CN112450056A - Water, fertilizer and pesticide integrated intelligent irrigation system based on machine learning algorithm - Google Patents
Water, fertilizer and pesticide integrated intelligent irrigation system based on machine learning algorithm Download PDFInfo
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Images
Classifications
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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
- A01G25/00—Watering gardens, fields, sports grounds or the like
- A01G25/16—Control of watering
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01C—PLANTING; SOWING; FERTILISING
- A01C23/00—Distributing devices specially adapted for liquid manure or other fertilising liquid, including ammonia, e.g. transport tanks or sprinkling wagons
- A01C23/007—Metering or regulating systems
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01C—PLANTING; SOWING; FERTILISING
- A01C23/00—Distributing devices specially adapted for liquid manure or other fertilising liquid, including ammonia, e.g. transport tanks or sprinkling wagons
- A01C23/04—Distributing under pressure; Distributing mud; Adaptation of watering systems for fertilising-liquids
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01C—PLANTING; SOWING; FERTILISING
- A01C23/00—Distributing devices specially adapted for liquid manure or other fertilising liquid, including ammonia, e.g. transport tanks or sprinkling wagons
- A01C23/04—Distributing under pressure; Distributing mud; Adaptation of watering systems for fertilising-liquids
- A01C23/042—Adding fertiliser to watering systems
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01M—CATCHING, TRAPPING OR SCARING OF ANIMALS; APPARATUS FOR THE DESTRUCTION OF NOXIOUS ANIMALS OR NOXIOUS PLANTS
- A01M7/00—Special adaptations or arrangements of liquid-spraying apparatus for purposes covered by this subclass
- A01M7/0089—Regulating or controlling systems
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- A—HUMAN NECESSITIES
- A23—FOODS OR FOODSTUFFS; TREATMENT THEREOF, NOT COVERED BY OTHER CLASSES
- A23L—FOODS, FOODSTUFFS, OR NON-ALCOHOLIC BEVERAGES, NOT COVERED BY SUBCLASSES A21D OR A23B-A23J; THEIR PREPARATION OR TREATMENT, e.g. COOKING, MODIFICATION OF NUTRITIVE QUALITIES, PHYSICAL TREATMENT; PRESERVATION OF FOODS OR FOODSTUFFS, IN GENERAL
- A23L31/00—Edible extracts or preparations of fungi; Preparation or treatment thereof
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
Abstract
The invention discloses a remote-control water-fertilizer-pesticide integrated intelligent irrigation system which comprises an environmental data acquisition unit, an intelligent data analysis system, a remote control terminal, a water-fertilizer-pesticide integrated irrigation system and a field pipeline system. The environment data acquisition unit is used for acquiring environment parameters; the intelligent data analysis system is used for data storage, data analysis and water, fertilizer and pesticide integrated scheme output; the remote control terminal is used for checking environmental data, a water-fertilizer-pesticide integrated irrigation scheme and crop field growth vigor and remotely controlling the water-fertilizer-pesticide integrated irrigation system; the water-fertilizer-pesticide integrated irrigation system is used for executing a water-fertilizer-pesticide integrated irrigation scheme. The invention can carry out irrigation, fertilization and pesticide application in a targeted manner according to the water and fertilizer requirement rule and plant diseases and insect pests of crops in each growth period, can adjust the irrigation, fertilization and pesticide application schemes in time according to soil moisture content, nutrients and weather conditions, can carry out remote high-efficiency control, and realizes water-fertilizer-pesticide integrated intelligent irrigation.
Description
Technical Field
The invention belongs to the technical field of scientific and intelligent crop growth management, and particularly relates to a water-fertilizer-pesticide integrated intelligent irrigation system based on a machine learning algorithm.
Background
The method comprises the steps of analyzing relevant data of soil, climate and plants collected by the front end of an automatic monitoring system, processing data results and making a water-fertilizer-pesticide implementation scheme, carrying out water-fertilizer irrigation on farmland crops, providing a practical and efficient automatic agricultural irrigation system for crop management, and applying the system in a plurality of demonstration bases and in a row of camps. Water, fertilizer and pesticide integration technology has been applied to agriculture for many years, has more mature technical solution, mostly is semi-automatic manual control at present, and there is full job flow automation intellectuality, and the development of wisdom agricultural irrigation field mainly faces the challenge in the aspect of comprehensive factor decision support, the long-range unmanned control of thing networking, data analysis decision accuracy at present. The method comprises the following specific steps:
at present, in the irrigation decision of the existing orchard accurate irrigation system in the industry, only one or two factors (such as soil humidity) are generally considered, and various factors are rarely and comprehensively considered for carrying out application decision by multiple indexes; most of fertilizer and pesticide application is implemented by manual analysis design implementation schemes, so that the efficiency is low, a data analysis model is manufactured, the implementation schemes have large limitations on knowledge, and automation, intellectualization and precision cannot be realized; the method comprises the steps of accurately extracting under the condition that a data acquisition unit is difficult to achieve multi-factor influence, effectively monitoring and evaluating the implemented effect, and continuously optimizing implementation reference data and the process method due to the fact that feedback cannot be carried out.
A scientific model and data of crop health are not established, a water and fertilizer application scheme needs to be made by manually analyzing the data of the data acquisition monitoring unit, and the water and fertilizer application system has insufficient flexibility and cannot be divided into multiple time intervals, the flow is refined and the combined implementation is realized; the data acquisition monitoring module, the data analysis decision-making module and the water and fertilizer irrigation implementation system do not establish a data association feedback optimization mechanism, need manual connection, and cannot realize automation, intellectualization and continuous optimization.
The implementation effect of the water and fertilizer is not monitored and data is acquired, the effect after application is not analyzed and evaluated, an application effect data model is not established, and the application evaluation and the optimization and the improvement of an application scheme are not facilitated.
When the data monitoring and collecting unit collects monitoring data, the influence of various influence factors on the precision cannot establish a scientific precision model, and related reference crop health data cannot be completely fit with geographical climate, species decay and species evolution difference, so that data errors from two aspects of data collection and data processing can be caused, and the data analysis method cannot be continuously optimized.
Data updating among the three modules is not related, influence factors cannot be continuously updated into the data, a related data updating mechanism among the three modules is not established, flow methods of the three modules cannot be continuously optimized, and errors caused by various factors such as different crop growth periods and environmental changes cannot be controlled.
The problems lead to inaccurate and unscientific implementation of water and fertilizer medicines of crops, poor implementation effect, influence on the healthy growth and development of crops, influence on the yield and quality of agricultural products and reduction of the economic benefit of crops.
The machine learning neural algorithm technology is practically applied in recent years, and is a subject for a computer to construct a probability statistical model based on data and predict and analyze the data by using the model; the method simulates or realizes the learning behavior of human beings to acquire new knowledge or skills, reorganizes the existing knowledge structure to continuously improve the performance of the knowledge structure, reconstructs a reconstruction flow method and a reference data base to lead the knowledge structure to be infinite and tend to be accurate, is the core of artificial intelligence, and is a fundamental way for leading a computer to have intelligence.
Disclosure of Invention
The invention aims to apply a machine learning technology based on a multi-factor comprehensive accurate algorithm, standardize process management, reduce uncontrollable factors, establish a comprehensive algorithm regulation mechanism for each factor, train water and fertilizer application data of crops according to data of each time, and calculate a correlation model of environment attributes, crop attributes, water and fertilizer application data and application effects by data test classification regression. Thereby achieving the accurate management of water, fertilizer and pesticide and improving the quality and the yield of agricultural products.
A machine learning algorithm-based water-fertilizer-pesticide integrated intelligent irrigation system is characterized by comprising a remote control terminal, an intelligent data analysis system, an environmental data acquisition unit, a water-fertilizer-pesticide integrated control system and a field pipeline system; the remote control terminal, the environmental data acquisition unit and the water, fertilizer and pesticide integrated control system are respectively connected with the intelligent data analysis system through a mobile internet or a Wide Area Network (WAN); the remote control terminal is used for checking environmental data, a water-fertilizer-pesticide integrated irrigation scheme and crop field growth vigor and remotely regulating the water-fertilizer-pesticide integrated irrigation scheme; the environment data acquisition unit is used for acquiring environment parameters; the intelligent data analysis system is used for storing water demand data, nutrient demand data, pest control agent data, soil monitoring data, meteorological monitoring data and pest monitoring data, and outputting a water-fertilizer-pesticide integrated scheme; the water-fertilizer-pesticide integrated control system is used for executing irrigation, fertilizer application, pesticide application, water-fertilizer integration and water-fertilizer-pesticide integrated irrigation schemes.
The remote control terminal comprises a smart phone, a tablet computer, a notebook computer and a desktop computer. The smart phone and the tablet personal computer check environmental monitoring data and pest monitoring data, check a water-fertilizer-pesticide integrated scheme and remotely regulate and control through an APP management interface; the notebook computer and the desktop computer check environmental monitoring data and pest monitoring data through a PC end webpage, and check and remotely regulate and control a water-fertilizer-pesticide integrated scheme.
The environmental data acquisition unit comprises a soil moisture content monitor, a soil nutrient monitor, a meteorological monitoring station, a camera and a communication module which are distributed in the field. The soil moisture content monitor can monitor soil moisture, temperature, pH value and EC value; the soil nutrient monitor can monitor the nutrient contents of nitrogen (N), phosphorus (P) and potassium (K) in soil; the weather monitoring station can monitor air temperature, humidity, rainfall and evaporation capacity; the environment data acquisition unit transmits soil monitoring data and meteorological monitoring data to an intelligent data analysis system for storage, analysis and intelligent scheme output by using a communication module through a mobile internet (GPRS) or a Wide Area Network (WAN).
The water-fertilizer-pesticide integrated control system comprises a communication module, a control console, a water tank, a water pump, an N fertilizer mother liquor tank, a P fertilizer mother liquor tank, a K fertilizer mother liquor tank, a micro fertilizer mother liquor tank, a pesticide mother liquor tank, a fertilizer applicator, a filtering system, a wire cable, a valve system and a field pipeline system. The communication module comprises a GPRS wireless communication module and a WAN wide area network communication module; the control console comprises a display screen and an operation module, wherein the display screen is used for displaying parameters of the currently implemented or to-be-implemented water-fertilizer-pesticide integration scheme and real-time field images, and the operation module is used for modifying the parameters of the water-fertilizer-pesticide integration scheme and checking video recordings; the valve system comprises an electromagnetic valve, a timing assembly, a flow control assembly and a pressure regulator, and is positioned on a pipeline connected among the assemblies; the control console controls the valve system through a cable, the valve system controls the opening and closing time of the valve system through the timing assembly, the opening degree of the valve system is controlled through the flow control assembly, and the flowing time and flow of water, fertilizer liquid and liquid medicine are controlled through the opening and closing time and the opening degree of the valve system; the field pipeline system comprises a PVC pipeline, an electromagnetic valve, a pressure regulator and a drip irrigation belt (a sprinkler irrigation head); the water tank and the water pump, the water pump and the fertilizer mother liquor tank and the medicament mother liquor tank, the fertilizer mother liquor tank and the fertilizer applicator, the water pump and the filtering system, the filtering system and the fertilizer mother liquor tank, and the filtering system and the field pipeline are connected through PVC pipelines.
The intelligent data analysis system comprises a communication module, a data storage module, a data analysis module and a scheme output module. The communication module comprises a GPRS wireless communication module and a WAN wide area network communication module; the data storage module comprises a water demand database, a nutrient demand database, a pest control medicament database, a meteorological monitoring database, a soil monitoring database and a pest monitoring database; the water demand database, the nutrient demand database and the pest control agent database are input by technical personnel through an intelligent data analysis system background; the disease and pest monitoring data are input by plant protection personnel of a planting base through a disease and pest detecting and reporting interface of a remote control terminal; the data analysis module analyzes and gives out an irrigation scheme through crop moisture demand data, soil monitoring data and meteorological monitoring data, analyzes and gives out a fertilization scheme through crop nutrient demand data, soil monitoring data and meteorological monitoring data, gives out a pesticide application scheme through pest monitoring data, meteorological monitoring data and pest control medicament data, and gives out a water-fertilizer-integrated and water-fertilizer-medicament-integrated irrigation scheme according to the superposition of irrigation, fertilization and pesticide application in time; the scheme output module transmits the irrigation scheme, the fertilization scheme, the pesticide application scheme, the water-fertilizer-pesticide integrated scheme and the water-fertilizer-pesticide integrated irrigation scheme to the water-fertilizer-pesticide integrated control system through a mobile internet (GPRS) or a Wide Area Network (WAN) by utilizing the communication module.
The machine learning algorithm-based water-fertilizer-pesticide integrated intelligent irrigation system has the main technical advantages that: complete, continuously updated, scientific and accurate crop health data. Data acquisition, analysis and formulation of a scheme, implementation and effect evaluation are integrated, a full-automatic seamless process is established, and data intercommunication is realized. An optimization transformation mechanism is established for the method flow of each module, and a factor difference comprehensive process is established for the data acquisition module. The modules are causal and feed back mutually, and the data models in the modules are continuously optimized.
The implementation effect evaluation system is a networking system, so that each terminal user using the system can share implementation effect evaluation data, and two key indexes can be evaluated after each implementation: an implementation effect bias value and an implementation effect value. The two values provide training data for the intelligent data analysis system to establish a linear regression algorithm. And training a verification data set with excellent implementation effect and extremely small implementation effect deviation value.
The environment data acquisition unit quantifies and standardizes the acquired value of the irregular attribute data. The data acquisition unit comprises: soil moisture, temperature, pH, EC, soil nitrogen (N), phosphorus (P), potassium (K), trace element nutrient content, air temperature, humidity, rainfall, evaporation, climate change in future time, CO2Concentration, illumination intensity, bud size, leaf color, leaf size, fruit number, leaf number, tree vigor, growth period, insect pest situation detection data, disease and pest statistical data and weed damage monitoring data.
The intelligent data analysis system utilizes a linear regression algorithm to establish a data model among environment attribute data, implementation data values and evaluation effect values, prunes accurate implementation data, predicts and broadens implementation data distribution, establishes a data matrix X based on an attribute value database, an implementation database and an effect evaluation database, obtains a most appropriate vector w according to the fact that an expected value vector (implementation effect value) y is known, and enables a linear equation set to meet linear distribution of sample points as far as possible, and then predicts and generates water and fertilizer medicine implementation scheme data which tend to be accurate by utilizing the obtained w.
The intelligent data analysis system comprises the following specific steps:
generating sample data based on the environment attribute data, the implementation scheme data and the effect evaluation data, and performing data standardization preprocessing;
calling a linear regression algorithm to minimize the error between the y value and the predicted value, and training to obtain a linear regression model;
predicting implementation scheme data by using a linear regression model, and storing the implementation scheme data in an implementation scheme database;
executing the implementation scheme to obtain an implementation effect and storing the implementation effect in an effect evaluation database;
and triggering a linear regression algorithm, and training a correlation model of the environment attribute, the crop attribute, the water and fertilizer pesticide application data and the application effect again to improve the abundance and the accuracy of the data.
Linear algorithm: the error between the actual y value and the predicted value is evaluated using the squared error:
modification is carried out in a matrix form:
(y-Xw)T(y-Xw)
minimize the squared error, derive w:
XT(y-Xw)
let the above equation equal 0, then find:
the invention provides a machine learning algorithm-based water, fertilizer and pesticide integrated intelligent irrigation system, which has the beneficial effects that: the crop water and fertilizer requirement rule and the pest control medicament are digitally stored in an intelligent data analysis system, and a highly targeted irrigation, fertilization, pesticide application, water and fertilizer integration and water, fertilizer and pesticide integration irrigation scheme can be given according to the water and fertilizer requirement rule and the pest monitoring data of each growth period of crops; by monitoring environmental data such as soil moisture content, soil nutrients, air temperature and humidity, rainfall, disease and pest morbidity and the like in real time, the irrigation, fertilization, pesticide application, water and fertilizer integration and water, fertilizer and pesticide integration irrigation scheme can be adjusted in real time; the remote control terminal, the environmental data acquisition unit and the water-fertilizer-pesticide integrated irrigation system are connected with the intelligent data analysis system through a mobile internet or a WAN (wide area network), so that the water-fertilizer-pesticide integrated irrigation system can be remotely and efficiently managed and controlled.
Drawings
Fig. 1 is a schematic structural diagram of a water-fertilizer-pesticide integrated intelligent irrigation system based on a machine learning algorithm.
Fig. 2 is a detailed structure diagram of a water-fertilizer-pesticide integrated intelligent irrigation system based on a machine learning algorithm.
In the figure 2, 1 is an environmental data acquisition unit, 2 is an intelligent data analysis system, 3 is a remote control terminal, 4 is a water and fertilizer integrated irrigation system, 5 is a field pipeline system, 6 is a communication module B, 7 is a soil moisture content monitor, 8 is a soil nutrient monitor, 9 is a meteorological monitoring station, 10 is a camera, 11 is a communication module A, 12 is a data storage module, 13 is a water demand database, 14 is a nutrient demand database, 15 is a pest control medicament library, 16 is a meteorological monitoring database, 17 is a soil monitoring database, 18 is a pest monitoring database, 19 is a data analysis module, 20 is an irrigation scheme, 21 is a fertilization scheme, 22 is a pesticide application scheme, 23 is a water and fertilizer integrated scheme, 24 is a water and fertilizer integrated scheme, 25 is a scheme output module, 26 is a communication module C, 27 is a control console, 28 is a water pool, 29 is a water pump, 26 is a water pump, 27 is a water and fertilizer integrated irrigation scheme, 30, an N fertilizer mother liquor tank, 31, a P fertilizer mother liquor tank, 32, a K fertilizer mother liquor tank, 33, a micro fertilizer mother liquor tank, 34, a medicament mother liquor tank, 35, a fertilizer applicator, 36, a filtering system, 37, an effect evaluation system, 38, an attribute value database, 39, an implementation scheme database, 40, an effect evaluation database, 41, an implementation effect deviation value, 42, an implementation effect value, 43, a verification data set, T1-T14, a valve system on a pipeline system, a solid line connected between 26-36 is a pipeline system, and a dotted line is a wire cable.
Fig. 3 is a schematic diagram of steps of a machine learning algorithm of a water-fertilizer-pesticide integrated intelligent irrigation system based on the machine learning algorithm.
Detailed Description
In order to clearly understand the technical features, functions and advantageous effects of the present invention, embodiments of the present invention will be described with reference to the accompanying drawings.
A water-fertilizer-pesticide integrated intelligent irrigation system based on a machine learning algorithm is characterized by comprising a remote control terminal, an intelligent data analysis system, an environmental data acquisition unit, a water-fertilizer-pesticide integrated control system and a field pipeline system; the remote control terminal, the environmental data acquisition unit and the water, fertilizer and pesticide integrated control system are respectively connected with the intelligent data analysis system through a mobile internet or a Wide Area Network (WAN); the remote control terminal is used for checking environmental data, a water-fertilizer-pesticide integrated irrigation scheme and crop field growth vigor and remotely regulating the water-fertilizer-pesticide integrated irrigation scheme; the environment data acquisition unit is used for acquiring environment parameters; the intelligent data analysis system is used for storing water demand data, nutrient demand data, pest control agent data, soil monitoring data, meteorological monitoring data and pest monitoring data, and outputting a water-fertilizer-pesticide integrated scheme; the water-fertilizer-pesticide integrated control system is used for executing irrigation, fertilizer application, pesticide application, water-fertilizer integration and water-fertilizer-pesticide integrated irrigation schemes.
The remote control terminal comprises a smart phone, a tablet computer, a notebook computer and a desktop computer. The smart phone and the tablet personal computer check environmental monitoring data and pest monitoring data, check a water-fertilizer-pesticide integrated scheme and remotely regulate and control through an APP management interface; the notebook computer and the desktop computer check environmental monitoring data and pest monitoring data through a PC end webpage, and check and remotely regulate and control a water-fertilizer-pesticide integrated scheme.
The environmental data acquisition unit comprises a soil moisture content monitor, a soil nutrient monitor, a meteorological monitoring station, a camera and a communication module which are distributed in the field. The soil moisture content monitor can monitor soil moisture, temperature, pH value and EC value; the soil nutrient monitor can monitor the nutrient contents of nitrogen (N), phosphorus (P) and potassium (K) in soil; the weather monitoring station can monitor air temperature, humidity, rainfall and evaporation capacity; the environment data acquisition unit transmits soil monitoring data and meteorological monitoring data to an intelligent data analysis system for storage, analysis and intelligent scheme output by using a communication module through a mobile internet (GPRS) or a Wide Area Network (WAN).
The water-fertilizer-pesticide integrated control system comprises a communication module, a control console, a water tank, a water pump, an N fertilizer mother liquor tank, a P fertilizer mother liquor tank, a K fertilizer mother liquor tank, a micro fertilizer mother liquor tank, a pesticide mother liquor tank, a fertilizer applicator, a filtering system, a wire cable, a valve system and a field pipeline system. The communication module comprises a GPRS wireless communication module and a WAN wide area network communication module; the control console comprises a display screen and an operation module, wherein the display screen is used for displaying parameters of the currently implemented or to-be-implemented water-fertilizer-pesticide integration scheme and real-time field images, and the operation module is used for modifying the parameters of the water-fertilizer-pesticide integration scheme and checking video recordings; the valve system comprises an electromagnetic valve, a timing assembly, a flow control assembly and a pressure regulator, and is positioned on a pipeline connected among the assemblies; the control console controls the valve system through a cable, the valve system controls the opening and closing time of the valve system through the timing assembly, the opening degree of the valve system is controlled through the flow control assembly, and the flowing time and flow of water, fertilizer liquid and liquid medicine are controlled through the opening and closing time and the opening degree of the valve system; the field pipeline system comprises a PVC pipeline, an electromagnetic valve, a pressure regulator and a drip irrigation belt (a sprinkler irrigation head); the water tank and the water pump, the water pump and the fertilizer mother liquor tank and the medicament mother liquor tank, the fertilizer mother liquor tank and the fertilizer applicator, the water pump and the filtering system, the filtering system and the fertilizer mother liquor tank, and the filtering system and the field pipeline are connected through PVC pipelines.
The intelligent data analysis system comprises a communication module, a data storage module, a data analysis module and a scheme output module. The communication module comprises a GPRS wireless communication module and a WAN wide area network communication module; the data storage module comprises a water demand database, a nutrient demand database, a pest control medicament database, a meteorological monitoring database, a soil monitoring database and a pest monitoring database; the water demand database, the nutrient demand database and the pest control agent database are input by technical personnel through an intelligent data analysis system background; the disease and pest monitoring data are input by plant protection personnel of a planting base through a disease and pest detecting and reporting interface of a remote control terminal; the data analysis module analyzes and gives out an irrigation scheme through crop moisture demand data, soil monitoring data and meteorological monitoring data, analyzes and gives out a fertilization scheme through crop nutrient demand data, soil monitoring data and meteorological monitoring data, gives out a pesticide application scheme through pest monitoring data, meteorological monitoring data and pest control medicament data, and gives out a water-fertilizer-integrated and water-fertilizer-medicament-integrated irrigation scheme according to the superposition of irrigation, fertilization and pesticide application in time; the scheme output module transmits the irrigation scheme, the fertilization scheme, the pesticide application scheme, the water-fertilizer-pesticide integrated scheme and the water-fertilizer-pesticide integrated irrigation scheme to the water-fertilizer-pesticide integrated control system through a mobile internet (GPRS) or a Wide Area Network (WAN) by utilizing the communication module.
The machine learning algorithm-based water-fertilizer-pesticide integrated intelligent irrigation system has the main technical advantages that: complete, continuously updated, scientific and accurate crop health data. Data acquisition, analysis and formulation of a scheme, implementation and effect evaluation are integrated, a full-automatic seamless process is established, and data intercommunication is realized. An optimization transformation mechanism is established for the method flow of each module, and a factor difference comprehensive process is established for the data acquisition module. The modules are causal and feed back mutually, and the data models in the modules are continuously optimized.
The implementation effect evaluation system is a networking system, so that each terminal user using the system can share implementation effect evaluation data, and two key indexes can be evaluated after each implementation: an implementation effect bias value and an implementation effect value. The two values provide training data for the intelligent data analysis system to establish a linear regression algorithm. And training a verification data set with excellent implementation effect and extremely small implementation effect deviation value.
The environment data acquisition unit quantifies and standardizes the acquired value of the irregular attribute data. The data acquisition unit comprises: soil moisture, temperature, pH, EC, soil nitrogen (N), phosphorus (P), potassium (K), trace element nutrient content, air temperature, humidity, rainfall, evaporation, climate change in future time, CO2Concentration, illumination intensity, bud size, leaf color, leaf size, fruit number, leaf number, tree vigor, growth period, insect pest situation detection data, disease and pest statistical data and weed damage monitoring data.
Claims (10)
1. A machine learning algorithm-based water-fertilizer-pesticide integrated intelligent irrigation system is characterized by comprising a remote control terminal, an intelligent data analysis system, an environmental data acquisition unit, a water-fertilizer-pesticide integrated control system and a field pipeline system; the remote control terminal, the environmental data acquisition unit and the water, fertilizer and pesticide integrated control system are respectively connected with the intelligent data analysis system through a mobile internet or a Wide Area Network (WAN); the remote control terminal is used for checking environmental data, a water-fertilizer-pesticide integrated irrigation scheme and crop field growth vigor and remotely regulating the water-fertilizer-pesticide integrated irrigation scheme; the environment data acquisition unit is used for acquiring environment parameters; the intelligent data analysis system is used for storing water demand data, nutrient demand data, pest control agent data, soil monitoring data, meteorological monitoring data and pest monitoring data, and outputting a water-fertilizer-pesticide integrated scheme; the water-fertilizer-pesticide integrated control system is used for executing irrigation, fertilizer application, pesticide application, water-fertilizer integration and water-fertilizer-pesticide integrated irrigation schemes.
2. The remote control terminal comprises a smart phone, a tablet computer, a notebook computer and a desktop computer. The smart phone and the tablet personal computer check environmental monitoring data and pest monitoring data, check a water-fertilizer-pesticide integrated scheme and remotely regulate and control through an APP management interface; the notebook computer and the desktop computer check environmental monitoring data and pest monitoring data through a PC end webpage, and check and remotely regulate and control a water-fertilizer-pesticide integrated scheme.
3. The environmental data acquisition unit comprises a soil moisture content monitor, a soil nutrient monitor, a meteorological monitoring station, a camera and a communication module which are distributed in the field. The soil moisture content monitor can monitor soil moisture, temperature, pH value and EC value; the soil nutrient monitor can monitor the nutrient contents of nitrogen (N), phosphorus (P) and potassium (K) in soil; the weather monitoring station can monitor air temperature, humidity, rainfall and evaporation capacity; the environment data acquisition unit transmits soil monitoring data and meteorological monitoring data to an intelligent data analysis system for storage, analysis and intelligent scheme output by using a communication module through a mobile internet (GPRS) or a Wide Area Network (WAN).
4. The water-fertilizer-pesticide integrated control system comprises a communication module, a control console, a water tank, a water pump, an N fertilizer mother liquor tank, a P fertilizer mother liquor tank, a K fertilizer mother liquor tank, a micro fertilizer mother liquor tank, a pesticide mother liquor tank, a fertilizer applicator, a filtering system, a wire cable, a valve system and a field pipeline system. The communication module comprises a GPRS wireless communication module and a WAN wide area network communication module; the control console comprises a display screen and an operation module, wherein the display screen is used for displaying parameters of the currently implemented or to-be-implemented water-fertilizer-pesticide integration scheme and real-time field images, and the operation module is used for modifying the parameters of the water-fertilizer-pesticide integration scheme and checking video recordings; the valve system comprises an electromagnetic valve, a timing assembly, a flow control assembly and a pressure regulator, and is positioned on a pipeline connected among the assemblies; the control console controls the valve system through a cable, the valve system controls the opening and closing time of the valve system through the timing assembly, the opening degree of the valve system is controlled through the flow control assembly, and the flowing time and flow of water, fertilizer liquid and liquid medicine are controlled through the opening and closing time and the opening degree of the valve system; the field pipeline system comprises a PVC pipeline, an electromagnetic valve, a pressure regulator and a drip irrigation belt (a sprinkler irrigation head); the water tank and the water pump, the water pump and the fertilizer mother liquor tank and the medicament mother liquor tank, the fertilizer mother liquor tank and the fertilizer applicator, the water pump and the filtering system, the filtering system and the fertilizer mother liquor tank, and the filtering system and the field pipeline are connected through PVC pipelines.
5. The intelligent data analysis system comprises a communication module, a data storage module, a data analysis module and a scheme output module. The communication module comprises a GPRS wireless communication module and a WAN wide area network communication module; the data storage module comprises a water demand database, a nutrient demand database, a pest control medicament database, a meteorological monitoring database, a soil monitoring database and a pest monitoring database; the water demand database, the nutrient demand database and the pest control agent database are input by technical personnel through an intelligent data analysis system background; the disease and pest monitoring data are input by plant protection personnel of a planting base through a disease and pest detecting and reporting interface of a remote control terminal; the data analysis module analyzes and gives out an irrigation scheme through crop moisture demand data, soil monitoring data and meteorological monitoring data, analyzes and gives out a fertilization scheme through crop nutrient demand data, soil monitoring data and meteorological monitoring data, gives out a pesticide application scheme through pest monitoring data, meteorological monitoring data and pest control medicament data, and gives out a water-fertilizer-integrated and water-fertilizer-medicament-integrated irrigation scheme according to the superposition of irrigation, fertilization and pesticide application in time; the scheme output module transmits the irrigation scheme, the fertilization scheme, the pesticide application scheme, the water-fertilizer-pesticide integrated scheme and the water-fertilizer-pesticide integrated irrigation scheme to the water-fertilizer-pesticide integrated control system through a mobile internet (GPRS) or a Wide Area Network (WAN) by utilizing the communication module.
6. The machine learning algorithm-based water-fertilizer-pesticide integrated intelligent irrigation system has the main technical advantages that: complete, continuously updated, scientific and accurate crop health data. Data acquisition, analysis and formulation of a scheme, implementation and effect evaluation are integrated, a full-automatic seamless process is established, and data intercommunication is realized. An optimization transformation mechanism is established for the method flow of each module, and a factor difference comprehensive process is established for the data acquisition module. The modules are causal and feed back mutually, and the data models in the modules are continuously optimized.
7. The implementation effect evaluation system is a networking system, so that each terminal user using the system can share implementation effect evaluation data, and two key indexes can be evaluated after each implementation: an implementation effect bias value and an implementation effect value. The two values provide training data for the intelligent data analysis system to establish a linear regression algorithm. And training a verification data set with excellent implementation effect and extremely small implementation effect deviation value.
8. The environment data acquisition unit quantifies and standardizes the acquired value of the irregular attribute data. The data acquisition unit comprises: soil moisture, temperature, pH value, EC value, soil nitrogen (N), phosphorus (P), potassium (K), trace element nutrient content, air temperature, humidity, rainfall, evaporation capacity, climate change in future time, CO2 concentration, illumination intensity, bud body size, leaf color, leaf size, fruit number, leaf number, tree vigor, growth period, insect pest detection data, pest statistical data and weed damage monitoring data.
9. The intelligent data analysis system utilizes a linear regression algorithm to establish a data model among environment attribute data, implementation data values and evaluation effect values, prunes accurate implementation data, predicts and broadens implementation data distribution, establishes a data matrix X based on an attribute value database, an implementation database and an effect evaluation database, obtains a most appropriate vector w according to the fact that an expected value vector (implementation effect value) y is known, and enables a linear equation set to meet linear distribution of sample points as far as possible, and then predicts and generates water and fertilizer medicine implementation scheme data which tend to be accurate by utilizing the obtained w.
10. The intelligent data analysis system comprises the following specific steps:
a. generating sample data based on the environment attribute data, the implementation scheme data and the effect evaluation data, and performing data standardization preprocessing;
b. calling a linear regression algorithm to minimize the error between the y value and the predicted value, and training to obtain a linear regression model;
c. predicting implementation scheme data by using a linear regression model, and storing the implementation scheme data in an implementation scheme database;
d. executing the implementation scheme to obtain an implementation effect and storing the implementation effect in an effect evaluation database;
e. and triggering a linear regression algorithm, and training a correlation model of the environment attribute, the crop attribute, the water and fertilizer pesticide application data and the application effect again to improve the abundance and the accuracy of the data.
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