CN110432046B - Intelligent irrigation system in greenhouse - Google Patents
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
- A01G9/00—Cultivation in receptacles, forcing-frames or greenhouses; Edging for beds, lawn or the like
- A01G9/24—Devices or systems for heating, ventilating, regulating temperature, illuminating, or watering, in greenhouses, forcing-frames, or the like
- A01G9/247—Watering arrangements
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Mining
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
- Y02A40/10—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in agriculture
- Y02A40/25—Greenhouse technology, e.g. cooling systems therefor
Abstract
The invention provides an intelligent irrigation system in a greenhouse, which is characterized in that a water demand estimation model is established for total radiation R and water vapor pressure difference VPD in unit time and a transpiration amount ET in unit time, the water demand estimation model is used for predicting the transpiration amount ET, the ET in unit time is accumulated to obtain the total transpiration amount sum (ET), when sum (ET) is less than 20mm, the water demand estimation model uploads data to a controller, the controller controls an environment monitoring module to continue monitoring, and when sum (ET) is more than or equal to 20mm, the water demand estimation model sends an alarm to an early-warning irrigation system through the controller and informs a user of irrigation time and irrigation amount; the problem of current irrigation monitoring system structure complicacy, the suitability is poor, use troublesome, calculation difficulty and prediction precision are poor has effectually been solved, has improved the efficiency of irrigation, has practiced thrift the irrigation water, has reduced the input of manpower and material resources, only needs monitoring temperature, humidity and radiation can predict transpiration volume, easy operation, convenient to use.
Description
The technical field is as follows:
the invention belongs to the technical field of greenhouse crop planting, and particularly relates to an intelligent irrigation system in a greenhouse.
Background art:
as the greenhouse has the sealing and semi-sealing properties, the water demand of crops inside and outside the greenhouse has great difference, for example, research on the water demand of greenhouse horticultural crops by Forgaz et al shows that the water demand of indoor crops is 30% -40% lower than that of outdoor crops in the growing season of the crops, and the height of the greenhouse crops is usually 1.5-2.0 m due to the supporting effect, so that the high crops with the supporting effect can intercept more solar radiation than short crops without the supporting effect, and the large leaf area index LAI and the wide canopy structure can better absorb the solar radiation, thereby improving the hydrothermal migration of the plants, and the water demand is also great.
Therefore, when calculating the water demand of greenhouse crops in the greenhouse, the existing research result of the water demand of field crops cannot be directly applied, but the daily change rule, the change rule in the whole growth period and the annual change rule of the water demand of the greenhouse crops need to be systematically researched to find out main influence factors and influence mechanisms. The water demand of crops is related to meteorological conditions (radiation, temperature, sunlight, humidity and wind speed), soil moisture conditions, crop types and growth and development stages thereof, agricultural technical measures, irrigation drainage measures and the like, and the influence of the factors on the water demand is complicated and interconnected. As for the water demand of greenhouse crops, only qualitative research and analysis are far from sufficient, and how to quantitatively calculate the water demand of the greenhouse crops is the key, so that the establishment of an estimation model of the water demand of the greenhouse crops is necessary.
At present, an estimation model of evapotranspiration in a greenhouse environment mainly comprises an empirical model, a FAO-based crop coefficient method model and a mechanism model. The empirical model is commonly used for greenhouse crop evapotranspiration simulation due to less required parameters, is mainly fit through the relationship between crop evapotranspiration and meteorological elements inside or outside a greenhouse and physiological and ecological indexes of crops, can be widely applied to greenhouses with difficult parameter acquisition or low simulation precision requirements, but needs to further study the applicability to other regions, other greenhouses or other crops due to stronger experience and regionality; the FAO crop coefficient method is a simple and convenient method for estimating crop evapotranspiration, and is widely applied to field crops and trees, and comprises a single crop coefficient method and a double crop coefficient method; the mechanism model is mainly based on the leaf (or canopy) transpiration and leaf energy balance model or the material and energy exchange between the crop and the air. However, these models are complex in structure, difficult to measure parameters, and often limited in practical application.
The invention content is as follows:
aiming at the defects and problems of the existing equipment, the invention provides an intelligent irrigation system in a greenhouse, which effectively solves the problems of complex structure, poor applicability, troublesome use, difficult calculation and poor prediction precision of the existing irrigation monitoring system.
The technical scheme adopted by the invention for solving the technical problems is as follows: an intelligent irrigation monitoring system in a greenhouse, comprising the steps of:
s1, establishing a water demand estimation model;
a, monitoring solar radiation in a greenhouse by using a radiation monitoring module to obtain total radiation R in unit time; monitoring the temperature and humidity in the greenhouse by using a temperature monitoring module and a humidity monitoring module to obtain the average relative humidity RH and the average temperature T in unit time, and obtaining the transpiration ET in unit time by using a transpiration monitoring module;
b, obtaining an average water vapor pressure difference VPD by using the average relative humidity RH and the average temperature T;
and c, performing linear regression on the corresponding groups of total radiations R, the corresponding groups of average water vapor pressure differences VPD and the corresponding groups of transpiration quantities ET to obtain a water demand estimation model.
S2, establishing an intelligent monitoring irrigation system by using a water demand estimation model;
the intelligent monitoring irrigation system comprises an environment monitoring module, a water demand estimation model and an early warning irrigation system, wherein the environment monitoring module comprises a radiation monitoring module, a temperature monitoring module and a humidity monitoring module; the method comprises the steps of obtaining total radiation R in unit time by using a radiation monitoring module, obtaining average temperature T in unit time by using a temperature monitoring module, obtaining average relative humidity RH in unit time by using a humidity monitoring module, obtaining average steam pressure difference VPD according to the average temperature T and the average relative humidity RH, inputting the average steam pressure difference VPD and the total radiation R into a water demand estimation model to obtain a transpiration amount ET in unit time, accumulating the transpiration amounts ET in unit time to obtain a total transpiration amount sum (ET), when sum (ET) is less than 20mm, uploading data to a controller by the water demand estimation model, controlling an environment monitoring module by the controller to continuously monitor the total radiation R, the temperature T and the relative humidity RH in a greenhouse, when sum (ET) is more than or equal to 20mm, sending an alarm to an irrigation system by the controller by the water demand estimation model, and informing a user of irrigation time and irrigation amount, after the user determines that there is an irrigation system to apply a predetermined amount of irrigation at the irrigation time.
Furthermore, the early warning irrigation system also comprises artificial set irrigation, and the irrigation time and the irrigation water quantity can be artificially and automatically set.
Further, the transpiration monitoring module is a transpiration instrument, the transpiration instrument is buried in soil in the middle of the greenhouse, a plurality of seedlings with uniform growth and no plant diseases and insect pests are transplanted and fixedly planted in the transpiration instrument, the spacing and the row spacing of the seedlings are the same as those of a field, when the plants grow to 40cm high, the seedlings are subjected to racking treatment, and the transpiration amount ET in unit time is calculated by using a water balance method;
the concrete formula is as follows: ET ═ Tc·A＝(Wt-1-Wt)/ρ+Ir；
In the formula TcIs the water consumption of the farmland in a time period, which is mm; a is the superficial area of the lysimeter, mm2；Wt-1And WtThe mass g of the soil body in the evapotranspiration at the time t-1 and the time t respectively; rho is the density of water, 1.0g/cm3；IrThe amount of water entering the lysimeter in a time interval is mm3。
Furthermore, the radiation monitoring module is a light quantum sensor, and the temperature monitoring module is used for measuring by a temperature recorder; the humidity monitoring module is used for measuring by a humidity recorder.
Further, the unit time is d, and the unit of d is day.
Further, the water demand estimation model establishes water demand estimation models in a plurality of corresponding time periods according to total radiation R, average temperature T and average relative humidity RH in the plurality of time periods, and estimates the transpiration ET in a plurality of next corresponding time periods by using the water demand estimation models in the plurality of corresponding time periods.
Further, the plurality of time periods are in the unit of months or quarters.
The invention has the beneficial effects that: the invention provides an intelligent irrigation system used in a greenhouse, which utilizes the linear regression of the measured data of total radiation R and water vapor pressure difference VPD and the transpiration amount ET to obtain the linear regression equation of a target function transpiration amount ET and variable total radiation R and water vapor pressure difference VPD, obtains the transpiration amount ET according to the linear regression equation by measuring the total radiation R and the water vapor pressure difference VPD, utilizes the parameters R and VPD which are easy to obtain to measure the transpiration amount ET, automatically sends out early warning when the cumulative amount of the transpiration amount ET is more than 20mm, automatically sends a short message notice to a user, and the user can automatically irrigate after finally confirming the irrigation time and the irrigation amount, namely when the system sends out the irrigation early warning, the user only needs to confirm, and then automatically irrigates according to the calculated irrigation amount according to the irrigation amount and sends out a short message to the user to inform the user after irrigation; meanwhile, the irrigation can be manually set, namely in a manual mode, a user can automatically set the irrigation water quantity according to the agricultural needs or special conditions, and if the irrigation water is used for preserving soil moisture before transplanting, the needed irrigation water quantity is larger; and in the fruit picking period, the irrigation amount is reduced for avoiding fruit cracking, and the like. The user only needs to input this water yield that needs to irrigate in the dialog box of formulating, the system just carries out the volume of irrigating of settlement and irrigates, irrigate and close the water valve by oneself after setting for the water yield, and inform to the user sending the SMS, and the operation is simple, the effectual current irrigation monitoring system structure of having solved is complicated, the suitability is poor, the troublesome poeration, the difficult problem of calculation and prediction precision are poor, the efficiency of irrigation is improved, the irrigation water is practiced thrift, the input of manpower and material resources is reduced, only need monitor temperature, humidity and total radiation can predict the transpiration volume, and the operation is simple and the use is convenient.
Different water demand estimation models are established according to different regions and different time periods, the transpiration amount of the corresponding region and the corresponding time period is subjected to targeted evaluation, the pertinence is strong, the prediction is accurate, the practical range is wide, the operation is simple, the cost is low, and the method is performed in south China and ChinaExperiments in the northern and other regions have good effect, and can realize the water saving of 8-10 m on average3The cost of water and electricity is reduced per mu; the degree of automation of greenhouse planting is improved, crops are irrigated in time, the yield of the crops is improved, and the greenhouse planting method has great popularization and practical values and has great economic and social benefits.
Description of the drawings:
fig. 1 is a dispersion distribution diagram of the measured ET and the simulated ET in 2017.
Fig. 2 is a dispersion plot of the measured ET versus the simulated ET in 2018.
The specific implementation mode is as follows:
the present invention will be described in more detail with reference to the following examples.
Example 1: the embodiment aims to provide an intelligent irrigation system in a greenhouse, which aims at solving the problem that a sunlight greenhouse water consumption estimation model is difficult to be generally applied due to geographical limitation; the parameters are difficult to obtain, and the simulation precision is not high; the model structure and parameters are complex, some parameters are difficult to measure, and the used equipment is expensive. Through observing environmental factors in the greenhouse, such as radiation, temperature, humidity, water vapor pressure, wind speed and the like, and analyzing the relationship between different environmental factors and the transpiration amount ET, the key factors influencing the change of the transpiration amount ET are finally determined to be two factors of total radiation R and water vapor pressure difference VPD, wherein the total radiation R is the total solar radiation amount in unit time and has the unit of MJm-2d-1And performing linear regression on actual measurement data of the total radiation R and the average water vapor pressure difference VPD and ET to obtain a water demand estimation model, wherein the water vapor pressure difference VPD is the average water vapor pressure difference in unit time, and the unit is kPa, and the transpiration ET is the evaporated water level height in unit time, and the unit is mm.
The embodiment provides an intelligent irrigation system of a multi-region sunlight greenhouse, the total radiation R, the temperature and the humidity of the middle position of the greenhouse 2 meters away from the ground surface are measured, the water demand of greenhouse crops can be calculated through 3 parameters, accurate irrigation can be carried out according to the water demand, and the problems of complex structure, poor applicability and difficulty in calculation of the existing irrigation mode are effectively solved; the following description is made of how to obtain a water demand estimation model:
the considered factor is a water vapor diffusion and dissipation reference surface, namely the air volume of the ventilation opening is generally collected at the height of about 2 meters, and then the water vapor is freely diffused at the height and can be diffused to different positions of the greenhouse;
the method comprises the following steps of installing a temperature monitoring module and a humidity monitoring module at a height of 300mm to 2000mm from the ground surface above a plant canopy in a greenhouse, monitoring the temperature and the relative humidity RH in a unit time period in the greenhouse through the temperature monitoring module and the humidity monitoring module respectively, monitoring the total radiation R in the unit time period in the greenhouse by using a radiation monitoring module, recording the total radiation intensity, the average temperature and the average relative humidity in one day by taking the day as a unit, calculating the average water-vapor pressure difference VPD by using the average temperature T and the average relative humidity RH, and adopting the calculation method as follows:
esis the saturated vapor pressure (kPa), T is the air temperature (DEG C);
eaactual water vapor pressure (kPa), RH is relative humidity (%), es is saturated water vapor pressure (kPa)
Water vapor pressure difference VPD ═ es-ea。
Utilize the transpiration monitoring module to monitor the transpiration ET in the greenhouse in unit time quantum, the concrete way is: embedding a lysimeter in soil in the middle of a greenhouse, wherein the depth of the lysimeter embedded in the soil is 1 m, selecting 6 seedlings with uniform growth vigor and no plant diseases and insect pests, planting the seedlings in the lysimeter, wherein the spacing and the row spacing of the seedlings are the same as those of a field and are used for simulating the growth environment of the seedlings in the greenhouse, building frames when the plants grow to 40cm high, and calculating the transpiration ET in unit time by using a water balance method;
the concrete formula is as follows: ET ═ Tc·A＝(Wt-1-Wt)/ρ+Ir；
In the formula TcIs the water consumption of the farmland in a time period, which is mm; a is the superficial area of the lysimeter, mm2；Wt-1And WtThe mass g of the soil body in the evapotranspiration at the time t-1 and the time t respectively; rho is the density of water, 1.0g/cm3；IrThe amount of water entering the lysimeter in a time interval is mm3。
And performing linear regression on the total radiation R and the water vapor pressure difference VPD in the corresponding unit time period and the transpiration ET to obtain a water demand estimation model.
The following is an application of the estimation model for water demand.
A temperature monitoring module and a humidity monitoring module are installed at a position 300mm above a plant canopy in a greenhouse and 2000mm away from the ground surface, the temperature and the relative humidity RH in the greenhouse in a unit time period are monitored through the temperature monitoring module and the humidity monitoring module respectively, the total radiation R in the greenhouse in the unit time period is monitored through the radiation monitoring module, the total radiation, the average temperature and the average relative humidity in one day are recorded and obtained by taking the day as a unit, and the average steam pressure difference VPD is obtained through calculation of the average temperature T and the average relative humidity RH.
Inputting the obtained total radiation R and the average water vapor pressure difference VPD into a water demand estimation model to obtain the transpiration ET; and adding the transpiration amount ET in the unit time to obtain the total transpiration amount sum (ET).
When sum (ET) <20mm, the water demand estimation model uploads the data to the controller, and the controller controls the environment monitoring module to continuously monitor the total radiation R, the temperature T and the relative indoor RH in the greenhouse.
When sum (ET) is more than or equal to 20mm, the water demand estimation model sends an alarm to the early warning irrigation system through the controller, and informs users of irrigation time and irrigation quantity in communication modes such as short messages and apps, and after the user determines that the irrigation time and the irrigation quantity are preset, the irrigation system implements the preset irrigation quantity in the irrigation time.
After the last irrigation is finished, when the accumulated water surface evaporation reaches 20mm, the system automatically sends out early warning, automatically sends short message notification to the user, and the user can finally confirm the irrigation time and the irrigation amount and analogize in turn to realize the automatic irrigation in the automatic greenhouse.
The present embodiment is further described below with reference to examples; in spring, experiments are carried out in north China, as shown in fig. 1, the transpiration amount ET, the total radiation R, the average temperature T and the average relative humidity RH of 4-6 months in 2017 every day are obtained, the average water-vapor pressure difference VPD is obtained according to the average temperature T and the average relative humidity RH, and linear regression is carried out on the total radiation R, the average water-vapor pressure difference VPD and the transpiration amount ET to obtain a water demand estimation model.
The model equation is: ET ═ 0.237R +0.322 VPD-0.649;
the following table is obtained by analyzing the simulation equation and the measured data:
model summary
| Model (model) | R | R side | Adjusting the R square | Error of |
| 1 | .937a | .877 | .875 | .50198 |
a. Predictor variables (constants), VPD, R.
Watch 1
Anovaa
a. Dependent variable ET
b. Predictor variables (constants), VPD, R.
TABLE 2
Coefficient of performancea
a. Dependent variable ET
TABLE 3
As can be seen from tables 1, 2, and 3, the simulated ET obtained by the water demand estimation model established in 2017 and the estimation model of 2017 has a better correlation with the measured ET.
Monitoring total radiation R, average temperature T and average relative humidity RH in 4-6 months in 2018, obtaining average water vapor pressure difference VPD from the average temperature T and the average relative humidity RH, inputting the total radiation R and the average water vapor pressure difference VPD into a simulation equation ET which is established in 2017 and is 0.237R +0.322VPD-0.649, obtaining a simulation ET in 2018, and referring to fig. 2, the measured ET and the simulation ET in 2018 have better correlation.
Therefore, the intelligent irrigation system in the greenhouse provided by the invention obtains the simulated ET by utilizing the water demand estimation model established in 2017 and utilizing the actual measured total radiation R and the average water vapor pressure difference VPD in 2017, the simulated ET not only has higher correlation with the actual measured ET in 2017, but also well predicts the transpiration ET in 2018, the simulated ET in 2018 has better correlation with the actual measured ET in 2018, and has high practicability, namely, the transpiration in the next year is well predicted by utilizing the water demand estimation model established in the last year.
Example 2: the embodiment further illustrates the technical solution of the present invention on the basis of embodiment 1, and the specific contents are as follows: the early warning irrigation system also comprises artificial set irrigation, and the irrigation time and the irrigation water quantity can be artificially and automatically set.
The automatic irrigation means that when the system sends out an irrigation early warning, a user only needs to determine that the system automatically irrigates according to the irrigation quantity calculated by the irrigation, and after irrigation, a short message is sent to the user to inform the user; artificially setting irrigation, namely, a user automatically sets irrigation water quantity according to the agricultural needs or special conditions, and if irrigation water is used for preserving soil moisture before transplanting, the needed irrigation water quantity is larger; and in the fruit picking period, the irrigation amount is reduced for avoiding fruit cracking, and the like. The user only needs to input the water quantity needed to be irrigated in the established dialog box, the system executes the set irrigation quantity to irrigate, the water valve is automatically closed after the water quantity is irrigated to the set water quantity, and a short message is sent to the user to inform the user.
Example 3: the embodiment further illustrates the technical solution of the present invention on the basis of embodiment 1, and the specific contents are as follows: the water demand estimation model establishes water demand estimation models in a plurality of corresponding time periods according to total radiation R, average temperature T and average relative humidity RH in the plurality of time periods, and estimates the transpiration ET in a plurality of next corresponding time periods by using the water demand estimation models in the plurality of corresponding time periods.
The units of the plurality of time periods in this embodiment are quarterly, such as spring, summer, fall, and winter; for example: and evaluating the spring water demand of the next year by using the water demand estimation model of the spring of the last year, dividing the year into four seasons, respectively establishing the water demand estimation models, and evaluating the transpiration ET of the corresponding season of the next year by using the total radiation R and the average water vapor pressure difference VPD of the next year.
By analogy, in the embodiment, the unit of the plurality of time periods is a month, and for example, the transpiration volume in the next month of the year is estimated by using the water demand estimation model established in the previous month of the year.
Different water demand estimation models are established according to different regions and different time periods, the transpiration of the time period corresponding to the corresponding region is estimated, the pertinence is strong, the prediction is accurate, the practical range is wide, the operation is simple, the cost is low, and through experiments in regions such as south China and north China,good effect is obtained, and the water can be saved by 8-10 m on average3The water and electricity costs are reduced, reasonable irrigation not only reduces the situation of high-temperature and high-humidity extreme environment in the greenhouse, but also avoids crop plant diseases and insect pests caused by environmental problems, so that the use amount of pesticides is reduced, the environmental pollution is reduced, and the method has great economic and social benefits and great practical and popularization values.
Claims (5)
1. An intelligent irrigation monitoring system in a greenhouse, which is characterized in that: the method comprises the following steps:
s1, obtaining a water demand estimation model of the greenhouse crops;
a. monitoring solar radiation in the greenhouse by using a radiation monitoring module to obtain total radiation R in unit time; monitoring the temperature and humidity in the greenhouse by using a temperature monitoring module and a humidity monitoring module to obtain the average relative humidity RH and the average temperature T in unit time, and obtaining the transpiration ET in unit time by using a transpiration monitoring module;
the transpiration monitoring module is a transpiration instrument, the transpiration instrument is buried in soil in the middle of the greenhouse, a plurality of seedlings with uniform growth and no plant diseases and insect pests are transplanted and planted in the transpiration instrument, the spacing and the row spacing are the same as those of a field, when the plants grow to 40cm high, the seedlings are subjected to racking treatment, and the transpiration ET in unit time is calculated by using a water quantity balance method; the concrete formula is as follows:
ET＝Tc·A＝(Wt-1-Wt)/ρ+Ir；
in the formula TcIs the water consumption of the farmland in a time period, which is mm; a is the superficial area of the lysimeter, mm2；Wt-1And WtThe mass g of the soil body in the evapotranspiration at the time t-1 and the time t respectively; rho is the density of water, 1.0g/cm3；IrThe amount of water entering the lysimeter in a time interval is mm3
b. Obtaining an average water vapor pressure difference VPD by using the average relative humidity RH and the average temperature T; the calculation method of the average water vapor pressure difference VPD comprises the following steps:
in the formula: e.g. of the typesIs the saturated vapor pressure (kPa), T is the air temperature (DEG C);
in the formula: e.g. of the typeaActual water vapor pressure (kPa), RH is relative humidity (%), es is saturated water vapor pressure (kPa)
Water vapor pressure difference VPD ═ es-ea；
c. Carrying out linear regression on the corresponding multiple groups of daily total radiations R, the multiple groups of daily average water-vapor pressure differences VPD and the multiple groups of daily transpiration ET to obtain a water demand estimation model of the greenhouse crops; the water demand estimation model establishes water demand estimation models in a plurality of corresponding time periods according to total radiation R, average temperature T and average relative humidity RH in the plurality of time periods, and estimates the transpiration ET in the next corresponding time periods by using the water demand estimation models in the plurality of corresponding time periods;
s2, establishing an intelligent monitoring irrigation system by using a water demand estimation model of greenhouse crops;
the intelligent monitoring irrigation system comprises an environment monitoring module, a water demand monitoring module and an early warning irrigation system, wherein the environment monitoring module comprises a radiation monitoring module, a temperature monitoring module and a humidity monitoring module; the method comprises the steps of obtaining total radiation R in unit time by using a radiation monitoring module, obtaining average temperature T in unit time by using a temperature monitoring module, obtaining average relative humidity RH in unit time by using a humidity monitoring module, obtaining average steam pressure difference VPD according to the average temperature T and the average relative humidity RH, inputting the average steam pressure difference VPD and the total radiation R into a water demand estimation model to obtain a transpiration amount ET in unit time, accumulating the transpiration amounts ET in unit time to obtain a total transpiration amount sum (ET), when sum (ET) is less than 20mm, uploading data to a controller by the water demand estimation model, controlling an environment monitoring module by the controller to continuously monitor the total radiation R, the temperature T and the relative indoor RH in a greenhouse, when sum (ET) is more than or equal to 20mm, sending an alarm to an irrigation system by the controller by the water demand estimation model, and informing a user of irrigation time and irrigation amount, after the user determines that there is an irrigation system to apply a predetermined amount of irrigation at the irrigation time.
2. The intelligent irrigation monitoring system in a greenhouse of claim 1, wherein: the early warning irrigation system also comprises artificial set irrigation, and the irrigation time and the irrigation water quantity can be artificially and automatically set.
3. The intelligent irrigation monitoring system in a greenhouse of claim 1, wherein: the radiation monitoring module is a light quantum sensor, and the temperature monitoring module is used for measuring by a temperature recorder; the humidity monitoring module is used for measuring by a humidity recorder.
4. The intelligent irrigation monitoring system in a greenhouse of claim 1, wherein: the unit time is d, and the unit of d is day.
5. The intelligent irrigation monitoring system in a greenhouse of claim 1, wherein: the units of the plurality of time periods are months or quarters.
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