Precise irrigation decision-making method based on soil-crop-environment information

Abstract

The invention discloses a precise irrigation decision-making method based on soil-crop-environment information, and relates to the technical field of agricultural irrigation, and the method comprises the following steps: calculating the irrigation quota accommodated by a planned wetting layer, calculating the accumulated water consumption of crops, and deciding the irrigation date; according to the method, multi-source information, dynamic threshold optimization and an intelligent feedback mechanism are deeply fused, the planned wetting layer depth is dynamically adjusted according to crop root system distribution to change the irrigation quota, the actual water consumption of crops is calculated through crop-environment data, future weather forecast is considered, and the irrigation efficiency is improved. Soil moisture deep seepage and nitrate nitrogen leaching loss caused by rainfall after irrigation can be effectively avoided, the crop yield and the water and fertilizer utilization efficiency are remarkably improved, and meanwhile a dynamic irrigation system capable of changing according to the environment and crop requirements is formulated by fully considering soil characteristics, crop water consumption characteristics and environment information. The crop yield and the water productivity can be obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural irrigation, and in particular to a precise irrigation decision-making method based on soil-crop-environment information. Background Art

[0002] Agriculture refers to the industry that utilizes the growth and development laws of animals and plants to obtain products through artificial cultivation. It belongs to the primary industry. Agriculture in a narrow sense refers to planting industry. Agriculture in a broad sense refers to industries including agriculture (i.e. planting industry), forestry, animal husbandry, fishery and the auxiliary activities provided by the above-mentioned behaviors. Agricultural irrigation mainly refers to irrigation operations in agricultural farming 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. This irrigation strategy may cause crops to suffer from drought stress, waste water resources due to excessive irrigation, and even cause environmental problems such as soil salinization. Therefore, it is urgent to consider soil-crop-environment information to propose accurate and effective irrigation strategies. Therefore, this paper proposes a precise irrigation decision-making method based on soil-crop-environment information to solve the problems existing in the existing technology. Summary of the Invention

[0005] In response to the above problems, the purpose of the present invention is to propose a precise irrigation decision-making method based on soil-crop-environment information, which solves the problem that traditional irrigation methods mostly rely on manual judgment or fixed irrigation patterns. This irrigation strategy may cause crops to suffer from drought stress, may also cause water resource waste due to excessive irrigation, and even cause environmental problems such as soil salinization.

[0006] To achieve the purpose of the present invention, the present invention is implemented through the following technical solutions: a precision 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 withering coefficient θ w , determine the planned wet layer depth H and irrigation upper limit θ up , irrigation lower limit θ down and irrigation moisture ratio R w , calculate the irrigation quota Q that the planned moist layer can accommodate, and dynamically adjust the planned moist 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 the basic crop coefficient K during the actual crop growth period c and remote sensing dynamic correction K c Calculate the actual water consumption of crops ET a , cumulatively calculate the actual water consumption of crops ET a Get the cumulative water consumption AW c ;

[0009] Step 3: Irrigation decision, if there is no rainfall since the last irrigation, when AW c ≥Q and there is no rainfall of more than 10mm in the next three days, irrigation is started, and the irrigation amount is Q. If there is rainfall since the last irrigation, and the rainfall amount P is less than Q, then update AW c AW c -P, when updating AW c ≥Q and there is no rainfall of more than 10mm in the next three days, start irrigation. If the rainfall P≥Q, reset AW c 0, waiting for AW c Start irrigation when the temperature is ≥Q and there is no rainfall exceeding 10 mm in the next three days.

[0010] A further improvement is that in step 1, the field sampling adopts a zoning sampling strategy: management units are divided in combination with a soil type map, and soil samples of different depths are collected in layers within each management unit.

[0011] A further improvement is that the calculation formula for the irrigation quota Q that the planned wet layer accommodates is:

[0012] Q=10×H×R w ×ρ×(θ up -θ down ).

[0013] Further improvements are as follows: in step one, the method for dynamically adjusting the planned moistening layer depth H includes: obtaining crop root depth distribution data during the critical growth period through micro-root window technology or root drilling method, and dynamically adjusting the planned moistening layer depth H according to root growth. The planned moistening layer depth H is set to a shallow layer 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 2, 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 inverted by satellite remote sensing.

[0015] A further improvement is that in step 2, the meteorological data obtained include the maximum daily temperature T max , daily minimum temperature T min , daily average temperature T, soil heat flux G, actual solar radiation reaching the ground Rs , Net longwave radiation R nl , local atmospheric pressure P, maximum daily humidity RH max , daily minimum humidity RH min and the wind speed u2 at a height of two meters, the calculation formula for the reference crop evapotranspiration ET0 is:

[0016]

[0017] Where γ is the psychrometric constant, and the calculation formula is: γ=0.665×10 -3 ×P,e s is the saturated water vapor pressure, and the calculation formula is:

[0018]

[0019] e a is the actual water vapor pressure, and the calculation formula is:

[0020]

[0021] Δ is the slope of the saturated water vapor pressure curve, and the calculation formula is:

[0022]

[0023] R n is the net radiation, calculated as R n =(1-α)R s -R nl , a is the surface reflectivity.

[0024] A further improvement is that in step 2, the actual water consumption of the crop ET 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 is that in step 2, the crop coefficient K is dynamically corrected c The methods include: obtaining vegetation index NDVI or LAI from satellite or UAV remote sensing, and c Mapping model or machine learning algorithm to generate spatial dynamic K c value.

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

[0031] At the same time, by fully considering soil properties, crop water consumption characteristics and environmental information, a dynamic irrigation system can be formulated according to changes in the environment and crop needs. This can significantly increase crop yield and water productivity, reduce nitrate leaching losses caused by unreasonable irrigation, and promote the sustainable development of agriculture. It is universal and highly compatible, and can be integrated with agricultural information platforms, sensor systems, etc., to support automation and intelligent control expansion. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 Schematic diagram of the decision-making process of the soil-crop-environment information-based precision irrigation decision-making method of the present invention;

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

[0034] Figure 3 Schematic diagram of corn irrigation quota and irrigation time in an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall 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 through scientific planning, rational allocation and efficient utilization of various water resources (including surface water, groundwater, recycled water, rainwater, etc.) on the premise of ensuring food security and ecological security.

[0037] Precision agriculture refers to a modern agricultural management system based on modern information technology and intelligent equipment, which achieves precise and on-demand input of agricultural production factors by obtaining real-time information on the spatiotemporal variation of farmland. 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 precise intelligent irrigation systems based on soil-crop-environment information. This system can intelligently adjust irrigation schedules based on soil moisture conditions, crop growth, water consumption characteristics, and future meteorological conditions, thereby achieving precise, on-demand irrigation of crops.

[0039] See also 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 that the planned wet layer can accommodate

[0041] Field sampling was carried out using a zoning sampling strategy. The soil type map was used to divide the soil into management units (IMUs). Soil samples were collected at depths of 0-20 cm, 20-40 cm, and 40-60 cm in each management unit to determine soil bulk density ρ and soil field water holding capacity θ. f and the withering coefficient θ w ;

[0042] Determine the planned wetted layer depth H and irrigation upper limit θ based on the crop root depth distribution data obtained regularly (monitored during the key growth period by micro-root window technology or root drilling method) up (θ f 85-95%), irrigation lower limit θ down (θ w 1.1 to 1.2 times of the irrigation water ratio R w (Accurately calculated based on irrigation methods and crop row spacing), H is set at a shallow layer (0-30 cm) during the seedling stage and gradually deepened to the main root activity layer (40-60 cm) as the growth period progresses;

[0043] Based on the determined planned wet layer depth H, irrigation wetting ratio R w , soil bulk density ρ, irrigation upper limit θ up and the lower limit of irrigation θ down , calculate the irrigation quota Q that the planned wet layer can accommodate, the calculation formula is: Q = 10 × H × R w ×ρ×(θ up -θ down ), and dynamically adjust the planned moist layer depth H according to the crop root depth to update the irrigation quota Q. The specific steps are: obtain crop root depth distribution data during the key growth period through micro-root window technology or root drilling method, and adjust the planned moist layer depth H according to the root growth dynamics. The planned moist layer depth H is set to a shallow layer 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 including measured data from local automatic weather stations, regional meteorological grid forecast data, and surface temperature and radiation data derived from satellite remote sensing are obtained through multi-source fusion. Quality control and interpolation processing are performed on the input data. The data items obtained specifically include the maximum daily temperature T max , daily minimum temperature T min , daily average temperature T (℃), soil heat flux G (MJ / m 2 / day), usually the daily scale can be ignored (taken as 0), the actual solar radiation reaching the ground R s , Net longwave radiation R nl , local atmospheric pressure P (kPa), daily maximum humidity RH max , daily minimum humidity RH min and wind speed u2 (m / s) at two-meter height;

[0046] The Penman-Monteith (PM) formula was used to calculate the reference crop evapotranspiration ET0, which is as follows:

[0047]

[0048] Where γ is the psychrometric constant (kPa / °C), and the calculation formula is: γ = 0.665 × 10 -3 ×Pe s is the saturated water vapor pressure (kPa), and the calculation formula is:

[0049]

[0050] e a is the actual water vapor pressure (kPa), and the calculation formula is:

[0051]

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

[0053]

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

[0055] Combined with the basic crop coefficient K of the actual growth period of the crop cand remote sensing dynamic correction K c Calculate the actual water consumption of crops ET a , the calculation formula is:

[0056] ET a =K c ×ET0

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

[0058]

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

[0060] This embodiment dynamically corrects the crop coefficient K c The methods include: integrating NDVI or LAI data from satellite / UAV remote sensing, vegetation index and K c Mapping model or machine learning algorithm to generate spatial dynamic K c value;

[0061] Step 3: Decide on the irrigation date

[0062] Get the weather forecast for the next three days. If there is no rainfall since the last irrigation, the accumulated water consumption AW c When the irrigation quota is greater than Q and there is no rainfall exceeding 10 mm in the next three days, irrigation is started with a water volume of Q. If there is rainfall since the last irrigation and the rainfall volume P is less than Q, AW is updated. c AW c -P, when updating AW c ≥Q and there is no rainfall of more than 10mm in the next three days, start irrigation. If the rainfall P≥Q, reset AW c 0, waiting for AW c Irrigation is started when Q is ≥ Q and there is no rainfall exceeding 10 mm in the next three days. In this embodiment, if irrigation is triggered, it is selected to be executed at night or early morning in combination with the peak and valley of electricity prices and the evaporation forecast. When the forecast uncertainty is high (such as a 50% probability of rainfall), the decision is delayed for one day or irrigation is performed at 80% of Q.

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

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

[0065] The reference crop water consumption was calculated by measuring meteorological data from a national meteorological station near the experimental station. Based on the local previous experimental crop coefficient, 0.6 was used for the seedling stage, 1.2 for the jointing-grain filling stage, and 0.6 for the milky to mature stage. The actual water consumption of corn was also calculated. The rainfall during the corn growth period, crop coefficient, and actual water consumption are shown in the figure below. Figure 2 shown.

[0066] The irrigation time for corn is determined based on the cumulative water consumption of crops and future weather forecasts. The specific irrigation quota and irrigation time are as follows: Figure 3 shown.

[0067] The precise irrigation decision-making based on soil-crop-environment information of the present invention significantly improved corn yield and water and nitrogen use efficiency. The results are shown in Table 1 below:

[0068] Table 1 Comparison of corn 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 in the scope of protection of the present invention.

Claims

1. A precision irrigation decision-making method based on soil-crop-environment information, characterized in that: The following steps are involved: Step 1: Conduct field sampling to determine soil bulk density ρ and soil field water holding capacity θ f and the withering coefficient θ w , determine the planned wet layer depth H and irrigation upper limit θ up , irrigation lower limit θ down and irrigation moisture ratio R w , calculate the irrigation quota Q that the planned moist layer can accommodate, and dynamically adjust the planned moist layer depth H according to the crop root depth to update the irrigation quota Q; Step 2: Obtain meteorological data, calculate the reference crop evapotranspiration ET0, and combine the basic crop coefficient K during the actual crop growth period c and remote sensing dynamic correction K c Calculate the actual water consumption of crops ET a , cumulatively calculate the actual water consumption of crops ET a Get the cumulative water consumption AW c ; Step 3: Irrigation decision, if there is no rainfall since the last irrigation, when AW c ≥Q and there is no rainfall of more than 10mm in the next three days, irrigation is started, and the irrigation amount is Q. If there is rainfall since the last irrigation, and the rainfall amount P is less than Q, then update AW c AW c -P, when updating AW c ≥Q and there is no rainfall of more than 10mm in the next three days, start irrigation. If the rainfall P≥Q, reset AW c 0, waiting for AW c Start irrigation when the temperature is ≥Q and there is no rainfall exceeding 10 mm in the next three days.

2. The method for precise irrigation decision-making based on soil-crop-environment information according to claim 1, characterized in that: In the step 1, the field sampling adopts a zoning sampling strategy: the soil type map is used to divide the management units, and soil samples of different depths are collected in layers within each management unit.

3. The method for precision irrigation decision-making based on soil-crop-environment information according to claim 1, characterized in that: The calculation formula for the irrigation quota Q accommodated by the planned wet layer is: Q=10×H×R w ×ρ×(θ up -θ down )。 4. The method for precision irrigation decision-making based on soil-crop-environment information according to claim 1, characterized in that: In step one, the method for dynamically adjusting the planned moist layer depth H includes: obtaining crop root depth distribution data during the critical growth period through micro-root window technology or root drilling method, and dynamically adjusting the planned moist layer depth H according to root growth. The planned moist layer depth H is set to a shallow layer during the seedling stage, and gradually deepened to the depth of the main root activity layer as the growth period progresses.

5. The method for precision irrigation decision-making based on soil-crop-environment information according to claim 1, characterized in that: In the step 2, 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 inverted by satellite remote sensing.

6. The method for precision irrigation decision-making based on soil-crop-environment information according to claim 1, characterized in that: In step 2, the meteorological data obtained include the maximum daily temperature T max , daily minimum temperature T min , daily average temperature T, soil heat flux G, actual solar radiation reaching the ground R s , Net longwave radiation R nl , local atmospheric pressure P, daily maximum humidity RH max , daily minimum humidity RH min and the wind speed u2 at a height of two meters, the calculation formula for the reference crop evapotranspiration ET0 is: Where γ is the psychrometric constant, and the calculation formula is: γ=0.665×10 -3 ×P,e s is the saturated water vapor pressure, and the calculation formula is: e a is the actual water vapor pressure, and the calculation formula is: Δ is the slope of the saturated water vapor pressure curve, and the calculation formula is: R n is the net radiation, calculated as R n =(1-α)R s -R nl , a is the surface reflectivity.

7. The method for precision irrigation decision-making based on soil-crop-environment information according to claim 1, characterized in that: In the step 2, 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.

8. The method for precision irrigation decision-making based on soil-crop-environment information according to claim 1, characterized in that: In the step 2, the crop coefficient K is dynamically corrected. c The methods include: obtaining vegetation index NDVI or LAI from satellite or UAV remote sensing, and c Mapping model or machine learning algorithm to generate spatial dynamic K c value.

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