entire field, generating a rasterized real-time soil dielectric constant map covering the entire sprinkler control area. This map represents the spatial distribution differences of soil dielectric constant within the field in the form of an image or a digital matrix.

[0021] The generated real-time soil dielectric constant map is compared and calibrated with measured soil moisture data from points located in different soil texture areas acquired by the optimized soil moisture sensor network during the same period. The map is verified and corrected as necessary by establishing a regional dielectric constant-soil volumetric water content relationship model. Finally, the calibrated real-time soil dielectric constant map is output to the dynamic zoning and decision module for dynamic irrigation decision-making.

[0022] In one embodiment, the dynamic zoning and decision-making module divides static management zones based on historical soil particle size data to characterize differences in soil water-holding capacity. Then, it uses a spatiotemporal clustering algorithm to process the continuous real-time soil dielectric constant spectrum data to identify dynamic regions with similar soil moisture conditions. Finally, it spatially superimposes and reclassifies the static management zones and the dynamic regions to form a dynamic integrated water management zoning for guiding current irrigation decisions.

[0023] It should be noted that the specific analysis process for the irrigation management zoning in the dynamic zoning and decision-making module is as follows: Static management zoning is established based on soil particle size composition data obtained from historical soil sampling. The available soil water at each sampling point is calculated, and the spatial distribution of available soil water in the entire field is divided into zones using the natural breakpoint method, forming static management zoning that characterizes the spatial heterogeneity of soil water holding capacity. Dynamic moisture region identification involves receiving real-time soil dielectric constant maps periodically acquired by an airborne dielectric constant sensor array, and using a temporal clustering algorithm to perform spatiotemporal analysis on the real-time soil dielectric constant map data from multiple consecutive periods. This identifies regions where the soil dielectric properties change trends and spatial patterns are similar, thereby dividing several dynamic regions with similar soil moisture conditions. More specifically, processing using temporal clustering algorithms includes: First, the real-time soil dielectric constant map for each period is gridded, and the time series dielectric constant data of each grid cell is extracted. Then, a clustering algorithm based on dynamic time regularization distance or Euclidean distance is used to perform similarity measurement and clustering analysis on the time series data of all grid units, and grid units with similar water dynamic change patterns are grouped into the same dynamic region with similar soil moisture conditions. The rule for attribute reclassification and region merging is: assign a composite code to each polygonal sub-region generated by superposition. This composite code is composed of the static management zoning level to which it belongs and the dynamic water similarity region number. Subsequently, polygonal sub-regions with the same composite code and spatial adjacency are merged to form the final dynamic integrated water management zoning unit. Each zoning unit is associated with both static water holding capacity attribute and dynamic water status attribute. The dynamic integrated water management zoning is generated by performing a geographic information system spatial overlay analysis on the vector boundary data of the static management zoning and the vector boundary data of the dynamic region with similar soil moisture conditions. The multiple polygonal sub-regions generated after overlay are reclassified and merged according to their static management category and dynamic water similarity category, and finally a dynamic integrated water management zoning is generated to uniformly guide the current variable irrigation decision.

[0024] In one embodiment, during irrigation decisions before the crop jointing stage, the dynamic zoning and decision-making module prioritizes the spatial distribution of soil volumetric water content obtained by inverting the real-time soil dielectric constant map, determines the irrigation triggering conditions and calculates the irrigation amount within the dynamic integrated water management zone. The calculation of the irrigation amount integrates the field water holding capacity information provided by the static management zone, the real-time soil moisture content, and the crop evapotranspiration predicted by the short-term weather forecast.

[0025] It should be noted that the specific analytical process for constructing irrigation decisions before the jointing stage is as follows: The soil dielectric constant real-time spectrum collected by the dielectric constant sensor array is periodically acquired and input, and short-term weather forecast data for the future preset period from the automatic weather station is acquired at the same time. The forecast data includes at least the reference crop evapotranspiration ET0 forecast value. The real-time soil dielectric constant spectrum was converted into a spatial distribution spectrum of soil volumetric water content. For the current dynamic integrated water management zones, the data falling into each zone in the spatial distribution map of soil volumetric water content are spatially statistically analyzed to calculate the average real-time soil water content of each zone. The average real-time soil moisture content of each partition is compared with the preset irrigation trigger threshold of that partition. When the average real-time soil moisture content of any one or more partitions reaches or falls below its corresponding irrigation trigger threshold, the irrigation decision process is triggered. For each zone that triggers irrigation, the parameters characterizing soil water holding capacity, including field water holding capacity, are obtained based on the static management zone type corresponding to that zone. Combined with the average real-time soil moisture content of that zone and the forecasted crop evapotranspiration calculated based on short-term weather forecast data for a future preset period, the current variable irrigation quota for that zone is determined through the irrigation amount calculation model. Based on the spatial range of all triggered irrigation zones and the calculated variable irrigation quotas, a variable irrigation prescription map suitable for the current irrigation cycle is generated and sent to the variable execution mechanism to control the sprinkler to perform variable irrigation operations.

[0026] In one embodiment, during irrigation decisions after the crop jointing stage, the dynamic zoning and decision-making module first weights and fuses the normalized relative canopy temperature index calculated based on canopy temperature data with the soil moisture deficit index calculated based on the real-time soil dielectric constant spectrum to generate a comprehensive crop water stress index. Then, based on the comprehensive crop water stress index, it further delineates sub-zones with different irrigation priorities within the dynamic comprehensive water management zoning, and dynamically calculates the variable irrigation quota for each sub-zone by integrating the real-time soil moisture deficit and future rainfall probability forecast.

[0027] It should be noted that the specific analytical process for calculating the variable irrigation quotas for each sub-region is as follows: Simultaneously acquire canopy temperature data collected by the airborne canopy temperature sensor network, and real-time soil dielectric constant spectrum acquired by the airborne dielectric constant sensor array; The normalized relative canopy temperature index is calculated based on the canopy temperature data. At the same time, the soil volumetric water content is inverted based on the real-time soil dielectric constant spectrum and the soil water deficit index is further calculated. Subsequently, based on the system's preset climate model configuration or user input, the normalized relative canopy temperature index and the soil moisture deficit index are assigned fusion weights respectively, and the two are fused through a weighted summation algorithm to generate a spatial distribution map of the crop comprehensive water stress index covering the entire field. Next, based on the spatial distribution map of the crop integrated water stress index, within the existing dynamic integrated water management zoning, a threshold segmentation method is used to further delineate high irrigation priority sub-regions, medium irrigation priority sub-regions, and low irrigation priority sub-regions. Finally, for each defined irrigation priority sub-region, the real-time soil moisture deficit is read, and the rainfall probability forecast data for the future preset time period is queried. The soil moisture deficit, the rainfall probability forecast data, and the average value of the crop comprehensive water stress index corresponding to the sub-region are dynamically integrated through the irrigation amount calculation model to calculate the variable irrigation quota for the current sub-region.

[0028] In one embodiment, when making irrigation decisions for semi-arid climate conditions, the dynamic zoning and decision-making module assigns a higher weight to the normalized relative canopy temperature index during the generation of the crop integrated water stress index. When making irrigation decisions under semi-humid climate conditions, the dynamic zoning and decision-making module assigns a higher weight to the soil moisture deficit index to ensure the reliability of the decision-making basis for adaptive adjustment according to different climate conditions.

[0029] It should be noted that the specific analytical process for adaptively adjusting decisions based on different climatic conditions is as follows: Based on the long-term historical meteorological data or short-term weather forecast data provided by the automatic weather station, the field climate type at the current season or decision-making time is determined to be semi-arid or semi-humid. Simultaneously, canopy temperature data collected by the canopy temperature sensor network and transformed over time, as well as spatial distribution data of soil volumetric water content collected and retrieved by the dielectric constant sensor array, are acquired. Based on the acquired data, two core diagnostic indices are calculated in parallel: First, calculate the normalized relative canopy temperature index for the entire field based on canopy temperature data; Second, based on the soil volumetric water content data and the soil characteristic parameters provided by the static management zoning, the soil water deficit index, which reflects the degree of soil water deficit, is calculated. If the system determines that the current climate conditions are semi-arid, it will automatically adopt the first weight configuration scheme, that is, assign a higher weight value to the normalized relative canopy temperature index and a relatively lower weight value to the soil moisture deficit index. Then, the two indices will be merged into a unified comprehensive crop water stress index through weighted average or primary and secondary decision logic. If the system determines that the current climate conditions are semi-humid, the module will automatically switch to the second weight configuration scheme, that is, assign a higher weight value to the soil moisture deficit index and reduce the weight of the normalized relative canopy temperature index, and then generate the crop comprehensive water stress index in the same fusion method. The dynamic zoning and decision-making module utilizes the spatial distribution data of the generated crop integrated water stress index to further identify subtle differences in the degree of water stress within the existing dynamic integrated water management zoning. Based on this, it fine-tunes the priority and amount of irrigation, ultimately forming a variable irrigation prescription map that integrates climate adaptive diagnostic results.

[0030] In one embodiment, the dynamic zoning and decision-making module integrates a data assimilation model. The data assimilation model couples the real-time soil dielectric constant spectrum, canopy temperature data, meteorological monitoring data, and crop growth model to simulate and predict the dynamic changes in soil moisture in the crop root zone in real time. The prediction results are then fed back to optimize the boundaries of the dynamic integrated water management zoning and the timing and amount of the next irrigation decision.

[0031] It should be noted that the specific analytical process for real-time simulation and prediction of the dynamic changes in crop root zone soil moisture is as follows: The central controller receives raw dielectric constant signals from the dielectric constant sensor array, raw temperature data from the canopy temperature sensor network and the canopy temperature sensor, and meteorological monitoring data from the automatic weather station; First, all the above data are synchronized in time and matched in spatial location to ensure that different types of data at the same time and in the same field location can be aligned. Next, the soil dielectric constant signal was transformed into spatial distribution data of soil volumetric water content using an inversion algorithm based on the calibration curve. For canopy temperature data, continuous observations from canopy temperature sensors are used as a reference. The time-scale conversion method is employed to uniformly correct the scattered canopy temperature values ​​from moving observations to the temperature values ​​at the daily reference time. Quality control and interpolation are performed on meteorological monitoring data to form a continuous standard meteorological data sequence; In the data assimilation model, a mechanistic or semi-mechanistic crop growth model (DSSAT) suitable for field crops is selected or constructed. Using historical irrigation records of the field plots, crop variety information, sowing dates, and pre-treated multi-period soil moisture content and canopy temperature data, the key parameters in the crop growth model are localized and calibrated and initialized. These parameters include, but are not limited to, crop coefficient, root water uptake parameters, and soil hydraulic parameters. Based on the crop growth model state vector of the previous moment (including soil moisture content, crop biomass, leaf area index, etc.), a model state set containing dozens to hundreds of members is generated. Then, each member model is driven forward for a short time step (e.g., 1 hour) to obtain the set of model state predictions at the end of that step. Next, the newly acquired and preprocessed soil dielectric constant real-time spectrum inverted soil moisture spatial data, and the canopy temperature data transformed by time scale, are used as the real observation values ​​at that moment. The covariance between the model prediction set and the real observation values ​​is calculated by the ensemble Kalman filter algorithm, and then each model state prediction member is optimally adjusted and corrected. Finally, we obtain the model state analysis set that has been assimilated and corrected and best reflects the current field conditions. This analysis set includes optimized, spatially continuous information on crop root zone soil moisture dynamics and crop growth status. Using the assimilated and corrected model state analysis set as a new initial field, the crop growth model is driven to perform deterministic or probabilistic forecasting of the future in the short term. During the operation, real-time monitoring data from automatic weather stations and short-term numerical weather forecast data will be integrated. The obtained dynamic soil moisture prediction results are used as core feedback information and input into the decision logic of the dynamic zoning and decision module: First, to optimize the boundaries of the dynamic integrated water management zoning: the predicted soil moisture spatial distribution map for future key periods (such as before the next planned irrigation) is overlaid and compared with the current dynamic integrated water management zoning. If the prediction shows that the water heterogeneity within the current zoning will increase significantly, or the predicted water conditions between different zoning tend to be consistent, the zoning re-division mechanism is triggered. Using the predicted data as one of the inputs, the spatial clustering algorithm is run again to generate an updated and more forward-looking management zoning map. Secondly, to optimize the timing and amount of water for the next irrigation decision: the predicted soil moisture consumption curves and crop water stress index of each zone are compared with the preset irrigation trigger threshold, and the recommended irrigation start time is dynamically adjusted. At the same time, combined with the predicted crop evapotranspiration and possible effective rainfall, the recommended irrigation amount for each updated dynamic integrated water management zone is calculated and optimized on a rolling basis with the goal of ensuring that the soil moisture at the end of the predicted irrigation cycle is not lower than the crop growth limit. This optimized timing and amount of water information is then integrated into the variable irrigation prescription map to be generated in the next cycle.

[0032] In one embodiment, the variable actuator is installed on the solenoid valve and flow regulator of each sprinkler head of the sprinkler machine. The variable actuator receives the variable irrigation prescription map and controls the opening and closing time and water flow rate of each sprinkler head above different dynamic integrated water management zones to achieve precise variable irrigation of water volume.

[0033] All data obtained in this invention has been authorized by the user.

[0034] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A zoned variable irrigation system based on real-time soil dielectric constant maps, characterized in that, The system includes a sprinkler irrigation machine, a central controller, and an optimized soil moisture sensor network, a fixed canopy temperature sensor, a canopy temperature sensor network, an automatic weather station, and a variable actuator, all of which are communicatively connected to the central controller. The canopy temperature sensor network is installed in the sprinkler irrigation machine. The sprinkler is equipped with a soil dielectric constant sensor array on its exterior. The soil dielectric constant sensor array is connected to the central controller and is used to periodically acquire real-time soil dielectric constant maps covering the entire field during the movement of the sprinkler. The central controller is equipped with a dynamic zoning and decision-making module. The dynamic zoning and decision-making module receives and integrates the real-time soil dielectric constant spectrum, the data from the optimized soil moisture sensor network, the data from the canopy temperature sensor network, and the meteorological data provided by the automatic weather station. The dynamic zoning and decision-making module divides the dynamic integrated water management zones and generates variable irrigation prescription maps, which are then sent to the variable execution mechanism. The division of the dynamic integrated water management zones is based on the static water-holding characteristics of the soil texture and the real-time spatial distribution of soil moisture based on the real-time soil dielectric constant spectrum.

2. The zoned variable irrigation system based on real-time soil dielectric constant spectrum according to claim 1, characterized in that, The soil dielectric constant sensor array is formed by multiple multi-band microwave reflectors arranged at preset intervals along the truss direction of the sprinkler irrigation machine, and the soil dielectric constant sensor array and the sensors of the canopy temperature sensor network are arranged correspondingly or alternately in spatial position.

3. A zoned variable irrigation system based on real-time soil dielectric constant spectrum as described in claim 1, characterized in that, The soil dielectric constant sensor array is equipped with a unified power supply and data acquisition synchronization controller. The controller enables all sensors in the array to acquire data at the same time reference and associates and binds them with the real-time position and attitude information of the sprinkler through the central controller. During the movement of the sprinkler machine to perform irrigation or special surveying operations, the soil dielectric constant sensor array is activated. Each sensor in the soil dielectric constant sensor array periodically emits electromagnetic wave signals to the ground surface and receives reflected signals at a preset acquisition frequency to obtain raw voltage or phase data reflecting the dielectric properties of the soil. At the same time, the geographical coordinates of the sprinkler machine corresponding to each acquisition are recorded. The acquired raw voltage or phase data is transmitted to the central controller. In the central controller, the raw voltage or phase data is first filtered, and then the preprocessed data is inverted into the soil dielectric constant value of the corresponding measurement point. Each data point is accompanied by its corresponding geographical coordinates, forming a discrete dielectric constant point dataset with geographical information. Based on the discrete dielectric constant point dataset with geographic information, the soil dielectric constant is spatially estimated across the entire field, generating a real-time soil dielectric constant map covering the entire sprinkler control area.

4. A zoned variable irrigation system based on real-time soil dielectric constant spectrum as described in claim 1, characterized in that, The dynamic zoning and decision-making module divides static management zones based on historical soil particle size data to characterize differences in soil water-holding capacity. Then, it processes the continuous real-time soil dielectric constant spectrum data to identify dynamic regions with similar soil moisture conditions. Finally, it spatially overlays and reclassifies the static management zones and the dynamic regions to form a dynamic integrated water management zoning for guiding current irrigation decisions.

5. A zoned variable irrigation system based on real-time soil dielectric constant spectrum as described in claim 4, characterized in that, In the irrigation decision-making process before the crop jointing stage, the dynamic zoning and decision-making module prioritizes the spatial distribution of soil volumetric water content obtained by inverting the real-time soil dielectric constant spectrum, and determines the irrigation triggering conditions and calculates the irrigation amount within the dynamic integrated water management zone.

6. A zoned variable irrigation system based on real-time soil dielectric constant spectrum according to claim 4, characterized in that, In irrigation decision-making after the crop jointing stage, the dynamic zoning and decision-making module first weights and fuses the normalized relative canopy temperature index calculated based on canopy temperature data and the soil moisture deficit index calculated based on the real-time soil dielectric constant spectrum to generate a comprehensive crop water stress index. Then, based on the comprehensive crop water stress index, it further delineates sub-zones with different irrigation priorities within the dynamic comprehensive water management zone, and dynamically calculates the variable irrigation quota for each sub-zone by integrating the real-time soil moisture deficit and the future rainfall probability forecast.

7. A zoned variable irrigation system based on real-time soil dielectric constant spectrum as described in claim 6, characterized in that, When making irrigation decisions under semi-arid climate conditions, the dynamic zoning and decision-making module assigns a higher weight to the normalized relative canopy temperature index during the generation of the crop comprehensive water stress index. When making irrigation decisions for semi-humid climate conditions, the dynamic zoning and decision-making module assigns a higher weight to the soil moisture deficit index.

8. A zoned variable irrigation system based on real-time soil dielectric constant spectrum as described in claim 1, characterized in that, The dynamic zoning and decision-making module integrates a data assimilation model, which couples the real-time soil dielectric constant spectrum, canopy temperature data, meteorological monitoring data, and crop growth model to simulate and predict the dynamic changes in soil moisture in the crop root zone in real time. The prediction results are then fed back to optimize the boundaries of the dynamic integrated water management zoning and the timing and amount of the next irrigation decision.

9. A zoned variable irrigation system based on real-time soil dielectric constant spectrum according to claim 1, characterized in that, The variable actuator is installed on the solenoid valves and flow regulators of each sprinkler head of the sprinkler machine. The variable actuator receives the variable irrigation prescription map and controls the opening and closing time and water flow rate of each sprinkler head above different dynamic integrated water management zones.

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