A precision irrigation decision-making method based on soil-crop-environment information

Abstract

The application discloses a kind of precision irrigation decision-making methods based on soil-crop-environment information, it is related to agricultural irrigation technical field, including the following steps: calculating the irrigation quota that plan wet layer contains, calculating crop cumulative water consumption and deciding irrigation date;The application is through depth fusion multi-source information, dynamic threshold optimization and intelligent feedback mechanism, according to crop root system distribution dynamic adjustment plan wet layer depth changes irrigation quota, through crop-environment data calculation crop actual water consumption, and consider future weather forecast, can effectively avoid the soil moisture deep seepage and nitrate nitrogen leaching loss after irrigation occurs rainfall, significantly improve crop yield and water and fertilizer utilization efficiency, simultaneously by fully considering soil characteristics, crop water consumption characteristics and environmental information formulate can according to environmental and crop demand change dynamic irrigation system, can significantly improve crop yield and water productivity.

Description

Technical Field

[0001] This invention relates to the field of agricultural irrigation technology, and in particular to a precision irrigation decision-making method based on soil-crop-environment information. Background Technology

[0002] Agriculture refers to the industry that uses the growth and development patterns of plants and animals to obtain products through artificial cultivation. It belongs to the primary industry. In a narrow sense, agriculture refers to crop cultivation. In a broad sense, agriculture includes agriculture (i.e., crop cultivation), forestry, animal husbandry, fishery, and industries that provide auxiliary activities for the above-mentioned activities. Agricultural irrigation mainly refers to irrigation operations carried out in agricultural cultivated areas.

[0003] Precision agriculture, as an effective way to improve agricultural production efficiency and optimize resource utilization, has received increasing attention in recent years. Irrigation, as an important part of agricultural production, is closely related to soil, crops and environmental conditions. The precision and intelligence of irrigation directly affect the transformation and upgrading of agriculture.

[0004] Traditional irrigation methods often rely on manual judgment or fixed irrigation patterns. Such irrigation strategies may cause crops to suffer from drought stress, or lead to water waste due to over-irrigation, and even cause environmental problems such as soil salinization. Therefore, it is urgent to consider soil-crop-environment information to propose precise and effective irrigation strategies. Thus, this invention proposes a precise irrigation decision-making method based on soil-crop-environment information to solve the problems existing in the prior art. Summary of the Invention

[0005] To address the aforementioned problems, the present invention aims to propose a precision irrigation decision-making method based on soil-crop-environment information. This method solves the problem that traditional irrigation methods often rely on manual judgment or fixed irrigation patterns. Such irrigation strategies may cause crops to suffer from drought stress, or lead to water waste due to excessive irrigation, and may even cause environmental problems such as soil salinization.

[0006] To achieve the objectives of this invention, the invention is implemented through the following technical solution: a precise irrigation decision-making method based on soil-crop-environment information, comprising the following steps:

[0007] Step 1: Conduct field sampling to determine soil bulk density ρ and soil field water holding capacity θ. f and the wilting coefficient θ w Determine the planned wetting layer depth H and the upper limit of irrigation θ. up Lower limit of irrigation θ down and irrigation moisture ratio R w Calculate the planned irrigation quota Q to be contained in the wet layer, and dynamically adjust the planned wet layer depth H according to the crop root depth to update the irrigation quota Q;

[0008] Step 2: Obtain meteorological data, calculate the reference crop evapotranspiration ET0, and combine it with the basic crop coefficient K during the actual crop growth period. c and K-sensing dynamic correction c Value, calculate the actual water consumption ET of the crop. a The actual water consumption ET of crops is calculated by summing them up. a Calculate the cumulative water consumption AW c ;

[0009] Step 3: Irrigation decision. If no rainfall has occurred since the last irrigation, when AW... c Irrigation will begin when the rainfall is ≥Q and there is no rainfall exceeding 10mm in the next three days, with an irrigation volume of Q. If there has been rainfall since the last irrigation and the rainfall P < Q, then update AW. c For AW c -P, after AW update c Irrigation will begin if the rainfall is ≥Q and there is no more than 10mm of rainfall in the next three days. If the rainfall is ≥Q, AW will be reset. c =0, waiting for AW c Irrigation should be initiated when the rainfall exceeds 10mm in the next three days and the rainfall is ≥Q.

[0010] A further improvement is made in the following step: In step one, the field sampling adopts a zonal sampling strategy: the management unit is divided in combination with the soil type map, and soil samples at different depths are collected in each management unit.

[0011] A further improvement lies in the following: the calculation formula for the irrigation quota Q to be accommodated by the planned wetting layer is:

[0012] Q = 10 × H × R w ×ρ×(θ up -θ down ).

[0013] A further improvement is made in the following: In step one, the method for dynamically adjusting the planned wetting layer depth H includes: obtaining crop root depth distribution data during key growth periods through micro-root window technology or root drilling method, and dynamically adjusting the planned wetting layer depth H according to root growth. The planned wetting layer depth H is set to shallow during the seedling stage and gradually deepened to the depth of the main root activity layer as the growth period progresses.

[0014] A further improvement is that, in step two, the meteorological data is obtained through multi-source fusion, including measured data from local automatic weather stations, regional meteorological grid forecast data, and surface temperature and radiation data retrieved from satellite remote sensing.

[0015] A further improvement is made in that: in step two, the acquired meteorological data includes the daily maximum temperature T. max Daily minimum temperature T min Daily average temperature T, soil heat flux G, and actual solar radiation reaching the ground Rs Net longwave radiation R nl Local atmospheric pressure (P) and maximum daily humidity (RH) max Minimum daily humidity (RH) min Given a wind speed of u2 at a height of two meters, the formula for calculating the reference crop evapotranspiration ET0 is as follows:

[0016]

[0017] Where γ is the wet / dry constant, calculated using the formula: γ = 0.665 × 10 -3 ×P, e s The saturated vapor pressure is calculated using the following formula:

[0018]

[0019] e a The actual water vapor pressure is calculated using the following formula:

[0020]

[0021] Δ is the slope of the saturated water vapor pressure curve, calculated using the following formula:

[0022]

[0023] R n Net radiation is calculated using the formula R. n =(1-α)R s -R nl , where a is the surface reflectance.

[0024] A further improvement is made in step two, where the actual water consumption ET of the crop is... a The calculation formula is:

[0025] ET a =K c ×ET0

[0026] The cumulative water consumption AW c The calculation formula is:

[0027]

[0028] Where n is the number of days after irrigation.

[0029] A further improvement lies in: in step two, dynamically correcting the crop coefficient K. c Methods include: acquiring the vegetation index NDVI or LAI from satellite or UAV remote sensing, and then using the vegetation index in relation to K... c The mapping model or machine learning algorithm generates a spatialized dynamic K c value.

[0030] The beneficial effects of this invention are as follows: This invention, through deep integration of multi-source information, dynamic threshold optimization and intelligent feedback mechanism, dynamically adjusts the planned wetting layer depth to change the irrigation quota according to the crop root distribution, calculates the actual water consumption of crops through crop-environment data, and takes into account future weather forecasts, which can effectively avoid deep soil moisture infiltration and nitrate nitrogen leaching loss caused by rainfall after irrigation, significantly improve crop yield and water and fertilizer use efficiency, and is applicable to different climate regions and soil types;

[0031] Meanwhile, by fully considering soil characteristics, crop water consumption features, and environmental information, a dynamic irrigation system can be formulated based on changes in environmental and crop needs. This system can significantly improve crop yield and water productivity, reduce nitrate leaching losses caused by unreasonable irrigation, promote sustainable agricultural development, and has universal applicability and strong compatibility. It can be integrated with agricultural information platforms, sensor systems, etc., and supports the expansion of automation and intelligent control. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the decision-making process of the precision irrigation decision-making method based on soil-crop-environment information of the present invention;

[0033] Figure 2 This is a schematic diagram of rainfall, crop coefficient and actual water consumption during the corn growth period in an embodiment of the present invention;

[0034] Figure 3 This is a schematic diagram of the corn irrigation quota and irrigation time in an embodiment of the present invention. Detailed Implementation

[0035] 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.

[0036] Agricultural water resources management refers to a comprehensive technical system that achieves sustainable utilization of water resources in agricultural production by scientifically planning, rationally allocating, and efficiently utilizing various water resources (including surface water, groundwater, reclaimed water, rainwater, etc.) while ensuring food security and ecological security.

[0037] Precision agriculture refers to a modern agricultural management system that uses modern information technology and intelligent equipment to acquire real-time information on the spatiotemporal variations of farmland and enable the precise input of agricultural production factors as needed. According to FAO research, the full implementation of precision agriculture technology can increase global food production by 25% while reducing agricultural non-point source pollution by more than 30%, which is of strategic significance for ensuring food security and ecological security.

[0038] Advances in sensor monitoring and data processing technologies have provided favorable conditions for precision intelligent irrigation systems based on soil-crop-environment information. This system can intelligently adjust irrigation regimes according to soil moisture conditions, crop growth, water consumption characteristics, and future weather conditions, thereby achieving precise irrigation of crops on demand.

[0039] See Figure 1 , Figure 2 , Figure 3 This embodiment provides a precision irrigation decision-making method based on soil-crop-environment information, including the following steps:

[0040] Step 1: Calculate the irrigation quota Q to be accommodated by the planned wetting layer.

[0041] A zonal sampling strategy was employed for field sampling. Management units (IMUs) were divided using soil type maps. Within each IMU, soil samples were collected at depths of 0–20 cm, 20–40 cm, and 40–60 cm to determine soil bulk density ρ and soil field capacity θ. f and the wilting coefficient θ w ;

[0042] Based on regularly acquired crop root depth distribution data (monitored during key growth periods using microroot window technology or root drilling), the planned wetting layer depth H and the upper limit of irrigation θ are determined. up (θ f 85-95% of the total), lower limit of irrigation θ down (θ w (1.1 to 1.2 times) and irrigation wetting ratio R w (Based on precise calculations of irrigation methods and crop row spacing), the seedling stage H is set as shallow (0-30cm), and gradually deepened to the main root activity layer (40-60cm) as the growth stage progresses;

[0043] Based on the determined planned wetting layer depth H and irrigation wetting ratio R w Soil bulk density ρ, upper limit of irrigation θ up and irrigation lower limit θ down The irrigation quota Q to be accommodated by the planned wetting layer is calculated using the formula: Q = 10 × H × R w ×ρ×(θ up -θ down The irrigation quota Q is updated by dynamically adjusting the planned wetting layer depth H based on the crop root depth. The specific steps are as follows: obtain crop root depth distribution data during key growth periods through micro root window technology or root drilling method, and adjust the planned wetting layer depth H according to the root growth dynamics. The planned wetting layer depth H is set to shallow during the seedling stage and gradually deepened to the depth of the main root activity layer as the growth period progresses.

[0044] Step 2: Calculate the cumulative water consumption of crops (AW) c

[0045] Meteorological data was obtained through multi-source fusion, including measured data from local automatic weather stations, regional meteorological grid forecast data, and surface temperature and radiation data retrieved from satellite remote sensing. Quality control and interpolation were performed on the input data. Specific data items acquired included daily maximum temperature (T). max Daily minimum temperature T min Daily average temperature T (°C), soil heat flux G (MJ / m³) 2 / day), which is usually negligible on a daily scale (taken as 0), the actual solar radiation reaching the ground R s Net longwave radiation R nl Local atmospheric pressure P (kPa) and maximum daily humidity RH max Minimum daily humidity (RH) min And the wind speed at a height of two meters, u2 (m / s);

[0046] The reference crop evapotranspiration ET0 is calculated using the Penman-Monteith (PM) formula. The formula is as follows:

[0047]

[0048] Wherein, γ is the wet / dry constant (kPa / ℃), calculated using the formula: γ=0.665×10 -3 ×Pe s The saturated vapor pressure (kPa) is calculated using the following formula:

[0049]

[0050] e a The actual water vapor pressure (kPa) is calculated using the following formula:

[0051]

[0052] Δ is the slope of the saturated water vapor pressure curve (kPa / ℃), calculated using the following formula:

[0053]

[0054] R n Net radiation (MJ / m 2 / day), the calculation formula is R n =(1-α)R s -R nl , where a is the surface reflectance, which is taken as 0.23 in this embodiment;

[0055] The basic crop coefficient K is combined with the actual growth period of the crop. cand K-sensing dynamic correction c Value, calculate the actual water consumption ET of the crop. a The calculation formula is:

[0056] ET a =K c ×ET0

[0057] Accumulate and calculate the actual water consumption of crops ET a Get cumulative water consumption AW c (accumulated water consumption), the calculation formula is:

[0058]

[0059] Where n is the number of days after irrigation;

[0060] This embodiment dynamically corrects the crop coefficient K. c Methods include: integrating NDVI or LAI data from satellite / UAV remote sensing, based on vegetation indices and K... c The mapping model or machine learning algorithm generates a spatialized dynamic K c value;

[0061] Step 3: Decide on the irrigation date

[0062] Get the weather forecast for the next three days. If there has been no rainfall since the last irrigation, the cumulative water consumption AW c Irrigation is initiated when the irrigation quota is ≥Q and there is no rainfall exceeding 10mm in the next three days, with an irrigation volume of Q. If there has been rainfall since the last irrigation, and the rainfall P < Q, then AW is updated. c For AW c -P, after AW update c Irrigation will begin if the rainfall is ≥Q and there is no more than 10mm of rainfall in the next three days. If the rainfall is ≥Q, AW will be reset. c =0, waiting for AW c Irrigation is initiated when there is no more than 10 mm of rainfall in the next three days, ≥Q. In this embodiment, if irrigation is triggered, it is selected to be executed at night or in the early morning, based on the peak and valley electricity prices and evaporation forecasts. When the weather forecast is highly uncertain (e.g., 50% probability of rainfall), the decision is delayed by one day or irrigation is carried out according to 80% Q.

[0063] Using the precision irrigation decision-making method based on soil-crop-environment information disclosed in this invention, a precision irrigation decision-making experiment for summer maize was carried out over a two-year period. The experiment included three water treatments: conventional irrigation (CK), full irrigation (FI) based on precision irrigation decision-making of soil-crop-environment information, and deficit irrigation (DI, 75% FI).

[0064] Before the experiment began, the soil field water holding capacity was measured to be 0.32 cm³. 3 The upper limit of irrigation was set at 90% of field capacity, the lower limit at 65% of field capacity, the drip irrigation wetting ratio at 0.6, the planned wetting layer depth at 60cm, and the irrigation quota for full irrigation calculated by the formula at 28.8mm, which was taken as 30mm. The corresponding irrigation quota for deficit irrigation was 22.5mm.

[0065] Meteorological data were measured at a national meteorological station near the experimental station. Reference crop water consumption was calculated, and the local crop coefficient from previous experiments was taken as 0.6 for the seedling stage, 1.2 for the jointing-grain-filling stage, and 0.6 for the milk-ripe to maturity stage. Actual water consumption for maize was also calculated. Rainfall, crop coefficient, and actual water consumption during the maize growth period are as follows: Figure 2 As shown.

[0066] Based on the cumulative water consumption of the crop and future weather forecasts, the irrigation time for corn was determined. Specific irrigation quotas and irrigation times are as follows: Figure 3 As shown.

[0067] The precision irrigation decision-making based on soil-crop-environment information of this invention significantly improved maize yield and water and nitrogen use efficiency, as shown in Table 1 below:

[0068] Table 1. Comparison of maize yield, water use efficiency, and nitrogen fertilizer productivity.

[0069]

[0070] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A precision irrigation decision-making method based on soil-crop-environment information, characterized in that, Includes the following steps: Step 1: Conduct field sampling to determine soil bulk density ρ and soil field water holding capacity θ. f and the wilting coefficient θ w Determine the planned wetting layer depth H and the upper limit of irrigation θ. up Lower limit of irrigation θ down and irrigation moisture ratio R w Calculate the planned irrigation quota Q to be contained in the wet layer, and dynamically adjust the planned wet layer depth H according to the crop root depth to update the irrigation quota Q; The formula for calculating the irrigation quota Q to be accommodated by the planned wetting layer is as follows: Step 2: Obtain meteorological data, calculate the reference crop evapotranspiration ET0, and combine it with the basic crop coefficient K during the actual crop growth period. c and K-sensing dynamic correction c Value, calculate the actual water consumption ET of the crop. a The actual water consumption of crops, ET, is calculated cumulatively. a Calculate the cumulative water consumption AW c ; The acquired meteorological data includes the daily maximum temperature T max Daily minimum temperature T min Daily average temperature T, soil heat flux G, and actual solar radiation reaching the ground R s Net longwave radiation R nl Local atmospheric pressure (P) and maximum daily humidity (RH) max Minimum daily humidity (RH) min Given a wind speed of u2 at a height of two meters, the formula for calculating the reference crop evapotranspiration ET0 is as follows: Wherein, γ is the wet / dry constant, and its calculation formula is: e s The saturated vapor pressure is calculated using the following formula: e a The actual water vapor pressure is calculated using the following formula: ∆ is the slope of the saturated water vapor pressure curve, calculated using the following formula: Net radiation, calculated using the following formula: , where a is the surface reflectance; The actual water consumption of the crop ET a The calculation formula is: AND a =K c ×ET0 The cumulative water consumption AW c The calculation formula is: Where n is the number of days after irrigation; Step 3: Irrigation decision. If no rainfall has occurred since the last irrigation, when AW... c Irrigation will begin when the rainfall is ≥Q and there is no rainfall exceeding 10mm in the next three days, with an irrigation volume of Q. If there has been rainfall since the last irrigation and the rainfall P < Q, then update AW. c For AW c -P, after AW update c Irrigation will begin if the rainfall is ≥Q and there is no more than 10mm of rainfall in the next three days. If the rainfall is ≥Q, AW will be reset. c =0, waiting for AW c Irrigation should be initiated when the rainfall exceeds 10mm in the next three days and the rainfall is ≥Q.

2. The precision irrigation decision-making method based on soil-crop-environment information according to claim 1, characterized in that: In step one, the field sampling adopts a zonal sampling strategy: the management unit is divided in combination with the soil type map, and soil samples at different depths are collected in each management unit.

3. The precision irrigation decision-making method based on soil-crop-environment information according to claim 1, characterized in that: In step one, the method for dynamically adjusting the planned wetting layer depth H includes: obtaining crop root depth distribution data during key growth periods through micro-root window technology or root drilling method, and dynamically adjusting the planned wetting layer depth H according to root growth. The planned wetting layer depth H is set to shallow during the seedling stage and gradually deepened to the depth of the main root activity layer as the growth period progresses.

4. The precision irrigation decision-making method based on soil-crop-environment information according to claim 1, characterized in that: In step two, the meteorological data is obtained through multi-source fusion, including measured data from local automatic weather stations, regional meteorological grid forecast data, and surface temperature and radiation data retrieved from satellite remote sensing.

5. The precision irrigation decision-making method based on soil-crop-environment information according to claim 1, characterized in that: In step two, the crop coefficient K is dynamically corrected. c Methods include: acquiring the vegetation index NDVI or LAI from satellite or UAV remote sensing, and then using the vegetation index in relation to K... c The mapping model or machine learning algorithm generates a spatialized dynamic K c value.

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