Farmland soil moisture content prediction and irrigation method

Abstract

The invention provides a farmland soil moisture content prediction and irrigation method, relates to the technical field of irrigation, and aims to solve the technical problem of poor soil moisture content prediction and irrigation strategy effects in related technologies. The method comprises the following steps: acquiring multi-source data, wherein the multi-source data comprises one or more of soil sensor data, remote sensing data, meteorological data and crop parameters; acquiring a first soil moisture content based on the multi-source data, wherein the first soil moisture content is used for indicating the content of moisture in the current soil; predicting a second soil moisture content according to the first soil moisture content and the multi-source data, wherein the second soil moisture content is used for indicating the content of moisture in the soil in a certain period of time in the future; when the second soil moisture content is smaller than the preset soil moisture content, an irrigation instruction is generated based on the second soil moisture content and the target soil moisture content, the farmland irrigation method performs irrigation according to the irrigation instruction, and the irrigation instruction comprises irrigation single time and irrigation interval time.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of irrigation technology, and in particular to a method for predicting soil moisture conditions and irrigation in farmland. Background Art

[0002] In traditional farmland irrigation management, irrigation decisions mostly rely on manual experience or static models based on fixed thresholds, lacking the ability to respond to dynamic changes in soil moisture and environmental conditions in real time. Existing technologies usually use a single sensor to monitor surface soil moisture content, but due to limitations in sensor accuracy, salt interference, and deep water penetration characteristics, it is difficult to accurately reflect the actual water demand of the crop root layer. In addition, the impact of meteorological factors (such as rainfall and evaporation) and crop growth stages has not been systematically integrated, resulting in inaccurate control of irrigation timing and water volume, which can easily lead to water resource waste or crop water stress. At the same time, with the growth of the global population and the intensification of climate change, agricultural production is facing increasingly severe challenges. In farmland irrigation management, how to accurately predict soil moisture (i.e., the content and state of water in the soil) and formulate reasonable irrigation strategies based on this is the key to achieving water-saving irrigation and increasing crop yields. Summary of the invention

[0003] The embodiment of the present application provides a method for predicting soil moisture content and irrigation in farmland, which is used to improve the technical problem of poor effects of soil moisture prediction and irrigation strategies in related technologies.

[0004] To achieve the above objectives, the embodiments of the present application adopt the following technical solutions: The present application provides a method for predicting and irrigating farmland soil moisture, which is applied to a farmland irrigation method. The method comprises: acquiring multi-source data, wherein the multi-source data comprises one or more of soil sensor data, remote sensing data, meteorological data and crop parameters; acquiring a first soil moisture condition based on the multi-source data, wherein the first soil moisture condition is used to indicate the current moisture content in the soil; predicting a second soil moisture condition based on the first soil moisture condition and the multi-source data, wherein the second soil moisture condition is used to indicate the moisture content in the soil within a certain period of time in the future; when the second soil moisture condition is less than a preset soil moisture condition, generating an irrigation instruction based on the second soil moisture condition and a target soil moisture condition, and the farmland irrigation method irrigates according to the irrigation instruction, wherein the irrigation instruction comprises a single irrigation time and an irrigation interval time.

[0005] In a possible implementation, the remote sensing data includes radar echo intensity and rainfall intensity, the meteorological data includes cloud map moving speed, and the acquiring the first soil moisture condition based on the multi-source data includes: Acquire a radar decision coefficient based on the radar echo intensity and the cloud map moving speed, wherein the radar decision coefficient is used to indicate the decision influence of the radar soil moisture content measured by the radar on the first soil moisture condition; acquiring a sensor decision coefficient based on the rainfall intensity and the crop parameter, the sensor decision coefficient being used to indicate a decision influence of the sensor soil moisture content measured by the soil sensor on the first soil moisture condition, the crop parameter comprising a root depth of a current crop; The first soil moisture condition is obtained according to the radar decision coefficient, the sensor decision coefficient, the radar soil moisture content and the sensor soil moisture content.

[0006] In a possible implementation, the soil sensor includes a first sensor and a second sensor, the first sensor includes a capacitive sensor, and the second sensor includes a frequency domain reflectance sensor.

[0007] In a possible implementation manner, obtaining the first soil moisture condition according to the radar decision coefficient, the sensor decision coefficient, the radar soil moisture content, and the sensor soil moisture content includes: Acquire a first initial moisture content of the current area measured based on the first sensor, and a second initial moisture content of the current area measured based on the second sensor; The first sensor is corrected according to the current soil salinity and the compensation formula to obtain the third initial water content. The compensation formula is: in, is the first initial water content, is the third initial water content, EC is the soil electrical conductivity, and K1 is the linear influence coefficient of electrical conductivity on the water content reading; When the error value between the second initial moisture content and the third initial moisture content exceeds an error threshold, the third initial moisture content of the first sensor in an area adjacent to the current area is selected as the sensor soil moisture content of the current area.

[0008] In a possible implementation manner, obtaining the first soil moisture condition according to the radar decision coefficient, the sensor decision coefficient, the radar soil moisture content, and the sensor soil moisture content further includes: When the error value between the second initial moisture content and the third initial moisture content exceeds an error threshold, a fourth initial moisture content obtained by weighted fusion of the second initial moisture content and the third initial moisture content of the current area is used as the sensor soil moisture content of the current area.

[0009] In a possible implementation manner, the acquiring a radar decision coefficient based on the radar echo intensity and the cloud map moving speed includes: The radar decision coefficient is obtained according to the radar echo intensity, the cloud map moving speed and the first decision coefficient formula, and the first decision coefficient formula is: in, is the radar decision coefficient, is the radar echo intensity, is the cloud map moving speed, , is the weight coefficient.

[0010] In a possible implementation manner, the acquiring a sensor decision coefficient based on the rainfall intensity and the crop parameter includes: The sensor decision coefficient is obtained according to the rainfall intensity, the crop parameter and the second decision coefficient formula, and the second decision coefficient formula is: in, is the sensor decision coefficient, The permeability coefficient reflects the soil's ability to allow water to pass through. is the rainfall intensity, is the crop root depth, is the reference depth of the root system.

[0011] In a possible implementation, predicting a second soil moisture condition according to the first soil moisture condition and the multi-source data includes: The second soil moisture condition is predicted based on the first soil moisture condition, the multi-source data and a prediction model, wherein the prediction model is: in, For the second soil moisture condition, For the first soil moisture condition, For the future The accumulated precipitation during the period, For the future The average temperature during the period, is the crop root depth, k is the natural moisture attenuation coefficient of the corresponding soil type, , , is the environmental coupling coefficient based on historical data fitting, A base item for soil moisture retention.

[0012] In a possible implementation, the step of generating an irrigation instruction based on the second soil moisture condition and the target soil moisture condition includes: Obtain the field water holding capacity and soil saturated hydraulic conductivity of the current area, wherein the field water holding capacity is the maximum water holding capacity of the soil, and the soil saturated hydraulic conductivity is the vertical penetration distance of water in saturated soil per unit time; Obtaining an irrigation amount based on the target soil moisture, the second soil moisture and the crop parameter; The single irrigation time is obtained according to the soil saturated hydraulic conductivity and the target wetting depth, and the irrigation interval time is obtained according to the first soil moisture condition and field water holding capacity.

[0013] In a possible implementation, the obtaining of the field water holding capacity and soil saturated hydraulic conductivity of the current area includes: The field water holding capacity is obtained according to the normalized difference water index of the current area, the initial field water holding capacity of the current area and the first correction formula, wherein the first correction formula is: in, is the field water holding capacity, is the initial field water capacity, learning rate, is the deviation between the measured value and the target value of the normalized difference moisture index, is the NDWI deviation threshold; The soil saturated hydraulic conductivity is obtained according to the infiltration rate of the current area, the initial soil saturated hydraulic conductivity of the current area and the second correction formula, wherein the first correction formula is: in, is the saturated hydraulic conductivity of soil, is the initial soil saturated hydraulic conductivity, is the infiltration rate, which refers to the vertical penetration depth of water in the soil per unit time. is the base permeation rate, is the normalization coefficient, is the adjustment coefficient, indicating the influence strength of the permeation rate deviation on the permeation rate.

[0014] In this way, this application realizes the future trend prediction of farmland moisture conditions by integrating multi-source data such as soil sensors, remote sensing, meteorology and crop parameters, and generates specific variable irrigation instructions based on the predicted moisture conditions, such as single duration and interval, to improve irrigation efficiency while reducing water waste and soil salinization risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic diagram of a generation method provided for some embodiments of the present application; Figure 2 for Figure 1S200 is a flow chart of some embodiments; Figure 3 for Figure 1 S400 is a flowchart of some embodiments. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0017] In the following, the terms "first", "second", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0018] In addition, in the present application, directional terms such as "up", "down", "left" and "right" may be defined including but not limited to the orientation relative to the schematic placement of the components in the drawings. It should be understood that these directional terms may be relative concepts, which are used for relative description and clarification, and may change accordingly according to the change in the orientation of the components in the drawings.

[0019] In this application, unless otherwise specified or limited, the term "connection" should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection or an indirect connection through an intermediate medium. In addition, the term "electrical connection" can be a way of achieving electrical connection for signal transmission.

[0020] As used herein, "about," "substantially," or "approximately" includes the stated value and reference values ​​that are within an acceptable range of deviation from the particular value, where the acceptable range of deviation is determined by one of ordinary skill in the art taking into account the measurements in question and the errors associated with the measurement of the particular quantity (i.e., the limitations of the measurement method).

[0021] In the embodiments of the present application, the words "exemplarily" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplarily" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the words "exemplarily" or "for example" are used.

[0022] In traditional farmland irrigation management, irrigation decisions mostly rely on manual experience or static models based on fixed thresholds, lacking the ability to respond to dynamic changes in soil moisture and environmental conditions in real time. Existing technologies usually use a single sensor to monitor surface soil moisture content, but due to limitations in sensor accuracy, salt interference, and deep water penetration characteristics, it is difficult to accurately reflect the actual water demand of the crop root layer. In addition, the impact of meteorological factors (such as rainfall and evaporation) and crop growth stages has not been systematically integrated, resulting in inaccurate control of irrigation timing and water volume, which can easily lead to water resource waste or crop water stress. At the same time, with the growth of the global population and the intensification of climate change, agricultural production is facing increasingly severe challenges. In farmland irrigation management, how to accurately predict soil moisture (i.e., the content and state of water in the soil) and formulate reasonable irrigation strategies based on this is the key to achieving water-saving irrigation and increasing crop yields.

[0023] The present application provides a method for predicting soil moisture and irrigation in farmland, which is used to improve the technical problem of poor soil moisture prediction and irrigation strategy effects in related technologies. Figure 1 , Figure 2 as well as Figure 3 As shown, the method includes: S100 , acquiring multi-source data, where the multi-source data includes one or more of soil sensor data, remote sensing data, meteorological data, and crop parameters.

[0024] Exemplarily, data such as soil moisture, temperature, and conductivity can be obtained through soil sensors. Specifically, soil sensors can include capacitive sensors and frequency domain reflective sensors, which are used to measure soil moisture content at different depths. Satellite cloud images, vegetation index and other data can be obtained through remote sensing equipment. For example, satellite remote sensing can provide information such as cloud movement speed and vegetation health. Meteorological data such as rainfall intensity, wind speed, and temperature can be obtained through weather stations. Crop parameters such as root depth and growth stage of the current crop can be obtained from agricultural databases or field measurements.

[0025] Exemplarily, the initial water content θ1 of the surface soil (0-20cm) is obtained by a capacitive sensor (first sensor), and the soil conductivity EC and temperature are measured synchronously. The initial water content θ2 of the deep soil (30cm, 50cm) is obtained by a frequency domain reflective sensor (second sensor). Receive remote sensing data, including radar echo intensity Rd (unit: dBZ) and satellite cloud map moving speed x (unit: km / h). Obtain meteorological data, including rainfall intensity Ry (unit: mm / h) and temperature T (unit: ℃). Obtain crop parameters, including root depth Dz (unit: cm) and root reference depth Db (such as the default 50cm).

[0026] For example, in a sandy corn field, through the above steps: the capacitive sensor measures θ1=25% (EC=1.2mS / cm, temperature 28°C), and the frequency domain reflective sensor measures θ2=20%. The radar echo intensity Rd=65dBZ (heavy rainfall signal), the cloud map moving speed x=25km / h (severe convective weather), the current rainfall intensity Ry=0mm / h, and the root depth Dz=50cm. It can be understood that the radar echo intensity can be inverted by the intensity of the reflected signal of the meteorological radar. The cloud map moving speed can be calculated by the satellite cloud map time series analysis. The root depth can be obtained through the crop growth model or field measurement, and the root depth can characterize the water absorption capacity of the crop.

[0027] S200: Acquire a first soil moisture condition based on the multi-source data, where the first soil moisture condition is used to indicate a current moisture content in the soil.

[0028] Exemplarily, the obtaining of the first soil moisture condition comprises the following steps: S210, obtaining a radar decision coefficient based on the radar echo intensity and the cloud map moving speed. The radar decision coefficient is used to indicate the decision influence of the radar soil moisture content measured by the radar on the first soil moisture condition. Exemplarily, the radar decision coefficient can be calculated by a first decision coefficient formula: in, is the radar decision coefficient, is the radar echo intensity, is the cloud map moving speed, , is the weight coefficient. It can be understood that the unit of radar echo intensity is dBZ and the unit of cloud map moving speed is km / h.

[0029] It can be understood that in the above formula, 70dBZ is an example of a heavy rainfall threshold provided by this application, which is used to standardize the radar echo intensity and quantify the weight of the impact of precipitation on soil moisture. The value can be set by the technician based on the needs. The cloud image moving speed can be mapped to the interval [0,1], with 15km / h as the critical value, indicating a fast-moving heavy rainfall system. K2 and K3 can be fitted through historical data to balance the contribution of radar and cloud image data.

[0030] According to the parameters obtained in the above steps, we can calculate .

[0031] S220, obtaining a sensor decision coefficient based on the rainfall intensity and the crop parameter. The sensor decision coefficient is used to indicate the decision influence of the sensor soil moisture content measured by the soil sensor on the first soil moisture condition, and the crop parameter includes the root depth of the current crop. Specifically, the sensor decision coefficient can be calculated by a second decision coefficient formula: in, is the sensor decision coefficient, The permeability coefficient reflects the soil's ability to allow water to pass through. is the rainfall intensity, is the crop root depth, is the reference depth of the root system. The unit of rainfall intensity is mm / h, and the unit of root depth is cm.

[0032] It can be understood that in the above formula, the part Suppress sensor weights by rainfall intensity (no suppression when Ry=0), Some can standardize the root depth (e.g. Db=50cm can be used as a reference value). The deeper the root system, the higher the weight of the sensor data. To reflect the permeability characteristics of sandy soil, a mapping table corresponding to the soil type can be established through permeability test calibration. For example, rainfall intensity can be obtained through rain gauge or radar inversion. Root depth can be retrieved from the agricultural database according to crop type and current growth stage.

[0033] According to the parameters obtained in the above steps, we can calculate .

[0034] S230, obtaining a first soil moisture condition according to the radar decision coefficient, the sensor decision coefficient, the radar soil moisture content, and the sensor soil moisture content. Specifically, the first soil moisture condition can be calculated using the following formula: in, is the first soil moisture, θradar is the radar soil moisture, and θsensor is the sensor soil moisture. It can be understood that before the calculation, and Normalize. The above parameters can be calculated .

[0035] In one embodiment, the soil sensor includes a first sensor and a second sensor, wherein the first sensor includes a capacitive sensor and the second sensor includes a frequency domain reflection sensor. Specifically, the capacitive sensor can be used to measure the moisture content of the surface soil, and the frequency domain reflection sensor can be used to measure the moisture content of the deep soil. The capacitive sensor can obtain the moisture content by measuring the dielectric constant of the soil, and the frequency domain reflection sensor can obtain the moisture content by measuring the reflection characteristics of electromagnetic waves.

[0036] Exemplarily, before calculating the first soil moisture condition, determining the soil moisture content of the sensor may include: Acquire a first initial moisture content of the current area measured based on the first sensor, and a second initial moisture content of the current area measured based on the second sensor; The first sensor is corrected according to the current soil salinity and the compensation formula to obtain the third initial water content. The compensation formula is: in, is the first initial water content, is the third initial water content, EC is soil conductivity, and K1 is the linear influence coefficient of conductivity on water content reading, which can be calibrated by experiments. For example, when K1=0.05, it means that every 1mS / cm of conductivity causes the capacitive sensor reading to be falsely high by 0.05%. EC can be measured by the built-in electrode of the sensor to reflect the current salt concentration of the soil. In this way, the interference of salt on the dielectric constant measurement can be eliminated, and the accuracy of surface water content can be improved.

[0037] According to the above parameters, it can be calculated that .

[0038] When the error value between the second initial moisture content and the third initial moisture content exceeds an error threshold, the third initial moisture content of the first sensor in an area adjacent to the current area is selected as the sensor soil moisture content of the current area.

[0039] Exemplarily, the error threshold can be set to 15%. At this time, the error between the second initial moisture content and the third initial moisture content |24.94%-20%|=4.94%<15%, and the third initial moisture content of the first sensor can be used as the sensor soil moisture content of the current area.

[0040] When the error value between the second initial moisture content and the third initial moisture content does not exceed the error threshold, the first initial moisture content of the current area measured by the first sensor can be used as the sensor soil moisture content, or the fourth initial moisture content obtained by weighted fusion of the second initial moisture content and the third initial moisture content of the current area can be used as the sensor soil moisture content of the current area.

[0041] For example, in some examples, the weight coefficients of the two are 0.3 and 0.7 respectively. .

[0042] S300: predicting a second soil moisture condition based on the first soil moisture condition and the multi-source data, wherein the second soil moisture condition is used to indicate the moisture content in the soil within a certain period of time in the future. Exemplarily, the second soil moisture condition prediction includes: The second soil moisture condition is predicted based on the first soil moisture condition, multi-source data and a prediction model. Specifically, the prediction model can be expressed by the following formula: in, For the second soil moisture condition, For the first soil moisture condition, For the future The accumulated precipitation during the period, For the future The average temperature during the period, is the crop root depth, k is the natural moisture attenuation coefficient of the corresponding soil type, , , is the environmental coupling coefficient, It is the soil water retention base term. Among them, k, α, β, and γ can be fitted through historical soil moisture data regression analysis or set or optimized by technicians according to needs or experimental results.

[0043] For example, when k=0.05 (attenuation coefficient of sand), α=0.8, β=2.5, γ=0.12, and ε=5%, In the above formula, the exponential decay term is used to simulate the natural loss of water (for example, when k = 0.05 / h, it means that the sand soil has a rapid infiltration). When there is no rainfall in the next 3 hours (P(t) = 0), the contribution of this term is 0. ln(Dz+1) reflects the nonlinear effect of root water absorption (when Dz = 50cm, ln(51) = 3.93). It can be measured by the laboratory pressure membrane method. For example, ε=5% can indicate the water retention capacity of the sandy soil base.

[0044] S400. When the second soil moisture condition is less than a preset soil moisture condition, an irrigation instruction is generated based on the second soil moisture condition and a target soil moisture condition. The farmland irrigation method performs irrigation according to the irrigation instruction. The irrigation instruction includes a single irrigation time and an irrigation interval time.

[0045] Exemplarily, the generation of the irrigation instruction includes the following steps: S410: Obtain the field water holding capacity and soil saturated hydraulic conductivity of the current area. The field water holding capacity is the maximum water holding capacity of the soil, and the soil saturated hydraulic conductivity is the vertical penetration distance of water in saturated soil per unit time.

[0046] Exemplarily, the field water holding capacity is obtained according to the normalized difference moisture index of the current region, the initial field water holding capacity of the current region and a first correction formula, wherein the first correction formula is: in, is the field water holding capacity, is the initial field water capacity, The learning rate is used to control the influence of NDWI deviation on θf1. is the deviation between the measured value and the target value of the normalized difference moisture index, is the NDWI deviation threshold, such as 0.15.

[0047] Among them, the normalized difference moisture index can be obtained through satellite remote sensing, the initial field water holding capacity can be obtained through soil sample analysis, and the NDWI deviation threshold can be obtained by fitting historical data.

[0048] The soil saturated hydraulic conductivity is obtained according to the infiltration rate of the current area, the initial soil saturated hydraulic conductivity of the current area and the second correction formula, and the second correction formula is: in, is the saturated hydraulic conductivity of soil, is the initial soil saturated hydraulic conductivity, is the infiltration rate, which refers to the vertical penetration depth of water in the soil per unit time. is the base permeation rate, is the normalization coefficient, is the adjustment coefficient, indicating the influence strength of the permeation rate deviation on the permeation rate.

[0049] The initial θf2=28% is obtained based on the technicians’ measurement and calculation or the current soil historical data. =2.5cm / h, penetration rate Vs=0.7cm / h, ΔNDWI=0.04 (calculated by satellite remote sensing NDWI measured value and target value (such as 0.38)), set α=0.02. Then: .

[0050] In the above example, for ease of calculation, the loam base infiltration rate Vj is set to 0.5 cm / h (example), the normalization coefficient Vg is 0.5 cm / h, and the adjustment coefficient ρ is set to 0.1 to limit the correction range to within ±10%.

[0051] S420, obtaining an irrigation amount based on the target soil moisture, the second soil moisture and the crop parameters. The target soil moisture can be obtained through an agricultural database or user settings, the second soil moisture can be obtained through the above steps, and the crop parameters can be obtained from an agricultural database or field measurements. Specifically, the irrigation amount can be calculated using the following formula: Where V is the irrigation amount, θtarget is the target soil moisture (the crop water requirement threshold set based on empirical values, such as θtarget=θf×80%), is the crop root depth, S is the area that needs irrigation, such as 1 mu is 666.7 square meters.

[0052] Based on the above parameters, it can be calculated .

[0053] S430: obtaining a single irrigation time according to the soil saturated hydraulic conductivity and the target wetting depth, and obtaining an irrigation interval time according to the first soil moisture condition and the field water holding capacity.

[0054] For example, the single irrigation time can be calculated by the following formula: Among them, Ton is the single irrigation time, Dwet is the target wetting depth, which can be set according to the root distribution of the crop, such as Dwet=30cm, Ks is the saturated hydraulic conductivity of the soil, and η is the irrigation efficiency, such as 20% infiltration loss in sandy soil.

[0055] For example, the irrigation interval can be calculated by the following formula: Among them, Toff is the irrigation interval, θd is the first soil moisture condition, and θf1 is the field water holding capacity.

[0056] According to the above parameters, we can calculate: Second In summary, the irrigation instructions can include a water volume of 2.72 m³ / mu, a single irrigation time of 7.21 seconds, and an interval of 17.9 seconds. The irrigation system implements the water volume based on the irrigation instructions. m³ / mu, single irrigation time of 7.21 seconds, and irrigation interval of 17.9 seconds.

[0057] Through the description of the above implementation methods, technicians in the relevant field can clearly understand that the diagnostic method in the above embodiment can be implemented by software plus a necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the relevant technology can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), including a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0058] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0059] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0060] The units described as separate components may or may not be physically separated, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0061] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated units may be implemented in the form of hardware.

[0062] The above contents are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application shall be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for predicting soil moisture in farmland and irrigation, applied to a method for farmland irrigation, characterized in that: The method comprises: Acquiring multi-source data, the multi-source data including one or more of soil sensor data, remote sensing data, meteorological data, and crop parameters; Acquire a first soil moisture condition based on the multi-source data, where the first soil moisture condition is used to indicate the current moisture content in the soil; Predicting a second soil moisture condition according to the first soil moisture condition and the multi-source data, wherein the second soil moisture condition is used to indicate the moisture content in the soil within a certain period of time in the future; When the second soil moisture condition is less than a preset soil moisture condition, an irrigation instruction is generated based on the second soil moisture condition and a target soil moisture condition, and the farmland irrigation method performs irrigation according to the irrigation instruction, wherein the irrigation instruction includes a single irrigation time and an irrigation interval time.

2. The farmland soil moisture prediction and irrigation method according to claim 1, characterized in that: The remote sensing data includes radar echo intensity and rainfall intensity, the meteorological data includes cloud map moving speed, and the first soil moisture condition is obtained based on the multi-source data, including: Acquire a radar decision coefficient based on the radar echo intensity and the cloud map moving speed, wherein the radar decision coefficient is used to indicate the decision influence of the radar soil moisture content measured by the radar on the first soil moisture condition; acquiring a sensor decision coefficient based on the rainfall intensity and the crop parameter, the sensor decision coefficient being used to indicate a decision influence of the sensor soil moisture content measured by the soil sensor on the first soil moisture condition, the crop parameter comprising a root depth of a current crop; The first soil moisture condition is obtained according to the radar decision coefficient, the sensor decision coefficient, the radar soil moisture content and the sensor soil moisture content.

3. The farmland soil moisture prediction and irrigation method according to claim 2, characterized in that: The soil sensor includes a first sensor and a second sensor, the first sensor includes a capacitive sensor, and the second sensor includes a frequency domain reflectance sensor.

4. The farmland soil moisture prediction and irrigation method according to claim 3, characterized in that: The obtaining the first soil moisture condition according to the radar decision coefficient, the sensor decision coefficient, the radar soil moisture content and the sensor soil moisture content includes: Acquire a first initial moisture content of the current area measured based on the first sensor, and a second initial moisture content of the current area measured based on the second sensor; The first sensor is corrected according to the current soil salinity and the compensation formula to obtain the third initial water content. The compensation formula is: in, is the first initial water content, is the third initial water content, EC is the soil electrical conductivity, and K1 is the linear influence coefficient of electrical conductivity on the water content reading; When the error value between the second initial moisture content and the third initial moisture content exceeds an error threshold, the third initial moisture content of the first sensor in an area adjacent to the current area is selected as the sensor soil moisture content of the current area.

5. The farmland soil moisture prediction and irrigation method according to claim 4, characterized in that: The obtaining the first soil moisture condition according to the radar decision coefficient, the sensor decision coefficient, the radar soil moisture content and the sensor soil moisture content further includes: When the error value between the second initial moisture content and the third initial moisture content exceeds an error threshold, a fourth initial moisture content obtained by weighted fusion of the second initial moisture content and the third initial moisture content of the current area is used as the sensor soil moisture content of the current area.

6. The farmland soil moisture prediction and irrigation method according to claim 2, characterized in that: The obtaining of the radar decision coefficient based on the radar echo intensity and the cloud map moving speed comprises: The radar decision coefficient is obtained according to the radar echo intensity, the cloud map moving speed and the first decision coefficient formula, and the first decision coefficient formula is: in, is the radar decision coefficient, is the radar echo intensity, is the cloud map moving speed, , is the weight coefficient.

7. The farmland soil moisture prediction and irrigation method according to claim 2, characterized in that: The obtaining of a sensor decision coefficient based on the rainfall intensity and the crop parameter comprises: The sensor decision coefficient is obtained according to the rainfall intensity, the crop parameter and the second decision coefficient formula, and the second decision coefficient formula is: in, is the sensor decision coefficient, The permeability coefficient reflects the soil's ability to allow water to pass through. is the rainfall intensity, is the crop root depth, is the reference depth of the root system.

8. The farmland soil moisture prediction and irrigation method according to claim 5, characterized in that: The predicting a second soil moisture condition according to the first soil moisture condition and the multi-source data comprises: The second soil moisture condition is predicted based on the first soil moisture condition, the multi-source data and a prediction model, wherein the prediction model is: in, For the second soil moisture condition, For the first soil moisture condition, For the future The accumulated precipitation during the period, For the future The average temperature during the period, is the crop root depth, k is the natural moisture attenuation coefficient of the corresponding soil type, , , is the environmental coupling coefficient based on historical data fitting, A base item for soil moisture retention.

9. The farmland soil moisture prediction and irrigation method according to claim 1, characterized in that: The step of generating an irrigation instruction based on the second soil moisture condition and the target soil moisture condition comprises: Obtain the field water holding capacity and soil saturated hydraulic conductivity of the current area, wherein the field water holding capacity is the maximum water holding capacity of the soil, and the soil saturated hydraulic conductivity is the vertical penetration distance of water in saturated soil per unit time; Obtaining an irrigation amount based on the target soil moisture, the second soil moisture and the crop parameter; The single irrigation time is obtained according to the soil saturated hydraulic conductivity and the target wetting depth, and the irrigation interval time is obtained according to the first soil moisture condition and field water holding capacity.

10. The farmland soil moisture prediction and irrigation method according to claim 9, characterized in that: The obtaining of the field water holding capacity and soil saturated hydraulic conductivity of the current area includes: The field water holding capacity is obtained according to the normalized difference water index of the current area, the initial field water holding capacity of the current area and the first correction formula, wherein the first correction formula is: in, is the field water holding capacity, is the initial field water capacity, learning rate, is the deviation between the measured value and the target value of the normalized difference moisture index, is the NDWI deviation threshold; The soil saturated hydraulic conductivity is obtained according to the infiltration rate of the current area, the initial soil saturated hydraulic conductivity of the current area and the second correction formula, wherein the first correction formula is: in, is the saturated hydraulic conductivity of soil, is the initial soil saturated hydraulic conductivity, is the infiltration rate, which refers to the vertical penetration depth of water in the soil per unit time. is the base permeation rate, is the normalization coefficient, is the adjustment coefficient, indicating the influence strength of the permeation rate deviation on the permeation rate.

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