CN108958329B - Drip irrigation water and fertilizer integrated intelligent decision-making method - Google Patents

Drip irrigation water and fertilizer integrated intelligent decision-making method Download PDF

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CN108958329B
CN108958329B CN201810386287.4A CN201810386287A CN108958329B CN 108958329 B CN108958329 B CN 108958329B CN 201810386287 A CN201810386287 A CN 201810386287A CN 108958329 B CN108958329 B CN 108958329B
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李云开
刘畅
刘洋
苏艳平
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China Agricultural University
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D27/00—Simultaneous control of variables covered by two or more of main groups G05D1/00 - G05D25/00
    • G05D27/02—Simultaneous control of variables covered by two or more of main groups G05D1/00 - G05D25/00 characterised by the use of electric means
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
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    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
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    • A01G25/167—Control by humidity of the soil itself or of devices simulating soil or of the atmosphere; Soil humidity sensors
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Abstract

The invention discloses a drip irrigation water and fertilizer integrated intelligent decision-making method in the technical field of drip irrigation intelligent control. The intelligent decision method comprises a drip irrigation and fertilization decision method and database construction, wherein the drip irrigation and decision comprehensively consider the influence of rainfall on the irrigation quantity, and the fertilization decision is carried out simultaneously; the database is built, so that discrete point data are converted into spatial data, and intelligent data mining is realized; the decision method comprehensively considers future meteorological conditions and crop growth requirements, realizes accurate output of irrigation and fertilization amount, can conveniently acquire, manage and mine data, and improves the intelligent degree of irrigation and fertilization decision.

Description

Drip irrigation water and fertilizer integrated intelligent decision-making method
Technical Field
The invention belongs to the technical field of drip irrigation intelligent control, and particularly relates to a drip irrigation water and fertilizer integrated intelligent decision method.
Background
The development of drip irrigation is an important way for realizing water saving, high yield and high efficiency of agriculture, and has great significance for ensuring the safe supply of agricultural products, driving farmers to lose poverty and become rich and promoting the modernization of agriculture. The intelligent drip irrigation is a drip irrigation technology which takes crop growth environment and crop physiological index information acquired by a sensor as a basis, analyzes the water and fertilizer demand condition of crops, provides management parameters such as drip irrigation water and fertilizer application amount and irrigation and fertilizer application time, drives execution equipment such as an electric control valve and a water pump to automatically perform irrigation and fertilizer application operation or provides scientific and reliable irrigation and fertilizer application decision support for drip irrigation managers, has the advantages of more precise control, high water and fertilizer utilization efficiency, low labor intensity and the like, and becomes an important direction for drip irrigation development.
At present, the control and execution part in the intelligent drip irrigation system has developed more maturely, and the intelligent drip irrigation system consisting of a plurality of sensing acquisition devices, driving execution devices and transmission devices is available in the market, so that the following problems mainly exist at present: the system is lack of a water and fertilizer integrated decision process in the drip irrigation water and fertilizer integrated intelligent decision and control process, for example, a patent with the patent number of CN200710179350.9 provides an underground drip irrigation water, fertilizer and pesticide integrated automatic control system and a method, the decision and automatic control of drip irrigation water and fertilizer integration are realized, but the decision process does not relate to fertilization amount calculation, the future precipitation condition is not considered, the repeated irrigation phenomenon easily occurs, and the problem of low rainwater utilization rate is caused; secondly, a great amount of data with different types and formats need to be input, collected and generated in the decision control process, an effective database structure needs to be constructed to effectively process and store the data, so that the difficulty in data management of an intelligent drip irrigation system is reduced, the data accumulation and further data mining of the system are facilitated, and no relevant report exists at present, for example, a patent with the application number of CN201611127292.0 provides an irrigation decision system and method based on an agricultural system model, wherein an irrigation decision model library is provided, but a complete water and fertilizer integrated decision database structure is not constructed. Meanwhile, the decision parameters are obtained through experiments or calculation in specific regions, so that the calculation parameters related to the decision are closely related to the geographic position, the distribution of the calculation parameters on the geographic position is mostly discrete points, and how to estimate the position points and manage the parameters related to the geographic position is of great significance to the intelligent decision of water and fertilizer integration.
Therefore, a decision system integrating water and fertilizer decisions and fully considering rainfall conditions needs to be constructed, a database suitable for the decision system is constructed, data acquisition, management and mining can be facilitated, and the intelligent degree of irrigation and fertilization decisions is improved.
Disclosure of Invention
The invention aims to provide an intelligent drip irrigation water and fertilizer integrated intelligent decision method, which has the following specific technical scheme:
a drip irrigation water and fertilizer integrated intelligent decision method comprises a drip irrigation and fertilizer application decision method and database construction;
the database construction specifically comprises the following steps: establishing a nationwide drip irrigation and fertilization parameter database by utilizing the internet, and directly selecting parameters or inputting local parameter values by a user according to the geographical position to obtain corresponding drip irrigation and fertilization decisions;
the drip irrigation and fertilization decision is as follows: when the soil moisture reaches the underwater irrigation limit value, directly determining whether irrigation is needed or not through an irrigation quantity calculation model according to parameters input by a user and parameters in a comprehensive database, and calculating corresponding irrigation quantity; the fertilization decision is carried out simultaneously, whether fertilization is carried out or not is determined by using a fertilization amount calculation model, and the fertilization amount is calculated; and sending the decision result to an executing device of the intelligent control system.
The crop irrigation quantity calculation in the drip irrigation decision takes the soil water content as a decision index, and when the control system detects that the current soil water content theta (volume water content of soil,%) is lower than the irrigation lower limit value theta set in the irrigation systemminWhen the irrigation is started; calculating weather forecast information and crop water consumption in a water irrigation quantity comprehensive decision-making period, wherein the decision-making period is selected according to the drought tolerance of crops, and the basic range is 5-10 days; preferably, weather forecast information in the decision period is acquired through the Internet and comprises air temperature forecast, precipitation forecast and weather phenomenon forecast; calculating the comprehensive geographical position parameters and weather forecast information of the crop water consumption;
the irrigation quantity calculation model in the drip irrigation decision is as follows:
the effective rainfall in the decision period is calculated according to the formula 1
PeAlpha P formula 1
Wherein P iseRepresenting effective rainfall, wherein alpha is a rainfall infiltration coefficient, and P is future rainfall acquired from weather forecast information; when P is less than 5mm, alpha is 0; when P is 5-50mm, alpha is 1.0-0.8, and in the interval, alpha is reduced along with the increase of P; when P is larger than 50mm, alpha is 0.7-0.8; sigma PeRepresenting the sum of multiple precipitations within the decision period.
(1) Cumulative effective rainfall sigma P in irrigation decision periodeWhen the rainfall does not fall within the decision period, directly calculating the irrigation quantity and immediately irrigating, wherein the irrigation quantity W is1The calculation formula is shown in formula 2:
Figure GDA0002684838160000031
in the formula, W1For the amount of irrigation, unit m3；θmaxThe upper limit of irrigation, namely the volume water content of the target soil to be irrigated,%, is dimensionless; z is the planned wetting layer depth of the crop in mm; p is the wetting ratio of the drip irrigation soil,%, and is dimensionless; m is the area of the irrigation area decided on, and the unit is M2；
(2) Cumulative effective rainfall sigma P in irrigation decision periodeNot more than the maximum irrigation quantity W1' immediately irrigating, wherein the irrigation amount is calculated by supplementing water which cannot be met by rainfall and the water consumption of crops before the date of first rainfall; amount of irrigation W2The calculation formula is shown in formula 3:
Figure GDA0002684838160000041
in the formula, W2For the amount of irrigation, unit m3；θmaxThe upper limit of irrigation, namely the volume water content of the target soil to be irrigated,%, is dimensionless; z is the planned wetting layer depth of the crop in mm; p is the wetting ratio of the drip irrigation soil,%, and is dimensionless; m is the area of the irrigation area decided on, and the unit is M2；∑ETcThe sum of the crop water consumption in unit irrigation area from the present to before the first occurrence of effective rainfall is shown as the unit
Figure GDA0002684838160000042
∑PeIs the sum of effective precipitation in unit irrigation area in forecast period
Figure GDA0002684838160000043
Wherein the maximum amount of irrigation W per unit area of irrigation1' by W1′＝W1The magnification is 1000/M, and the unit is mm.
Wherein the calculation formula of the daily water consumption of the crops is shown as the formula 4:
ETC＝KC·ET0formula 4
In the formula: ETcThe daily water consumption of the crops is in mm; kcIs a crop coefficient, is dimensionless and is determined by the crop growth period, and assuming that the seedling period is 20 days, the seedling period is Kc0.8, the seedling stage is reached within 20 days after sowing, KcTaking 0.8;
the water demand of the reference crop is calculated according to the following formula 5
ET0＝aTmax+bRS+ c formula 5
Wherein, ET0For forecasting the daily water demand of the reference crop using the model, ET according to FAO56 documentation0The evaporation capacity of the dwarf grassland with sufficient soil moisture, complete ground coverage, normal growth and regular height is called as reference crop water demand, and the value is only related to meteorological elements, so that the evaporation capacity can be directly calculated through meteorological parameters, namely potential crop evaporation capacity, and the unit is mm; t ismaxThe daily maximum temperature (. degree. C.); rsMJ/m for actual solar radiation2D; a. b and c are temperature coefficient, radiation coefficient and constant coefficient respectively, are dimensionless and are specifically determined according to the temperature, radiation and ET of a certain region for years0Performing multiple regression analysis to obtain; in the calculation formula, TmaxThe data comes directly from the highest temperature forecast in the weather forecast, because the weather forecast issued by the weather department to the public does not contain the actual solar radiation RsOne item generally only contains descriptions of weather phenomena, such as sunny, sunny turning cloudy, etc., in order to obtain a numerical value of actual solar radiation, the descriptions of the weather phenomena in weather forecast are analyzed by the following method, and a specific formula and a specific method are as follows:
RS＝βRS0formula 6
RS0＝(aS+bS)RaFormula 7
Figure GDA0002684838160000051
Figure GDA0002684838160000052
Figure GDA0002684838160000053
Figure GDA0002684838160000054
Figure GDA0002684838160000055
In the formula, RsActual solar radiation (MJ/m) obtained for conversion2·d)；Rs0Is clear sky radiation (MJ/m)2D), the value is only related to the latitude and the ordinal number of the date in one year, and the beta conversion coefficient is stored in the database of the invention, the corresponding conversion coefficient beta of the weather phenomena in the weather forecast, such as 'rain fall', 'cloudy to clear', 'cloudy', 'rain to clear', and the like, is stored in the database, and the specific conversion coefficient is shown in the table 1; raIs the total solar radiation (MJ/m)2·d)；asIs a regression constant, representing the fraction of radiation that reaches the earth's surface on a dark day, i.e., n is 0; a isS+bSRepresenting the radiation part reaching the earth's surface in a clear and cloudy day, as、bsAccording to the local meteorological data, the local area has no long-series regression relationship between solar radiation and sunlight in China, aS+bSUsually 0.75; gSCThe sun constant is 0.0820 MJ/(m)2·d)；drIs the reciprocal of the relative distance of the day and the ground; omegaSCalculating the solar time angle (radian, rad) according to the geographic latitude and the solar declination;
Figure GDA0002684838160000063
geographic latitude (radians, rad); solar declination (rad); j is the number of days;
TABLE 1 solar radiation conversion coefficient table for common weather phenomena
Figure GDA0002684838160000061
(3) Cumulative effective rainfall sigma P in irrigation decision periodeMaximum irrigation quantity W1', wherein the maximum irrigation quantity W1' by W1′＝W1X 1000/M is converted into mm; at the moment, rainfall is utilized to the maximum extent to save irrigation water, and the method is divided into two types according to whether crops are drought before rainfall:
a. water content theta in root zone of cropiSoil moisture content not greater than crop withering point thetadryThat is, before effective rainfall occurs, crops are affected by drought and should be irrigated immediately to supplement water consumed by the crops from decision time to rainfall occurrence, so as to ensure that the crops are not affected by severe drought and water irrigation quantity W3The calculation formula is shown in formula 13:
Figure GDA0002684838160000062
in the formula, W3For the amount of irrigation, unit m3(ii) a p is the wetting ratio of the drip irrigation soil,%, and is dimensionless; m is the area of the irrigation area decided on, and the unit is M2；∑ETcThe sum of the crop water consumption in unit irrigation area from the prior to the first occurrence of effective rainfall is expressed in mm;
b. water content theta in root zone of cropiSoil water content theta higher than crop withering pointdryNo irrigation;
volumetric water content theta of soildry(%) is an indicator of drought tolerance of the crop, and below this value,the crop growth will be irreversibly affected, and the value, which is usually not allowed to occur in crop growth management, can be set by the control system manager, preferably using the crop wilting point soil moisture value as θdry；
Wherein the water content theta of the root zone of the cropsiThe calculation method is shown in formula 14:
Figure GDA0002684838160000071
wherein i is the number of days before precipitation in decision making, and i is 1,2,3 … …; z is the planned wetting layer depth of the crop in the growth period, and the unit is mm; thetaiPlanning the volume water content (%) of the wet layer for the ith day; theta is the current volume water content (%); sigma ETcThe unit is the sum of the crop water consumption in unit irrigation area from the present to the first time before effective rainfall occurs, and the unit is mm.
The calculation of the fertilizing amount of the crops is the calculation of major elements of nitrogen, phosphorus and potassium required by the crops using irrigation water as a carrier. And (3) calculating the fertilizing amount by adopting a nutrient balance method, estimating the nutrient application amount under the condition of the target yield according to the difference between the target yield and the fertilizer demand of the crops and the soil fertilizer supply amount, and compensating the part with insufficient soil nutrient supply through fertilizing practice so as to maintain the growth and development of the crops.
The calculation model of the fertilization amount in each fertilization decision is shown as the formula 15:
Figure GDA0002684838160000072
wherein F is the fertilizing amount per time, and the unit is kg; fcThe unit of the total amount of nutrients required by the target output per mu at the decision point is kg; s is the amount of the available nutrients in kg of soil per mu at the decision point; u is the current season utilization rate of the fertilizer, and usually takes the values as follows: 30% -45% of nitrogen; 5% -25% of phosphorus; 40% -50% of potassium; f. ofzThe proportion of top dressing for crops in the whole growth period (the proportion of top dressing amount distributed in each growth period) is percent; f. ofiThe proportion of the fertilizer is distributed for the additional fertilizer in the growth periodThe percentage of the fertilizer required by the crop in the growing stage in the whole growing period is as follows; t is tfThe number of the fertilization times is experience, namely the experience value of the fertilization times in the growth period is different for different crops and different growth periods;
wherein, the total amount F of nitrogen, phosphorus or potassium required by each mu of target yield at the decision point is calculatedcCalculated according to equation 16:
Figure GDA0002684838160000081
in the formula, Y is the target yield of the calculated decision point, and is in kg, and the value is manually input; f100The amount of nitrogen, phosphorus or potassium required for a 100kg economic yield of a crop, part of which can be referred to in table 2 below:
TABLE 2100 kg amounts of nitrogen, phosphorus, potassium required for economic production (kg)
Figure GDA0002684838160000082
Calculating the nutrient supply amount S of each mu of soil at the decision point, namely the nutrient amount which can be provided for the crops planted in the season to absorb and utilize by the soil, and calculating according to a formula 17:
S-Ne formula 17
In the formula, N is a soil measured value of nitrogen, phosphorus and potassium at a calculation decision point, namely a soil background value, mg/kg; e is a soil available nutrient correction coefficient used for expressing the fraction of available nutrients in soil which can be utilized by crops, the correction coefficient is obtained through field experiments, dimension-free percent is obtained, if no experiment value is obtained, an empirical value is taken as a default: nitrogen content is 60%; 30% of phosphorus and 40% of potassium;
recording the accumulated fertilizing amount sigma F during each time of water and fertilizer integrated irrigation and judging whether sigma F is larger than F.tfIf the fertilizer is more than or equal to F.t after a certain irrigation fertilizationfAnd irrigating until the next growth period of the crops.
The database converts the parameter point data used by the drip irrigation and fertilization calculation model into the spatial data by using a spatial interpolation method and a data rasterization method, so that the required parameters can be quickly retrieved through geographical coordinates, and reasonable and effective calculation parameters are provided for positions lacking data through the interpolation method; after the database is formed, the data can be cleaned according to the parameters input by the user, and abnormal numerical values in the data are removed.
The spatial interpolation method is a method of converting measurement data of discrete points into a continuous data curved surface so as to compare with distribution patterns of other spatial phenomena. The data of the same region location point can be deduced through known sample data points. In the irrigation and fertilization decision process, a large number of parameters related to spatial geographic positions exist, the parameters are usually obtained by testing or long-term data calculation in a certain place, the points are usually discrete, and the parameters can change along with the change of the geographic positions, parameters a, b and c in the formula 5 are obtained by analyzing and calculating the historical meteorological data of a meteorological site for many years, the values of different sites are different, and the parameters are obtained only by conditioning the sites with perfect meteorological observation historical data, if the values of the parameters of the sites adjacent to the meteorological site are obtained by actual calculation, the parameters are very difficult to obtain, and the climate change has a gentle characteristic on the geographic scale, so that the data of adjacent points can be estimated by a spatial interpolation method in a certain area;
the spatial interpolation method is preferably an inverse distance weight interpolation method, the method obtains unit values by averaging each unit value of adjacent areas, the weighting is inversely proportional to the distance, the closer the point to the center of the prediction unit, the higher the weight, and the calculation formula is shown as formula 18:
Figure GDA0002684838160000101
in formula 18, Z (x)0) Is x0The predicted value of (c); n is the number of sample points around the prediction point to be used in the prediction calculation process; lambda [ alpha ]iFor predicting the weight of each sample point used in the calculation, the value decreases as the distance between the sample point and the predicted point increases; z (x)i) Is at xiObtaining the obtained measured value;
wherein the weight λiThe calculation formula of (c) is shown in equation 19:
Figure GDA0002684838160000102
where p is an exponential value, which significantly affects the result of the interpolation, usually by default to 2; di0Is a predicted point x0And known sampling point xiThe distance between them.
The data rasterization method preferably uses ArcGIS software, and stores the rasterized spatial data distribution map in a database; after the database structure is formed, a certain amount of input and output data can be accumulated through work for a certain time, machine learning training can be carried out by preferably using data mining methods such as a support vector machine and random forests, a new irrigation and fertilization model can be obtained and stored in the database, and calling can be carried out according to the decision requirements of drip irrigation and fertilization.
The decision method is characterized in that fertilization is carried out every time of irrigation, the accumulated fertilization amount sigma F is recorded every time of water and fertilizer integrated irrigation, and if the accumulated fertilization amount sigma F is larger than or equal to F.t after a certain irrigation fertilizationfAnd irrigating until the next growth period of the crops.
An intelligent decision making system for realizing the intelligent decision making method comprises an input module, a crop module, a model parameter module and an output module;
the input module comprises soil moisture data, irrigation area, wetting ratio, geographical position, meteorological data and sowing time;
the crop module comprises a growth period parameter and a crop fertilization parameter, the growth period parameter comprises a crop coefficient, an irrigation upper limit, an irrigation lower limit, a crop drought-receiving index, a planned wetting layer depth and a management suggestion, and the crop fertilization parameter comprises empirical fertilization times, a growth period topdressing distribution ratio and a topdressing ratio;
the model parameter module comprises irrigation quantity calculation and fertilization quantity calculation; the output module comprises fertilizing amount, irrigation amount and management suggestion.
The decision system is also provided with a data cleaning function and a data mining function.
The invention has the beneficial effects that:
(1) according to the drip irrigation water and fertilizer integrated intelligent decision method, future meteorological conditions and crop growth requirements are comprehensively considered according to the intelligent drip irrigation water and fertilizer integrated requirements, the irrigation and fertilizer decision method under the intelligent drip irrigation condition is constructed, accurate irrigation and fertilizer application amount can be output, the decision result is output to the drip irrigation intelligent control system, and the intelligent degree of the decision system is improved;
(2) the drip irrigation water and fertilizer integrated intelligent decision method is based on the database, data in the intelligent drip irrigation decision process are effectively classified, the problems that an intelligent drip irrigation system is multiple in data types and complex in classification are solved, a corresponding data analysis and mining scheme is provided, further optimization of a decision model can be achieved, and the potential value of the data is mined;
(3) the drip irrigation water and fertilizer integration intelligent decision method based database is provided with a data interface, can be applied to a control system, can output decision results as long as input data required by decision are input, and can be applied to a water and fertilizer integrated machine equal to drip irrigation and fertilization related equipment or software;
(4) according to the characteristics of spatial parameters in an intelligent drip irrigation water and fertilizer integrated model, a decision parameter management method based on a difference method and a data rasterization method is provided, numerical values of corresponding parameters can be rapidly acquired or estimated according to the geographical coordinate position of an intelligent drip irrigation project, the problem of dereferencing of irrigation and fertilization parameters and spatial distribution parameters is solved, and the accuracy and efficiency of parameter acquisition are improved.
Drawings
FIG. 1 is a flow chart of irrigation decision calculation in the decision system of the present invention;
FIG. 2 is an exemplary graph of the rasterization result of the temperature coefficient a in formula 5 of the present invention, where the left graph is the distribution diagram of discrete data points before rasterization, and the right graph is the distribution diagram of spatial data after rasterization;
FIG. 3 is a schematic diagram of a drip irrigation water and fertilizer integrated intelligent decision making system provided by the invention;
Detailed Description
The invention provides a drip irrigation water and fertilizer integrated intelligent decision method, which is further described by combining the attached drawings and an embodiment.
FIG. 1 is a flow chart of irrigation decision calculation in a decision system of the present invention, where a control system collects soil moisture parameters and calculates effective rainfall by integrating weather information in a decision period:
if the cumulative effective rainfall sigma P in the irrigation decision periodeIf 0, the condition (1) is satisfied, and the irrigation water amount W is calculated from the equation 21And immediately irrigating;
if the condition (1) is not met, and the accumulated effective rainfall sigma P in the irrigation decision periodeNot more than the maximum irrigation quantity W1If the condition (2) is satisfied, the irrigation water quantity W is calculated from the equation 32And immediately irrigating;
if the condition (2) is not met, accumulating the effective rainfall sigma P in the irrigation decision periodeMaximum irrigation quantity W1If the crop is drought, the irrigation quantity W is calculated according to the formula 133And immediately irrigating; otherwise, irrigation is not required.
FIG. 2 is an exemplary graph of the rasterization result of the temperature coefficient a in formula 5 of the present invention, where the left graph 2-a is a distribution diagram of discrete data points before rasterization, and the right graph 2-b is a distribution diagram of spatial data after rasterization; the data of the point is converted into the spatial data, so that the aim of quickly retrieving the required parameters through the geographic coordinates is fulfilled. The parameters related to the geographic position, including temperature coefficient, radiation coefficient, constant coefficient, nutrient background value and the like, are subjected to rasterization process to convert point data into spatial data, so that a user can quickly retrieve required parameters through geographic coordinates, and can directly call the required parameters from a database through the geographic position parameters to perform model calculation.
Fig. 3 is a simplified diagram of a drip irrigation water and fertilizer integrated intelligent decision system provided by the invention, which specifically comprises an information acquisition equipment and software interface, an input module, a crop module, a model parameter module, an output module and an automatic drip irrigation control system execution device module; the drip irrigation water and fertilizer integrated intelligent decision comprises an input module, a crop module, a model parameter module and an output module.
The input modules include soil moisture data θ, irrigation area M, wetting ratio p, geographic location, meteorological data, and seeding time. The concrete numerical value of the soil moisture data theta is provided by the soil moisture monitor in real time; the wetting ratio p is set by a user who needs to specifically use a decision system, and is a fixed parameter determined by a designer according to the soil texture of the location of crops and engineering when the drip irrigation engineering is designed; the geographic location parameters function as: when making irrigation decisions and fertilizing decisions, the parameters related to the geographic position are called by the database and are also used for the system to obtain corresponding meteorological data from the Internet; the system directly determines the current growth period of crops according to the sowing time, and further determines the parameters of the growth period part.
The input module is used for providing a parameter input window for a user, after the user inputs parameters, data is firstly cleaned before each data is transmitted to the next step, so that abnormal values in the data, including error data such as invalid data exceeding the range of the sensor and negative values, are removed, meanwhile, the data formats of the same type are unified, and intelligent decision errors caused by abnormal acquired values are prevented. The data cleaning principle is to compare whether the value meets the data valid rule, if the volume water content range of soil in a certain area is 5-40%, if the data obtained after filtering correction of the soil water sensor is 50%, the data exceeds the valid rule, the data is subjected to invalid processing, and meanwhile, the data is obtained from the irrigation land where the data meets the rule and is replaced.
The crop module comprises a growth period parameter and a crop fertilization parameter, wherein the growth period parameter comprises a crop coefficient KcUpper limit of irrigation thetamaxLower limit of irrigation thetaminAnd crop drought tolerance index thetadryPlan wetting layer depth z and management suggestion, the crop fertilization parameters comprise empirical fertilization times tfDistribution ratio f of topdressing in growth periodiAnd topdressing ratio fz. Wherein the crop coefficient KcUpper limit of irrigation thetamaxLower limit of irrigation thetaminAnd crop drought tolerance index thetadryThe planned wetting layer depth z is automatically converted by the decision-making system according to the seeding time parameters in the input module, and the specific numerical value is according to the growth periodDifferent and different, and also related to the crop species; the management suggestion is a planting management suggestion which exists in a text form and is suitable for different growth periods of crops. The crop fertilization parameters comprise the empirical fertilization times tfDistribution ratio f of topdressing in growth periodiAnd topdressing ratio fzDifferent crops have different growth periods, and are stored in a database in a classified manner according to the types and the growth periods of the crops; the module parameters can be input and set by a user using a decision-making system, and parameters corresponding to the growth period of the corresponding crops in a database can also be selected and used. The specific parameter values in the database are manually collected and collated by personnel with relevant agricultural knowledge on parameter data of different crops and different regions obtained by long-term field tests, and then are input into the database, or can be determined and input into the database by referring to relevant documents.
The crop module is used for storing parameters related to crop types, is directly called and used by the decision system of the invention during irrigation decision and fertilization decision, is stored in the decision system database of the invention, can be inquired and used by users at any time, and the experienced users can also input related experience values at any time, thereby continuously perfecting the database and achieving the purpose of sharing.
The model parameter module comprises irrigation quantity calculation and fertilization quantity calculation; the irrigation quantity calculating part stores a temperature coefficient a, a radiation coefficient b, a constant coefficient c and a training irrigation model related to the geographic position, specific numerical values of the temperature coefficient a, the radiation coefficient b and the constant coefficient c are obtained by calculating years of accumulated data of the national 700 residual weather stations, the national 700 residual weather station parameters a, b and c are obtained, the data are rasterized to convert the point data into spatial data, and the decision making system is directly called according to the geographic position parameters input by a user for decision making calculation. The parameters required by the fertilization amount calculation are a nutrient background value N, an effective nutrient correction coefficient e, nutrient balance calculation parameters (except N, e, various parameters used in a fertilization amount calculation model) and a training fertilization model, wherein the nutrient background value N refers to the soil nutrient content condition of a decided land mass before sowing, and can be specifically divided into soil nitrogen, phosphorus and potassium background values, and specific numerical values can be obtained by field sampling and testing or obtained by experience values provided by researchers with field experience for years; after the working personnel inputs the experimental values or the empirical values, point data are formed, the point data are converted into space data through data rasterization, and a user can obtain the point data through geographic position retrieval on a human-computer interface and can also directly call a decision irrigation amount and a decision fertilization amount for a decision system according to geographic position parameters input by the user. The irrigation and fertilization training model is a calculation model formed by data mining after long-term fertilization calculation for a user to select.
The output module comprises a fertilizing amount F, a watering amount W and management suggestions, decision values of the fertilizing amount F and the watering amount W are directly sent to execution equipment of the intelligent control system, such as a water pump, an electromagnetic valve, a fertilizer applicator and the like, and the management suggestions comprise agricultural machinery and agricultural technology management measures related to seedling management and pushed to drip irrigation system managers.
The decision method of the drip irrigation water and fertilizer integrated intelligent decision system is carried out according to the following steps:
(1) the method comprises the following steps that a user inputs soil moisture data, irrigation area, wetting ratio, geographical position and sowing time at a using end, an input module directly converts geographical position parameters into meteorological data parameters according to the Internet, and abnormal values are eliminated through data cleaning;
(2) and when the soil moisture reaches the underwater irrigation limit value, performing an irrigation decision: the decision making system directly determines whether irrigation is carried out or not through an irrigation quantity calculation model according to parameters input by a user and parameters in the comprehensive database, and calculates corresponding irrigation quantities; meanwhile, fertilizing decision is carried out, and a decision result is sent to an executing device of the intelligent control system;
(3) the system automatically judges the growth period of crops and pushes agricultural machinery and agricultural technology management measures related to seedling management to drip irrigation system management personnel.
The decision method is used for fertilizing every time of irrigation, and parameter data related in the decision process is provided by the database.
Example 1
The construction of a decision system and a database of an intelligent drip irrigation project in a Tongzhou area in Beijing is taken as an example for explanation, the project is used for planting summer corns, each cell is provided with a sensor capable of collecting soil moisture, and the irrigation decision period is set to be 10 days.
In the data acquisition step, 0% appears in the acquired real-time soil moisture data, the value is found out to be out of the normal data range in the data cleaning process, the value is removed from the acquired data, and the acquired soil moisture, meteorological data, irrigation area, wetting ratio, summer corn sowing time, geographical position and the like are input into the data base to be stored. And when the soil moisture reaches the lower limit, carrying out irrigation decision, wherein the specific decision process is as follows:
the values of the parameters a, b, c, located in the Tongzhou district in this example, were retrieved in a spatial database by coordinates to determine the specific values of the parameter a as 0.454, b as 0.1819, and c as-0.5759.
The decision-making system automatically obtains the future 10 weather forecast from the website of the meteorological department according to the geographical position, and calculates the future effective rainfall, in this embodiment, the effective rainfall is 0 in the decision-making period, and the irrigation water quantity W is1Comprises the following steps:
Figure GDA0002684838160000161
upper limit of irrigation thetamax30% (volume water content), 19% (volume water content) of soil water content theta, 400mm of z, 30% of p, 6670M of M2The irrigation quantity W is obtained by the calculation of the formula1＝88.04m3The maximum irrigation quantity W in the unit irrigation area1′＝13.2mm。
And (3) entering a fertilization decision, wherein as the test data is output unconditionally in the local, the basic value of the available nitrogen in Tongzhou district in Beijing is taken as 160mg/kg by taking the local basic nutrient data and the nitrogen fertilizer decision as an example, and the nutrient correction coefficient e is taken as 60%, the available nutrient amount S of the soil per mu at the decision point is as follows:
0.15X 160mg/kg X60% ═ 14.4 kg/mu
The user sets the target yield Y to be 600kg and the nitrogen F required by the economic yield of 100kg of crops100Taking 3.4kg, and calculating the total nutrient amount Fc required by the target yield per mu at the decision point as follows:
Figure GDA0002684838160000171
the seasonal utilization rate U of the fertilizer is 35 percent; retrieving data in a database, topdressing ratio fzIs 60 percent; distribution ratio f of topdressing in growth period required in seedling periodi10 percent; the empirical value t of the fertilization times in the growth periodfAnd 2, the fertilizing amount F at this time is as follows:
Figure GDA0002684838160000172
outputting a result that 0.5kg of nitrogen fertilizer needs to be applied, carrying out irrigation, and sending a decision result to an execution device of the intelligent control system; and (4) judging that the summer corn is in the seedling stage at present, and pushing agricultural and agronomic management measures related to seedling stage management to drip irrigation system management personnel.

Claims (9)

1. A drip irrigation water and fertilizer integrated intelligent decision method is characterized in that the intelligent decision method comprises a drip irrigation and fertilizer application decision method and database construction;
the database construction specifically comprises the following steps: establishing a nationwide drip irrigation and fertilization parameter database by utilizing the internet, and directly selecting parameters or inputting local parameter values by a user according to the geographical position to obtain corresponding drip irrigation and fertilization decisions;
the drip irrigation and fertilization decision is as follows: when the soil moisture reaches the underwater irrigation limit value, directly determining whether irrigation is needed or not through an irrigation quantity calculation model according to parameters input by a user and parameters in a comprehensive database, and calculating corresponding irrigation quantity; the fertilization decision is carried out simultaneously, whether fertilization is carried out or not is determined by using a fertilization amount calculation model, and the fertilization amount is calculated; sending the decision result to an executing device of the intelligent control system;
according to the accumulated effective rainfall sigma P in the irrigation decision periodeDetermining the precipitation condition:
∑Pethe sum of effective precipitation in unit irrigation area in the forecast period is in mm;
Pealpha is a rainfall infiltration coefficient, and P is the future rainfall obtained from weather forecast information; when P is less than 5mm, alpha is 0; when P is 5-50mm, taking 1.0-0.8; when the rainfall is more than 50mm, taking 0.7-0.8;
(1) cumulative effective rainfall sigma P in irrigation decision periodeCalculating the irrigation quantity as 0, and immediately irrigating;
(2) cumulative effective rainfall sigma P in irrigation decision periodeNot more than the maximum irrigation quantity W1', calculating the irrigation quantity and immediately irrigating;
(3) cumulative effective rainfall sigma P in irrigation decision periodeMaximum irrigation quantity W1', there are two categories of conditions depending on whether the crop is drought before precipitation:
a. water content theta in root zone of cropiSoil moisture content not greater than crop withering point thetadryImmediately irrigating;
b. water content theta in root zone of cropiSoil water content theta higher than crop withering pointdryNo irrigation;
W1' is the maximum irrigation per unit irrigation area; theta is the water content percentage of the soil volume detection, and is dimensionless.
2. The intelligent decision-making method according to claim 1, wherein the irrigation quantity calculation model in the drip irrigation decision is as follows:
(1) cumulative effective rainfall sigma P in irrigation decision periodeWater filling quantity W is equal to 01The calculation formula is shown in formula 2:
Figure FDA0002507050300000021
(2) cumulative effective rainfall sigma P in irrigation decision periodeNot more than the maximum irrigation quantity W1', amount of irrigation W2The calculation formula is shown in formula 3:
Figure FDA0002507050300000022
(3) cumulative effective rainfall sigma P in irrigation decision periodeMaximum irrigation quantity W1′：
a. Water content theta in root zone of cropiSoil moisture content not greater than crop withering point thetadryAmount of irrigation W3The calculation formula is shown in formula 13:
Figure FDA0002507050300000023
in formulae 2,3 and 13, W1、W2、W3For the amount of irrigation, unit m3；θmaxThe upper limit of irrigation, namely the volume water content of the target soil to be irrigated,%, is dimensionless; theta is the water content percentage of the soil volume detection, and is dimensionless; z is the planned wetting layer depth of the crop in mm; p is the wetting ratio of the drip irrigation soil,%, and is dimensionless; m is the area of the irrigation area decided on, and the unit is M2；∑ETcThe sum of the crop water consumption in unit irrigation area from the prior to the first occurrence of effective rainfall is expressed in mm;
W1′＝W11000/M in mm.
3. The intelligent decision-making method according to claim 1, wherein the fertilization amount calculation model in the fertilization decision is as follows:
Figure FDA0002507050300000031
in the formula 15, F is the fertilizing amount per time and the unit is kg; fcThe unit of the total amount of nutrients required by the target output per mu at the decision point is kg; s is the amount of the available nutrients in kg of soil per mu at the decision point; u is the season utilization rate of the fertilizer,%; f. ofzThe proportion of top dressing of crops in the whole growth period is percent; f. ofiThe proportion of the fertilizer is distributed for the additional fertilizer in the growth period; t is tfThe number of fertilization times is empirical.
4. The intelligent decision-making method according to claim 1, wherein the database construction converts the parameter point data used by the drip irrigation and fertilization calculation model into spatial data by using a spatial interpolation method and a data rasterization method;
the spatial interpolation method is preferably an inverse distance weight interpolation method, and the calculation formula is shown as formula 18:
Figure FDA0002507050300000032
in formula 18, Z (x)0) Is x0The predicted value of (c); n is the number of sample points around the prediction point to be used in the prediction calculation process; lambda [ alpha ]iFor predicting the weight of each sample point used in the calculation, the value decreases as the distance between the sample point and the predicted point increases; z (x)i) Is at xiObtaining the obtained measured value;
the data rasterization method preferably uses ArcGIS software.
5. An intelligent decision method according to claim 1, characterized in that after the database is formed, data mining is preferably performed by using a support vector machine and a random forest to obtain a new fertigation model.
6. The intelligent decision-making method according to claim 1, characterized in that after the database is formed, the data can be cleaned for the parameters input by the user to remove abnormal values in the data.
7. An intelligent decision method as claimed in claim 1, wherein the decision method performs fertilization every irrigation, records the cumulative fertilization amount sigma F every time the water and fertilizer are irrigated integrally, and if the cumulative fertilization amount sigma F is larger than or equal to F.t after a certain irrigation fertilizationfIrrigation after the growth period is not fertilized any more untilThe next growth period of the crop.
8. An intelligent decision making system based on the intelligent decision making method according to any one of claims 1 to 7, wherein the intelligent decision making system comprises an input module, a crop module, a model parameter module and an output module;
the input module comprises soil moisture data, irrigation area, wetting ratio, geographical position, meteorological data and sowing time;
the crop module comprises a growth period parameter and a crop fertilization parameter, the growth period parameter comprises a crop coefficient, an irrigation upper limit, an irrigation lower limit, a crop drought-enduring index, a planned wetting layer and a management suggestion, and the crop fertilization parameter comprises empirical fertilization times, a growth period topdressing distribution ratio and a topdressing ratio;
the model parameter module comprises irrigation quantity calculation and fertilization quantity calculation; the output module comprises fertilizing amount, irrigation amount and management suggestion.
9. The intelligent decision making system according to claim 8, wherein after the user inputs the geographical location or selects the corresponding parameters in the input module at the use end, the intelligent decision making system makes the decision of drip irrigation and fertilization, automatically judges the growth period of crops, and pushes agricultural and agronomic management measures related to seedling management to the management personnel of the drip irrigation system.
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Publication number Priority date Publication date Assignee Title
CN110235583B (en) * 2019-07-22 2022-03-22 深圳市芭田生态工程股份有限公司 Method and device for analyzing matching degree of fertilizer and crops
CN111670672B (en) * 2020-05-28 2021-07-02 中国水利水电科学研究院 Rice field irrigation water and fertilizer variable control system and device
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CN113396680B (en) * 2021-07-01 2022-03-04 中国水利水电科学研究院 Drip irrigation water and fertilizer integrated irrigation and fertilization method
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CN115486358A (en) * 2022-09-02 2022-12-20 水利部牧区水利科学研究所 Perennial forage grass drip irrigation automatic irrigation management decision control system
CN117616961B (en) * 2024-01-15 2024-04-30 中国农业科学院农业环境与可持续发展研究所 Drip irrigation water and fertilizer control system and method

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1631098A (en) * 2003-12-25 2005-06-29 中国农业大学 Insufficient irrigation forecast and control method
CN103477948A (en) * 2013-09-30 2014-01-01 中国农业大学 Irrigation control method and system for saline-alkali soil
CN104521699A (en) * 2014-11-18 2015-04-22 华北水利水电大学 Field intelligent irrigation on-line control management method
CN105230450A (en) * 2015-09-15 2016-01-13 中国农业大学 Intelligent device and method for irrigation rapid diagnosis
CN106508622A (en) * 2016-11-11 2017-03-22 河北农业大学 Automatic irrigation control method based on water balance model
CN106707767A (en) * 2017-03-13 2017-05-24 山东农业大学 System and method for integrally and intelligently controlling water and fertilizer in field based on multi-source information fusion

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070010915A1 (en) * 2005-07-07 2007-01-11 Pioneer Sales, Ltd. Weather monitor and irrigation overrride system with unique system identifier
NZ562316A (en) * 2007-10-09 2009-03-31 New Zealand Inst For Crop And Method and system of managing performance of a tuber crop
US9880537B2 (en) * 2015-08-05 2018-01-30 Clearag, Inc. Customized land surface modeling for irrigation decision support in a crop and agronomic advisory service in precision agriculture
US10028426B2 (en) * 2015-04-17 2018-07-24 360 Yield Center, Llc Agronomic systems, methods and apparatuses
US20170248927A1 (en) * 2016-02-29 2017-08-31 Energy Control Technologies, Inc. Weather adaptive control of an irrigation system
CN106359005B (en) * 2016-08-31 2019-04-16 内蒙古农业大学 One inter-species makees the automatic irrigation device and automatic irrigation method in farmland

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1631098A (en) * 2003-12-25 2005-06-29 中国农业大学 Insufficient irrigation forecast and control method
CN103477948A (en) * 2013-09-30 2014-01-01 中国农业大学 Irrigation control method and system for saline-alkali soil
CN104521699A (en) * 2014-11-18 2015-04-22 华北水利水电大学 Field intelligent irrigation on-line control management method
CN105230450A (en) * 2015-09-15 2016-01-13 中国农业大学 Intelligent device and method for irrigation rapid diagnosis
CN106508622A (en) * 2016-11-11 2017-03-22 河北农业大学 Automatic irrigation control method based on water balance model
CN106707767A (en) * 2017-03-13 2017-05-24 山东农业大学 System and method for integrally and intelligently controlling water and fertilizer in field based on multi-source information fusion

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
SH矮砧苹果幼树滴灌条件下需水特性与生理指标响应研究;贾俊杰;《中国优秀硕士学位论文全文数据库农业科技辑》;20180131(第1期);全文 *
基于Web的农田实时灌溉管理系统;马建琴;《水电能源科学》;20111231(第12期);全文 *
基于组件式GIS的四川丘区测土配方施肥信息系统研制-以安县为例;钟文挺;《中国优秀硕士学位论文全文数据库农业科技辑》;20110430(第4期);1-40 *

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