Intelligent irrigation control device and method for corn field

Abstract

The invention relates to the technical field of intelligent irrigation control, in particular to an intelligent irrigation control device and method for a corn field, and the method comprises the following steps: 1, data collection; 2, water demand calculation; step 3, irrigation decision making; step 4, executing irrigation; and step 5, feedback optimization. The device comprises an acquisition module, a processing module, an execution module and a correction module. According to the method, the illumination cumulant is calculated through numerical integration, and the illumination cumulant is used as the input of the digital twinborn model, so that the calculated ET0 is closer to the actual energy receiving condition of the corn canopy, real on-demand irrigation is realized, and excessive waste of irrigation water is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent irrigation control, in particular to a corn field intelligent irrigation control device and method. BACKGROUND

[0002] In corn field production, irrigation is a key link affecting yield and water use efficiency. Traditional irrigation methods rely on manual experience or fixed cycles, and there are problems such as inaccurate irrigation timing, large water waste, and crop water response lag. Although the existing intelligent irrigation system introduces soil moisture sensors and weather station data, due to the influence of soil spatial heterogeneity on soil moisture sensors and the slow update of weather data, it is difficult to reflect the corn canopy transpiration demand in real time, so there are still many limitations in the irrigation process.

[0003] As a C4 crop, the transpiration of corn is closely related to the cumulative light intensity (i.e. cumulative photosynthetically active radiation per unit area). Its reference crop evapotranspiration (ET0) can be calculated by the Penman formula, and in the Penman formula, net radiation is a key input parameter, which is directly related to the cumulative light intensity. The actual water requirement of corn crop (ETc) needs to be associated with ET0 through crop coefficient (Kc), and Kc changes dynamically with the growth stage of corn.

[0004] In the prior art, when irrigating the corn field, first, the soil moisture data is collected by the humidity sensor, and at the same time, the weather station synchronously monitors the environmental parameters such as wind speed, light intensity and rainfall. The crop growth monitor records biological indicators such as leaf area index (LAI). Subsequently, all the collected data is uploaded to the cloud platform integrated with an intelligent model, which analyzes the uploaded data in real time. During this period, the static crop coefficient (Kc) and the reference evapotranspiration (ET0) formula recommended by FAO (Food and Agriculture Organization of the United Nations) are directly used to calculate the water requirement, generate an irrigation strategy, and finally the irrigation strategy is implemented through the execution layer, thereby realizing the irrigation of the corn field.

[0005] Although the existing method can perform irrigation operation on the corn field, its main goal is to complete the irrigation of the corn field, and less attention is paid to the water requirement of the corn crop, which may result in over-irrigation during the irrigation process, leading to excessive waste of irrigation water. At the same time, the operation of directly using the static crop coefficient and the reference evapotranspiration formula recommended by FAO to calculate the water requirement ignores the microenvironment differences and dynamic growth conditions of the actual crop growth, resulting in deviation of the actual water requirement of the crop, so it is necessary to propose a corn field intelligent irrigation control device and method that can directly use the cumulative light intensity to drive the corn field irrigation decision in real time. SUMMARY

[0006] To solve the above problems, the present application provides a corn field intelligent irrigation control device and method, which calculates the light accumulation by numerical integration and uses it as the input of the digital twin model, so that the calculated ET0 is closer to the actual energy receiving of the corn canopy, and realizes real demand-based irrigation, thereby reducing the excessive waste of irrigation water.

[0007] In order to achieve the above purpose, the technical scheme of the present application is as follows: a corn field intelligent irrigation control method, comprising the following steps: Step one, data acquisition: install several light sensors and soil moisture sensors in the corn field, respectively collect the soil moisture data of the corn field and the light intensity data of the corn canopy, and based on the collected light intensity data, use the numerical integration algorithm to obtain the light accumulation of the corn field; at the same time, collect the crop coefficient Kc of corn and the leaf area index of corn crop at different growth stages through the internet large model, and collect the meteorological parameters of the location of the corn field through the collection module in the corn field intelligent irrigation control device.

[0008] Step two, water demand calculation: based on the soil moisture data of the corn field, the light intensity data of the corn canopy, and the crop coefficient Kc of corn, a digital twin model of the corn field is established, the light accumulation of the corn field is input into the digital twin model, the evapotranspiration ET0 of the corn crop is obtained, then the crop coefficient Kc of corn is input into the digital twin model, and the actual water demand ETc of the corn crop is calculated through the digital twin model.

[0009] Step three, irrigation decision: the staff sets the initial values of the irrigation threshold and the stop threshold, monitors the change of the actual water demand ETc of the corn crop in the past 3 hours through the digital twin model, predicts the change in the future 2 hours, obtains the prediction result, compares the prediction result with the irrigation threshold and the stop threshold respectively, obtains the comparison result, and formulates the irrigation scheme according to the comparison result.

[0010] Step four, irrigation execution: execute the irrigation scheme through the execution module in the corn field intelligent irrigation control device; Step five, feedback optimization: the digital twin model judges the irrigation effect according to the real-time detection of the soil moisture sensor, and dynamically adjusts the irrigation threshold and the stop threshold according to the irrigation effect.

[0011] The above scheme has the following beneficial effects: 1. This invention calculates the cumulative light intensity through numerical integration and uses it as input to a digital twin model, making the calculated ETO more closely reflect the actual energy reception of the corn canopy, thus achieving true on-demand irrigation and reducing excessive waste of irrigation water.

[0012] 2. This invention predicts the actual water requirement (ETc) of corn crops and formulates an irrigation plan based on this, enabling irrigation to be initiated before the corn crops face water stress. Compared with traditional irrigation methods, which irrigate only after the corn crops have already experienced drought, this invention reduces the lag caused by traditional irrigation methods.

[0013] 3. This invention achieves adaptive dynamic optimization of irrigation and stop thresholds by having staff set initial values ​​for irrigation and stop thresholds, and then using a digital twin model to dynamically adjust the irrigation and stop thresholds. Compared with the fixed thresholds used in existing technologies, it can achieve self-optimization of thresholds, thereby improving the accuracy of threshold setting.

[0014] Furthermore, in step one, the meteorological parameters of the location of the cornfield include the soil heat flux, air temperature, wind speed, and water vapor pressure at that location.

[0015] Beneficial effects: Soil heat flux reflects the intensity of heat exchange between the soil and the atmosphere, and air temperature is a key factor affecting physiological processes such as photosynthesis and respiration in maize. By combining soil heat flux and air temperature data, the growth and development process of maize can be predicted more accurately. Water vapor pressure is an indicator of the water vapor content in the air. By monitoring water vapor pressure and combining it with soil moisture and the water requirements of maize at different growth stages, more reasonable irrigation plans can be developed. Furthermore, since some pests can spread over long distances with the help of wind, monitoring wind speed can also help determine the spread range of pests and diseases.

[0016] Furthermore, in step two, when calculating the actual water requirement ETc of the maize crop, the Kc value of the maize crop is dynamically adjusted according to the growth stage of the maize crop.

[0017] Beneficial effects: Corn exhibits significant differences in plant morphology, leaf area index, and root development at different growth stages, which directly affect crop transpiration and soil evaporation, thus determining the crop's water requirement pattern. By dynamically adjusting the Kc value of corn, the matching relationship between the corn crop coefficient and physiological characteristics at different growth stages can be accurately reflected, making the calculated result of the actual water requirement ETC of corn closer to the actual water demand of corn.

[0018] Furthermore, in step two, the method for determining the growth stage of the corn crop is as follows: S1. Input the leaf area index of maize crop at different growth stages collected in step one into the digital twin model and integrate it into a growth stage database.

[0019] S2, The leaf area index of maize crops was monitored using leaf area index monitoring equipment, and the monitoring results were obtained.

[0020] S3. Input the monitoring results into the digital twin model, and compare them with the data in the growth stage database to obtain the growth stage of the corn crop.

[0021] Beneficial effects: By integrating the leaf area index of maize at different growth stages collected in step one into the digital twin model, a growth stage database covering the leaf area index characteristics of maize from sowing to maturity is constructed, providing a comprehensive and reliable reference for judging the growth stage of maize crops, thus reflecting the actual growth process of maize more accurately.

[0022] Furthermore, in step two, the evapotranspiration ET0 of maize is calculated using the Penman formula, the specific formula of which is as follows: (1).

[0023] in, Net radiation is calculated from cumulative sunlight. Soil heat flux; Temperature; Wind speed; and These are the saturated vapor pressure and the actual vapor pressure, respectively. The slope of the saturated water vapor pressure curve; This is the wet / dry constant.

[0024] Beneficial effects: The Penman formula can adapt to different climatic and geographical conditions and has wide applicability. It can accurately calculate the evapotranspiration of maize crops under different environments, thus providing a scientific basis for agricultural production in different regions.

[0025] Furthermore, in step two, the formula for calculating the actual water requirement (ETc) of the maize crop is as follows: (2).

[0026] in, The crop coefficient for maize; This refers to the evapotranspiration of the corn crop; and The specific value is determined by the growth stage of the corn crop.

[0027] Beneficial effects: In actual agricultural production, changes in environmental conditions will directly affect the evapotranspiration of crops, and the operation of adjusting the crop coefficient according to the growth stage enables the calculation of the actual water requirement of crops to comprehensively consider the influence of crop demand and environmental factors, thereby accurately calculating the actual water requirement of corn at the current growth stage to reduce water waste caused by excessive irrigation.

[0028] Further, in step three, the staff set 80% and 20% of the actual water requirement ETc of the corn crop as the initial values of the irrigation threshold and the stop threshold, respectively.

[0029] Beneficial effects: Setting the irrigation threshold to 80% of ETc can provide timely irrigation when soil moisture begins to decrease but has not yet seriously affected corn growth, thereby providing a suitable water environment for seedling corn; setting 20% of ETc as the initial value of the stop threshold can reduce the occurrence of excessive soil drying, thereby reducing the severe water stress suffered by corn due to long-term water shortage and reducing the risk of yield reduction due to drought.

[0030] Further, an intelligent corn field irrigation control device comprises the following modules: The acquisition module comprises a weather station for real-time monitoring of the specific conditions of light radiation, soil heat flux, air temperature, wind speed and water vapor pressure in the area where the corn field is located, and generating monitoring data.

[0031] The processing module comprises a central processing unit electrically connected with the weather station, for receiving the monitoring data sent by the weather station, calculating the evapotranspiration ET0 of the corn crop and the actual water requirement ETc of the corn crop based on the monitoring data, and formulating an irrigation scheme and generating an irrigation control signal.

[0032] The execution module comprises an irrigation system electrically connected with the central processing unit, for receiving the irrigation control signal sent by the processing module and executing the irrigation scheme.

[0033] The correction module comprises a soil moisture meter electrically connected with the central processing unit, for real-time monitoring of the soil water holding capacity of the corn field and generating holding water data, and feeding the holding water data back to the central processing unit to correct the irrigation threshold and the stop threshold set by the staff.

[0034] Beneficial effects: The weather station of the acquisition module can real-time monitor the light radiation, soil heat flux, air temperature, wind speed and water vapor pressure of the environment where the corn field is located, and the processing module can accurately calculate the evapotranspiration ET0 and the actual water requirement ETc of the corn crop based on these data, thereby formulating an irrigation scheme that meets the growth needs of corn and ensuring the growth needs of corn and improving its yield and quality.

[0035] Furthermore, the light sensors are all integrated into the weather station, and the data acquisition frequency of the light sensors is greater than 1 time / min.

[0036] Beneficial effects: Since light intensity fluctuates rapidly throughout the day due to factors such as time and weather conditions, a data acquisition frequency of more than 1 time / min can more accurately capture subtle changes in light intensity, thus providing an accurate data foundation for subsequent analysis and processing.

[0037] Furthermore, the irrigation system will be either a drip irrigation system or a sprinkler irrigation system.

[0038] Beneficial effects: Drip irrigation delivers water slowly, evenly, and precisely to the vicinity of the roots of corn plants, directly providing moisture to the root system, thereby reducing water evaporation and loss at the soil surface, and promoting crop root development and plant growth. Sprinkler irrigation, on the other hand, simulates natural rainfall, spraying water in the form of even droplets onto the corn field. It can cover a large area, and by adjusting the spray angle and range of the nozzles, water is evenly distributed on the corn plants, meeting the water requirements for transpiration and photosynthesis in the corn leaves.

[0039] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of the method steps of the intelligent irrigation control method for maize fields according to the present invention.

[0041] Figure 2 This is a schematic diagram of the process for calculating the actual water requirement (ETc) of maize crops in the intelligent irrigation control method for maize fields of the present invention.

[0042] Figure 3 This is a schematic diagram illustrating the steps of determining the growth stage of maize crops in the intelligent irrigation control method for maize fields of the present invention.

[0043] Figure 4 This is a schematic diagram of the intelligent irrigation control device for cornfields according to the present invention.

[0044] The reference numerals in the accompanying drawings include: 1. Weather station; 2. Central processing unit; 3. Irrigation system; 4. Soil moisture meter. Detailed Implementation

[0045] The following detailed description illustrates the specific implementation method: Example 1: As attached Figure 1 As shown: A smart irrigation control method for maize fields includes the following steps: Step one, data collection: first, the staff installs several light sensors and soil moisture sensors in the corn field, so that they can collect soil moisture data and corn canopy light intensity data, and based on the collected light intensity data, use numerical integration algorithm to obtain the light accumulation of the corn field. In this embodiment, the numerical integration algorithm uses the rectangular method, which is a prior art and will not be described in detail here.

[0046] At the same time, the staff also collects the crop coefficient Kc of corn (the crop coefficient Kc of corn in the seedling stage is 0.3-0.4, the crop coefficient Kc in the jointing stage is 0.6-0.7, the crop coefficient Kc in the tasseling stage is 0.9-1.0, the crop coefficient Kc in the milk stage is 0.7-0.8, and the crop coefficient Kc in the mature stage is 0.4-0.5) and the leaf area index of corn crops in different growth stages (the leaf area index in the seedling stage is 0.5-1.0, the leaf area index in the jointing stage is 1.5-2.5, the leaf area index in the tasseling stage is 3.0-4.0, the leaf area index in the milk stage is 2.5-3.5, and the leaf area index in the mature stage is 1.0-2.0) through the Internet large model, and the meteorological parameters of the location of the corn field are collected in real time through the collection module in the corn field intelligent irrigation control device. The meteorological parameters of the location of the corn field include the soil heat flux, air temperature, wind speed and water vapor pressure at the location, and in this embodiment, the Internet large model uses an agricultural professional large model, which is preferably the SIMDual_Kc model.

[0047] Step two, water requirement calculation: after completing the data collection operation, the staff constructs a digital twin model of the corn field based on the soil moisture data of the corn field, the light intensity data of the corn canopy, and the crop coefficient Kc of corn.

[0048] As shown in Figure 2 , the staff inputs the light accumulation of the corn field into the digital twin model, so that the Penman formula is used to calculate the evapotranspiration ET0 of the corn crop, and then the crop coefficient Kc of corn is input into the digital twin model, so that the actual water requirement ETc of the corn crop is calculated through the digital twin model with the evapotranspiration ET0 of the corn crop as input. The specific calculation formula is as follows: The calculation formula of the evapotranspiration ET0 of the corn crop is: (1).

[0049] Among them, is the net radiation, which is converted from the light accumulation; is the soil heat flux; is the air temperature; is the wind speed; and are the saturated water vapor pressure and the actual water vapor pressure, respectively; is the slope of the saturated water vapor pressure curve; is the dry constant and

[0050] The formula for calculating the actual water requirement ETc of the corn crop is: (2).

[0051] wherein, is the crop coefficient of corn; is the evapotranspiration of the corn crop; and The specific value of ETc is dynamically adjusted according to the growth stage of the corn crop.

[0052] As shown in Figure 3 , the growth stage determination method of the corn crop is as follows: S1, the staff first inputs the leaf area index of the corn crop at different growth stages collected in step one into the digital twin model, integrates them through the digital twin model, and constructs a growth stage library.

[0053] S2, while constructing the growth stage library, the staff uses the leaf area index monitoring device to monitor the leaf area index of the corn crop and obtains the monitoring results. In this embodiment, the leaf area index monitoring device is a plant canopy analyzer.

[0054] S3, after completing the construction of the growth stage library and obtaining the monitoring results, the staff inputs the current monitoring results into the digital twin model, compares them with the data in the growth stage library through the digital twin model, and when the similarity between the data in the growth stage library and the current monitoring data is greater than 90%, it is determined that the current monitoring data and the data in the growth stage library are in the same growth stage, thereby determining the current growth stage of the corn crop.

[0055] Step three, irrigation decision: after calculating the water requirement of the corn crop, the staff first takes 80% and 20% of the actual water requirement ETc (ETc is 5mm in this embodiment) of the corn crop as the initial values of the irrigation threshold and the stop threshold (4mm and 1mm respectively), then monitors the change of the actual water requirement ETc of the corn crop in the past 3 hours (such as ETc rising from 3mm to 4.5mm in the past 3 hours) through the digital twin model, and predicts the change in the next 2 hours (predicting that ETc will rise to 5.5mm in the next 2 hours), obtains the prediction result, and finally compares the prediction result with the current irrigation threshold and stop threshold respectively, obtains the comparison result, and based on the comparison result, the irrigation scheme is formulated.

[0056] Step four, irrigation execution: after the irrigation scheme is formulated, the execution module in the corn field intelligent irrigation control device is started to perform irrigation operation on the corn field based on the irrigation scheme.

[0057] Step 5, Feedback and Optimization: After the cornfield is irrigated, the digital twin model judges the irrigation effect based on the soil moisture detected in real time by the soil moisture sensor, and dynamically adjusts the irrigation threshold and stop threshold according to the irrigation effect.

[0058] For example, if the detected soil moisture is 80%, and the suitable soil moisture range for corn growth is 60%-70%, after irrigation according to the current irrigation plan, the digital twin model detects through the soil moisture sensor that the soil moisture has reached saturation and exceeds the suitable moisture range for corn growth. At this time, the digital twin model concludes that the irrigation water volume is too high, and then raises the irrigation threshold from 80% (4mm) of ETc to 85% (4.25mm), and the stop threshold from 20% (1mm) of ETc to 25% (1.25mm). Similarly, if the digital twin model detects that the soil moisture after irrigation does not meet the moisture requirements for corn growth, it concludes that the irrigation water volume is insufficient, and thus lowers the irrigation threshold from 80% (4mm) of ETc to 75% (3.75mm), and the stop threshold from 20% (1mm) of ETc to 15% (0.75mm) to ensure that subsequent irrigation can provide sufficient water volume.

[0059] like Figure 4 As shown, the intelligent irrigation control device for cornfields in this embodiment includes the following modules: The data acquisition module includes a weather station 1, which is used to monitor in real time the specific conditions of solar radiation, soil heat flux, air temperature, wind speed, and water vapor pressure in the area where the cornfield is located, and to generate monitoring data. All light sensors are integrated into the weather station 1, and the data acquisition frequency of the light sensors is greater than 1 time / min. In this embodiment, a photosynthetically active radiation sensor is selected as the light sensor.

[0060] The processing module includes a central processing unit 2 electrically connected to the weather station 1. This unit receives monitoring data sent by the weather station 1, calculates the evapotranspiration (ET0) and actual water requirement (ETc) of the corn crop based on the monitoring data, and simultaneously formulates an irrigation plan and generates irrigation control signals. In this embodiment, the central processing unit 2 has wireless communication capabilities, supporting remote monitoring and control.

[0061] The execution module includes an irrigation system 3 electrically connected to the central processing unit 2, used to receive irrigation control signals sent by the processing module and execute the irrigation plan. The irrigation system 3 is selected from either a drip irrigation system or a sprinkler irrigation system. In this embodiment, a drip irrigation system is selected (the dripper flow rate of the drip irrigation system is 2L / h).

[0062] The correction module comprises a soil moisture meter 4 electrically connected with the central processor 2, which is used for monitoring the soil water holding capacity of the corn field in real time and generating holding water data, and the holding water data is fed back to the central processor 2, and the irrigation threshold and the stop threshold set by the staff are corrected by the central processor 2 (when the soil water holding capacity monitored by the soil moisture meter 4 is 75%, and the suitable soil water holding capacity range is 60%-70%, the central processor 2 increases the irrigation threshold and the stop threshold by 5%-10% respectively). In the embodiment, the connection mode of the soil moisture meter 4 and the central processor 2 adopts one of wired connection and wireless connection.

[0063] The specific implementation process is as follows: The weather station 1 first monitors the specific conditions of the light radiation, soil heat flux, air temperature, wind speed and water vapor pressure of the corn field area in real time to generate monitoring data. Among them, the light radiation of the corn field area is monitored and collected by the photosynthetically active radiation sensor integrated in the weather station 1, and the collection frequency is greater than 1 time / min.

[0064] After completing the data collection operation, the weather station 1 sends the generated monitoring data to the central processor 2, so that the central processor 2 calculates the evapotranspiration ET0 of the corn crop and the actual water requirement ETc of the corn crop based on the monitoring data, and synchronously formulates an irrigation scheme and generates an irrigation control signal.

[0065] The central processor 2 controls the operation of the sprinkler system through the irrigation control signal, so that it executes the currently formulated irrigation scheme, and after the irrigation is completed, the soil water holding capacity of the corn field is monitored in real time by the soil moisture meter 4, and holding water data is generated and fed back to the central processor 2, so that the initial values of the irrigation threshold and the stop threshold set by the staff are dynamically corrected.

[0066] The present application calculates the cumulative amount of light by numerical integration, and uses it as the input of the digital twin model, so that the calculated ET0 is closer to the actual energy receiving condition of the corn canopy, and realizes the real demand-based irrigation, thereby reducing the excessive waste of irrigation water.

[0067] At the same time, the present application also improves the calculation accuracy of the evapotranspiration ET0 of the corn crop and the actual water requirement ETc of the corn crop by fusing multi-source data, and by setting the data collection frequency of the light sensor to be greater than 1 time / min, the subtle changes of light intensity can be captured in time, so that the influence of light on the transpiration of corn is more accurately reflected, and the calculation accuracy of the evapotranspiration ET0 of the corn crop and the actual water requirement ETc of the corn crop is further improved.

[0068] Obviously, the above embodiments are merely example for clearly illustrating but not limitation to the embodiments. Based on the above description, other different forms of changes or variations can be made by those skilled in the art. Here, all the embodiments need not and can not be enumerated. The obvious changes or variations derived from the above description are still within the protection scope of the present application.

Claims

1. A corn field intelligent irrigation control method, characterized in that, Comprise the following steps: Step one, data collection: install several light sensors and soil moisture sensors in the corn field, collect the soil moisture data and the light intensity data of the corn canopy in the corn field respectively, and based on the collected light intensity data, use numerical integral algorithm to obtain the light accumulation of the corn field; at the same time, collect the crop coefficient Kc of corn and the leaf area index of corn crop at different growth stages through the internet large model, and collect the meteorological parameters of the location of the corn field through the collection module in the intelligent irrigation control device of the corn field; Step two, water requirement calculation: based on the soil moisture data of the corn field, the light intensity data of the corn canopy, and the crop coefficient Kc of corn, a digital twin model of the corn field is established, the light accumulation of the corn field is input into the digital twin model, the evapotranspiration ET0 of the corn crop is obtained, and then the crop coefficient Kc of corn is input into the digital twin model, so that it and the evapotranspiration ET0 of the corn crop are used as input, and the actual water requirement ETc of the corn crop is calculated through the digital twin model; Step three, irrigation decision: the staff sets the initial values of the irrigation threshold and the stop threshold, monitors the change of the actual water requirement ETc of the corn crop in the past 3 hours through the digital twin model, predicts the change of the corn crop in the future 2 hours, obtains the prediction result, compares the prediction result with the irrigation threshold and the stop threshold respectively, obtains the comparison result, and formulates the irrigation scheme according to the comparison result; Step four, irrigation execution: execute the irrigation scheme through the execution module in the intelligent irrigation control device of the corn field; Step five, feedback optimization: the digital twin model judges the irrigation effect according to the real-time detection of the soil moisture sensor, and dynamically adjusts the irrigation threshold and the stop threshold according to the irrigation effect.

2. The corn field smart irrigation control method of claim 1, wherein, In step one, the meteorological parameters of the location of the corn field include the soil heat flux, air temperature, wind speed and water vapor pressure of the location.

3. The corn field smart irrigation control method of claim 2, wherein, In step two, when calculating the actual water requirement ETc of the corn crop, the Kc value of the corn crop is dynamically adjusted according to the growth stage of the corn crop.

4. The corn field smart irrigation control method of claim 3, wherein, In step two, the growth stage of the corn crop is determined as follows: S1, input the leaf area index of the corn crop at different growth stages collected in step one into the digital twin model, and integrate it into the growth stage library; S2, use the leaf area index monitoring equipment to monitor the leaf area index of the corn crop, and obtain the monitoring result; S3, input the monitoring result into the digital twin model, and compare it with the data in the growth stage library through the digital twin model to obtain the growth stage of the corn crop.

5. The corn field smart irrigation control method of claim 4, wherein, In step two, the evapotranspiration ET0 of the corn crop is calculated by using the Penman formula, and the specific formula is as follows: （1）； wherein, is the net radiation, converted from the accumulated amount of light; is the soil heat flux; is the air temperature; is the wind speed; and are the saturated water vapor pressure and the actual water vapor pressure, respectively; is the slope of the saturated water vapor pressure curve; is the dryness constant.

6. The corn field smart irrigation control method of claim 5, wherein, In step two, the calculation formula of the actual water requirement ETc of the corn crop is as follows: （2）； wherein, is the crop coefficient for corn; is the evapotranspiration for corn crop; and the specific value of is determined by the growth stage of the corn crop.

7. The corn field smart irrigation control method of claim 6, wherein, In step three, the staff sets the initial values of the irrigation threshold and the stop threshold as 80% and 20% of the actual water requirement ETc of the corn crop respectively.

8. A corn field intelligent irrigation control device for the corn field intelligent irrigation control method according to any one of claims 1 to 7, characterized by, Comprise the following modules: The collection module includes a weather station (1) for monitoring the specific conditions of light radiation, soil heat flux, air temperature, wind speed and water vapor pressure of the corn field area in real time and generating monitoring data; The processing module includes a central processing unit (2) electrically connected with the weather station (1) for receiving the monitoring data sent by the weather station (1), calculating the evapotranspiration ET0 of the corn crop and the actual water requirement ETc of the corn crop based on the monitoring data, simultaneously formulating an irrigation scheme and generating an irrigation control signal; The execution module includes an irrigation system (3) electrically connected with the central processing unit (2) for receiving the irrigation control signal sent by the processing module and executing the irrigation scheme; The correction module includes a soil moisture meter (4) electrically connected with the central processing unit (2) for monitoring the soil water holding capacity of the corn field in real time and generating holding water data, feeding back the holding water data to the central processing unit (2) and correcting the irrigation threshold and stop threshold set by the staff by the central processing unit (2).

9. The corn field smart irrigation control device of claim 8, wherein, The light sensors are integrated in the weather station (1), and the data collection frequency of the light sensors is greater than 1 time / min.

10. The corn field smart irrigation control device of claim 9, wherein, The irrigation system (3) selects one of a drip irrigation system and a sprinkler irrigation system.

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