CN114219673A - Agricultural cloud service system based on Internet of things - Google Patents
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Abstract
The invention belongs to the technical field of agricultural production, and particularly relates to an agricultural cloud service system and method based on the Internet of things; the invention provides an agricultural cloud service system based on the Internet of things, which comprises: the system comprises a cloud terminal, an information transmission network and a cloud computing unit based on the Internet of things; the cloud terminal based on the Internet of things performs information interaction with the cloud computing unit through the information transmission network; the cloud terminal based on the Internet of things comprises: the planting service perception terminal is used for perceiving the growth of farmland crops or collecting parameter information of the crops and surrounding environment; and the user terminal is used for receiving and displaying data to realize the supervision function. The invention also includes a precise control of the water delivery for irrigation. The artificial intelligent deep learning neural network is adopted to train the water delivery model so as to meet the requirements of accurate and real-time irrigation and water delivery.
Description
Technical Field
The invention belongs to the technical field of agriculture, and particularly relates to a cloud service system based on the Internet of things.
Background
According to relevant statistics, the accumulation of the current agricultural knowledge and data resources in China reaches the TB magnitude, but the information resources are dispersed in different agricultural infrastructure services or research organizations and have the characteristics of complexity, isomerism, distribution and the like; meanwhile, modern factory facilities are based on agriculture, environmentally safe agriculture and non-toxic agriculture are still in a sprouting state, the construction cost is high, the investment is large, and large-scale application is not available. Therefore, the information technology has become the focus of agricultural research on how to break the information barrier in the agricultural field and develop information technology business in various industries in cooperation with agricultural production.
The Internet of things cloud service system realizes the butt joint of the Internet of things technology and the cloud computing technology in the field of agricultural information services, well solves the problems of the existing agricultural information services, realizes the automatic, fine and intelligent management of agricultural production, reduces the use threshold of the agricultural information services and improves the efficiency of the agricultural information services.
It is always a difficult point in the art how to precisely control the amount of irrigation water delivery through the collected parameters. In the prior art, regarding irrigation water delivery, generally, collected parameters are used to find a corresponding value range of irrigation water delivery in a database, the value range of water delivery corresponding to growth related parameters of each crop (including root humidity, leaf humidity, root tensile stress, real-time illuminance, ambient temperature, soil humidity and the like) is estimated, and an average value is taken to meet the requirements of various environments. However, this approach does not provide for refinement of the irrigation of the crop. The value is far from the actual demand, and the water delivery quantity is usually larger or smaller than the actual demand, which greatly influences the growth of crops. In addition, the above calculated water delivery amount does not take into account real-time. For crops, especially plants of different families, which are grown in different time periods, there is a large difference in water consumption. Even during three fixed irrigation times of the day: the required water delivery rates were different at 8:00-9:00 am, 13:00-14:00 am and 18:00-19:00 pm. These all require fine water delivery control. Based on the problems in the prior art, the invention adopts a water delivery amount planning method based on a deep neural network and establishes a training model through the deep learning neural network. The input quantity of the model is each parameter actually measured by each sensor, and the parameters at least comprise root temperature, blade temperature, root tensile stress, real-time illuminance and soil humidity. The output is: the water delivery amount W. Through the model, parameters output based on parameter pairs can be effectively: the water delivery quantity for crop irrigation is accurately and timely calculated.
Disclosure of Invention
In view of the problems in the prior art, one of the objects of the present invention is to provide an agricultural cloud service system based on the internet of things. In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides an agricultural cloud service system based on the Internet of things, which is characterized in that: the system comprises: the system comprises a cloud terminal, an information transmission network and a cloud computing unit based on the Internet of things; the cloud terminal based on the Internet of things performs information interaction with the cloud computing unit through the information transmission network; the cloud terminal based on the Internet of things comprises:
the planting service perception terminal is used for perceiving the growth of farmland crops or collecting parameter information of the crops and surrounding environment;
and the user terminal is used for receiving and displaying data to realize a supervision function, and the supervision function adopts an AI model to calculate the output control quantity.
Preferably, the planting service perception terminal comprises a communication module, a power module, a plurality of planting state sensing sensors, a microcontroller, a display module, a plurality of relays and a water pump motor.
The invention also provides a method for irrigating and delivering water for crop growth, which is characterized by comprising the following steps:
step S1: detecting each parameter of crop growth in real time by using a planting service perception terminal;
step S2: uploading the parameters to a cloud computing unit through an information transmission network, and inputting the parameters serving as input quantities into a trained AI model;
step S3: calculating the required irrigation water delivery amount in real time through the trained AI model;
step S4: and the cloud computing unit controls the switch of the water pump motor according to the computed irrigation water delivery quantity.
Preferably, the parameters include root humidity, blade humidity, root tensile stress, real-time illuminance and soil humidity.
In another aspect of the present invention, a neural network model training method is provided, where the model is used in the above crop growth irrigation water delivery method, and the AI model is obtained by training using a neural network model.
Preferably, the training method is used for agricultural cloud services.
In another aspect of the present invention, a method for providing agricultural cloud services by using any one of the above systems is provided, wherein the method comprises: the method comprises the following steps:
step 1: the planting service perception terminal acquires the growth state of the plant and the parameter information of the surrounding environment, and transmits the information to the cloud computing unit through the information transmission network;
step 2: the cloud computing unit collects and processes information and forwards data to the user terminal as required;
and step 3: the user terminal receives the data of the cloud computing unit and sends a service request to the cloud computing unit according to the service requirement;
and 4, step 4: the cloud computing unit receives a service request of the user terminal, calls a corresponding module to execute processing by utilizing AI screening and feeds back a processing result to the user terminal;
and 5: and the user terminal receives and displays the feedback result.
In another aspect of the present invention, a neural network model training method is provided, where the target function used for the neural network model training is:
the objective function Cost in the formula (1) represents the growth degree of crops, and preferably the root growth length; h1, h2, T1 mean the real-time root moisture, blade moisture h2 and root tensile stress T1 measured by the sensors, respectively; the above measurement value may be an accumulated value of a period of time Δ t; alpha is alpha1、α2And alpha3Coefficients of respective parameters h1, h2, T1; y isgMeans the expected length of crop growth over a period of time; y is0When t is 0, the length of the root of the crop in the initial state; and for the objective function Cost, an iterative calculation mode is adopted, and a condition which is required to be met when Cost is Cost (min) is obtained.
Compared with the prior art, the invention at least has the following beneficial effects:
(1) the invention combines cloud computing and agriculture, better solves the problem of a large amount of public agricultural information services existing at present, realizes the access of a large-scale agricultural sensor terminal and massive data integration services, effectively reduces the use threshold of agricultural information services, and improves the efficiency of the agricultural information services;
(2) the invention automatically connects the physical sensing device and the agricultural irrigation control mechanism to the cloud, properly monitors different agricultural parameter data by using AI screening, and adjusts irrigation according to the detection result, thereby maintaining the capability of analyzing the whole system structure and solving problems.
(3) The irrigation water delivery quantity of crops calculated by the existing irrigation water delivery quantity is rough, cannot be accurately controlled, and cannot reflect the influence brought by the change of the surrounding environment. In addition, the calculation mode adopts empirical formulas such as table look-up and the like for calculation, and the function of accurate control of different crops cannot be met. The model used by the invention utilizes a deep neural network learning method in artificial intelligence, takes the most economy (the fastest crop growth mode) as an objective function in the iterative learning process, and adds the surrounding environment parameters as control boundary parameters, so that the output result can be more suitable for the real-time change requirement.
Drawings
FIG. 1 is a block diagram of an agricultural cloud service system based on the Internet of things of the present invention;
fig. 2 is an AI-based irrigation water delivery flow diagram of the present invention.
The present invention is described in further detail below. The following examples are merely illustrative of the present invention and do not represent or limit the scope of the claims, which are defined by the claims.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
Example 1
The technical scheme of the invention is further explained by the specific implementation mode in combination with the attached drawings.
To better illustrate the invention and to facilitate the understanding of the technical solutions thereof, typical but non-limiting examples of the invention are as follows:
fig. 1 is a block diagram of an agricultural service system based on the internet of things.
The system comprises a cloud terminal based on the Internet of things, an information transmission network and a cloud computing unit.
The cloud terminal based on the Internet of things, namely the cloud terminal, comprises at least one planting service sensing terminal, a tracing terminal and a user terminal.
The planting service sensing terminal is arranged in a farmland crop growth environment and used for sensing crop growth or collecting crop and surrounding environment parameter information, and the planting service sensing terminal performs data exchange with the cloud computing unit through the wireless sensor network.
And the user terminal is placed in the surrounding environment used by the user and used for receiving and displaying data to complete a proper supervision function, and the user terminal exchanges data with the cloud computing unit through the Internet.
The information transmission network can comprise one or more field buses, a wireless sensor network, a mobile communication network, a public telephone network, a public switched data network, a wireless network, satellite communication, networking and the Internet, and is used for constructing a data transmission channel between the terminal and a cloud computing unit cloud, and the cloud terminal receives requests and responds to data transmission of the cloud computing unit.
And the cloud computing unit comprises a cloud receiving unit, a cloud computing center and a cloud storage. The cloud computing center connects the cloud storage through one or more clouds using switches and/or routers.
The cloud computing center comprises at least two servers, performs distributed processing of data, and provides various cloud service capabilities of the cloud terminal.
The cloud storage is used for providing various types of data storage for cloud computing and is composed of hardware and software.
The cloud computing center comprises a cloud management unit, a cloud application unit and the like. The cloud computing center may also include a data persistent storage component responsible for storing data completed by hardware and software. And the cloud computing center receives the data service request and encapsulates the data of the standard interface in response to the request.
And the cloud management unit is responsible for completing task scheduling management, and can manage the cloud application unit and the interface unit through a set of hardware and software equipment. And receiving service requests of the cloud application unit and the interface unit, and calling data stored in the cloud storage for analysis and processing.
The cloud application unit is used for processing all types of application services of the cloud terminal, receives service requests and controls the cloud management unit through the interface unit by a set of hardware and software equipment components, and feeds back processing results of various application service requests to the interface unit for the cloud terminal to call the system.
Planting service perception terminal includes communication module, power module, a plurality of planting state sensing sensor, microcontroller, display module, a plurality of relay and water pump motor.
And the communication module is responsible for establishing a data channel between the terminal and the cloud computing unit, receiving instruction information, transmitting the instruction information to the cloud computing unit, and sending a subsequent processing instruction to the microcontroller. The communication module comprises one or more wireless communication modules, Ethernet communication modules and mobile communication modules.
The microcontroller is responsible for all service processes of the terminal, belongs to a core unit of the terminal, is responsible for controlling other modules of the whole terminal, controls data acquisition of each sensor and requests the communication module to transmit data to the cloud computing unit. The microcontroller is an integrated chip consisting of a RAM, a ROM, a timer, a counter, an ADC (analog-to-digital converter), a DAC (digital-to-analog converter) and the like. In particular, the microcontroller may be an Adriano microcontroller, alerting the farmer if the sensed data is below or above a predetermined value.
A plurality of planting state sensing sensors are responsible for acquiring the overall condition of crops and the environmental parameters of field information equipment, and comprise a temperature sensor, a humidity sensor, a tensile stress sensor, a light intensity sensor and the like, and specifically, the environmental parameters comprise the moisture, the humidity, the temperature and the weather conditions of the crops and the soil. A plurality of planting state sensing sensors receive microcontroller control, submit the sensor data of gathering for microcontroller and send to the cloud computing element and adopt AI screening, adjust the irrigation according to observing the plant.
And the power supply module is responsible for providing energy for each part of the terminal and comprises a communication module, a microcontroller and a plurality of planting state sensing sensors. Including batteries and dry cells for supplying power in the form of direct current.
And the display module is responsible for displaying the provided voltage reading.
And the relays are responsible for switching of the circuit.
And the water pump motor is in charge of regulating the flow of water.
And the communication module is responsible for establishing a data channel between the terminal and the cloud computing unit, receiving instruction information, pushing the cloud computing unit and sending a subsequent processing instruction to the microcontroller. The communication module comprises one or more wireless communication modules, Ethernet communication modules and mobile communication modules.
The microcontroller is responsible for all service processes of the terminal, belongs to a core unit of the terminal, is responsible for controlling other modules of the whole terminal, controls data acquisition of each sensor, and requests the communication module to transmit data to the cloud computing unit, and comprises an internal computing unit, a memory, a controller and the like.
And the power supply module is responsible for providing energy for all parts of the terminal and comprises a communication module, a microcontroller and a plurality of state sensing sensors.
The tracing terminal comprises a communication module, a microcontroller, a power module, an identification module and a display module.
And the communication module is responsible for establishing a data channel between the terminal and the cloud computing unit, receiving instruction information, pushing the cloud computing unit and sending a subsequent processing instruction to the microcontroller. The communication module comprises one or more wireless communication modules, Ethernet communication modules and mobile communication modules.
The microcontroller is responsible for all service processes of the terminal, belongs to a core unit of the terminal, is responsible for controlling other modules of the whole terminal, controls the identification module to collect or identify field data, requests the communication module to transmit the data to the cloud computing unit, receives a cloud service request sent to the communication module, and comprises an internal computing unit, a memory, a controller and the like.
And the identification module is responsible for identifying various types of label information and sending real-time data of the identification information to the microcontroller for subsequent processing, and the identification types comprise identification bar codes, two-dimensional code identification, voice identification and image identification modules.
And the display module provides a data display function and is controlled by the microcontroller.
And the power supply module is responsible for providing energy for each part of the terminal and comprises a communication module, a microcontroller, an identification module and a display module.
The user terminal comprises a communication module, a CPU, a power supply module, a control module and a display module. The user terminal can be a PC, a mobile phone, an intelligent terminal, a telephone and a television.
And the communication module is responsible for establishing a data channel between the user terminal and the cloud computing unit, receiving instruction information, pushing the cloud computing unit, and sending the instruction to the CPU for further processing. The communication module comprises one or more wireless communication modules, Ethernet communication modules and mobile communication modules.
The CPU is responsible for processing various user services of the user terminal, belongs to a core unit of the terminal and is responsible for overall control of other modules of the terminal.
And the control module is responsible for various control input commands, completes a service control function and is controlled by the CPU. The control module comprises a keyboard, a mouse, a keyboard simulation module, a simulation mouse and a touch screen.
And the display module provides display data and is controlled by the CPU module.
The power module is responsible for providing energy for each part of the terminal and comprises a communication module, a CPU, a control module and a display module.
The method for agricultural service by using the system comprises the following steps:
step 1: the planting service perception terminal acquires the growth state of plants and parameter information of surrounding environment, the traceability terminal acquires safety traceability information of agricultural products, and the information is transmitted to the cloud computing unit through the information transmission network;
step 2: the cloud computing unit collects and processes information and forwards data to the user terminal as required;
and step 3: the user terminal receives the data of the cloud computing unit and sends a service request to the cloud computing unit according to the service requirement;
and 4, step 4: the cloud computing unit receives a service request of the user terminal, calls a corresponding module to execute processing by utilizing AI screening and feeds back a processing result to the user terminal;
and 5: and the user terminal receives and displays the feedback result.
Example 2
Embodiment 2 is a further description of embodiment 1, and the content including embodiment 1 is not described herein again.
For invoking the beneficiary module to perform processing by utilizing AI screening, an important aspect is the manner in which the amount of irrigation water delivery is controlled by the AI computation of the cloud computing unit. The real-time water delivery quantity obtained by utilizing AI calculation can be used for irrigating and watering crops by controlling the water pump.
To apply accurate irrigation watering, it is necessary to train the model needed for the calculation to reach the requirements, this model training being aided by AI techniques, preferably model training by what is known as neural networks. More preferably, a deep neural network learning model is used. And a good deep neural network learning model is needed to determine whether irrigation can be accurately controlled. So that the output control parameters can meet the aim of accurate control after training is finished.
The planting state sensing sensors are responsible for acquiring the overall conditions of crops and the environmental parameters of field information equipment, and comprise temperature sensors, humidity sensors, tensile stress sensors, illuminance sensors and the like, and specifically, the environmental parameters comprise the moisture, humidity, temperature and weather conditions of the crops and soil. The plurality of planting state sensing sensors receive the control of the microcontroller, submit the acquired sensor data to the microcontroller and send the sensor data to the cloud computing unit for AI screening, and irrigation is adjusted according to observed plants.
Specifically, in the embodiment, for convenience of model establishment, only a few crop self parameters and environmental parameters which affect the crop growth greatly are selected, so that the model is simplified, and meanwhile, the burden of cloud computing in the later period can be reduced. The parameters at least comprise a root humidity sensor which is used for detecting the water absorption of the roots of the crops in real time. The blade humidity sensor is used for roughly representing the transpiration phenomenon of the crops, when the blade humidity is larger than a threshold value, the transpiration phenomenon is aggravated, and when the blade humidity is smaller than the threshold value, the transpiration phenomenon is reduced. And the root tensile stress sensor is used for representing the real-time growth condition of the crop roots. Besides the state parameters characterizing the crops, a series of parameters characterizing the surrounding environment of the crops are also provided; these parameters include a light level sensor, a soil moisture sensor, a soil moisture sensor, a sensor
Humidity. See table 1 for details.
TABLE 1 planting status sensing sensor schematic table
And the microcontroller is used for communicating and uploading the sensor data containing the real-time measurement parameter values to a cloud service center, is responsible for all business processes, belongs to a core unit, controls data acquisition of each sensor and requests a communication module to transmit data to the cloud service center. The microcontroller is an integrated chip consisting of a RAM, a ROM, a timer, a counter, an ADC (analog-to-digital converter), a DAC (digital-to-analog converter) and the like. In particular, the microcontroller may be an Adriano microcontroller.
The cloud service center at least comprises a cloud receiving unit for receiving the parameters and the cloud computing unit. And the cloud computing unit receives the data, performs computing analysis through the model, and at least outputs an output value including the irrigation water delivery quantity so as to control the real-time irrigation water delivery quantity of the crops.
The specific model training calculation process is as follows:
a deep neural network model can be designed, and the objective function of the model is set as:
the meaning of the objective function Cost in equation (1) is to proceed in the most economical way (e.g. fastest way of growing crops)Water is delivered, and meanwhile, the prediction target of crop growth needs to be met; h1, h2, T1 mean the real-time root moisture, blade moisture h2 and root tensile stress T1 measured by the sensors, respectively. The h1, h2, T1 vary with time T, and the measured value may be an accumulated value over a period of time Δ T; alpha is alpha1、α2And alpha3Respectively, the coefficients of the respective parameters; y isgRefers to the expected value of crop growth over a period of time. Generally, crop growth values are described by a number of parameters, only the length of the most predominant crop growth being taken as a measure, i.e. ygRepresenting the root length of the intended crop. y is0When t is 0 (initial), i.e., the length of the root of the crop in the initial state.
For the prediction model, an iterative calculation method that is common in the art may be adopted to calculate a planning objective function Cost, that is, a condition that should be satisfied when Cost is determined to be Cost (min). The specific iterative calculation mode is as follows:
in the formula (2)
Wherein t isW+Refers to the period of forward opening of the valve using the irrigation system; t is tW-Is and tW+The matched valve is closed for a time period after being opened in the positive direction; v is the flow rate of the irrigation water pipe when the water flow is constant. Alpha is alpha4、α5Are respectively the respective proportionality coefficients; Δ t is the measurement period.
In addition, the measured value h of the parameter therein1、h2、T1Certain conditions should be satisfied. The conditions were as follows:
Hmin<h1<Hmax； (3)
wherein HminAnd HmaxThe measured minimum value of soil humidity and the maximum value of soil temperature are respectively indicated;
and
is the minimum and maximum values that the measured blade moisture measurements satisfy, and β is the fitting parameter. When measured h1And h2If the interval is not satisfied, the model is not appropriate, and the model calculation is terminated; and then the water delivery quantity is calculated by adopting a conventional water delivery quantity calculation mode. The root tensile stress T1 is related to the water transport amount W, the nutrient solution ratio, and the like, but the relationship between T1 and the water transport amount W may be considered in the model without limitation for simplification and reduction of the post-calculation pressure. The tensile stress T1 is now proportional to vt, where δ is the fitting coefficient of equation (5). The above-mentioned measurement parameter h1,h2T1 varies with the amount of water delivered, and is therefore controlled by the amount of water (here mainly velocity v and opening/closing time T)W+、tW-) The parameter h can be adjusted1,h2And T1. Continuously performing convergence operation within the range of the environmental parameters of the above formulas (3) to (5) to satisfy the requirement of y within the minimum timegThe requirement of a maximum value. Preferably, the training does not need actual irrigation experiments, and the simulation operation can be realized by a simulation simulator which is conventional in the field.
Iterative training is carried out by using the model, so that the water delivery quantity which is input as the root humidity, the blade humidity, the root tensile stress, the real-time illuminance, the environment temperature and the soil humidity is obtained, and the water delivery quantity which is output under the most economic water delivery condition is obtained. The control of the water delivery amount can be performed by the water pump motor in embodiment 1. The water pump motor can be an openClosed, rather than stepless, regulation to meet tW-Is and tW+The requirements of (1). In addition, the model is directed to the complexity of models for crop varieties of the same family, preferably the same plant variety, due to simple different crops. This is mainly aimed at
In the case of crops of different families,
wherein the value range of gamma is 0.2<γ<Varying between 0.75. Although the above iterative method is applied in other fields, it is a challenge in the field of crop planting, mainly for the selection of parameters in the iterative training process and the consideration of the surrounding environment factors to the iteration quantity XnAnd (4) controlling.
Preferably, the training model is a training model for a period of time. I.e. preferably for the same period of time, which is preferably 10 days, due to the climate consistency. And terminating the training of the model if factors such as bad weather are met. Different time periods require different training models but the same for the training mode. These training are preferably performed in the cloud computing unit.
The embodiment also comprises a method for delivering irrigation water for crop growth, which is characterized by comprising the following steps:
step S1: detecting each parameter of crop growth in real time by using a planting service perception terminal;
step S2: uploading the parameters to a cloud computing unit through a planting service perception terminal through an information transmission network, and inputting the parameters as input quantities into a trained AI model;
step S3: calculating the required irrigation water delivery amount in real time through the trained AI model;
step S4: and the cloud computing unit controls the switch of the water pump motor according to the computed irrigation water delivery quantity. The method may be used for the cloud service described in the embodiments.
Each part of the invention is an independent technical scheme, for example, the cloud service based on the network is operated independently. But also operates independently for irrigation water delivery. However, these technical solutions may also be combined, for example, the agricultural cloud service based on the internet of things in embodiment 1 may be combined with the calculation of the irrigation water delivery amount, and specifically, the AI module in the cloud computing unit may be trained by using the deep neural network training model in embodiment 2.
The applicant declares that the present invention illustrates the detailed structural features of the present invention through the above embodiments, but the present invention is not limited to the above detailed structural features, that is, it does not mean that the present invention must be implemented depending on the above detailed structural features. It should be understood by those skilled in the art that any modifications of the present invention, equivalent substitutions of selected components of the present invention, additions of auxiliary components, selection of specific modes, etc., are within the scope and disclosure of the present invention.
The preferred embodiments of the present invention have been described in detail, however, the present invention is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present invention within the technical idea of the present invention, and these simple modifications are within the protective scope of the present invention.
It should be noted that the various technical features described in the above embodiments can be combined in any suitable manner without contradiction, and the invention is not described in any way for the possible combinations in order to avoid unnecessary repetition.
In addition, any combination of the various embodiments of the present invention is also possible, and the same should be considered as the disclosure of the present invention as long as it does not depart from the spirit of the present invention.
Claims (7)
1. The utility model provides an agricultural cloud service system based on thing networking which characterized in that: the system comprises: the system comprises a cloud terminal, an information transmission network and a cloud computing unit based on the Internet of things; the cloud terminal based on the Internet of things performs information interaction with the cloud computing unit through the information transmission network; the cloud terminal based on the Internet of things comprises:
the planting service perception terminal is used for perceiving the growth of farmland crops or collecting parameter information of the crops and surrounding environment;
and the user terminal is used for receiving and displaying data to realize a supervision function, and the supervision function adopts an AI model to calculate the output control quantity.
2. The system of claim 1, wherein: the planting service perception terminal comprises a communication module, a power module, a plurality of planting state sensing sensors, a microcontroller, a display module, a plurality of relays and a water pump motor.
3. A method of delivering irrigation water for crop growth, the method comprising:
step S1: detecting each parameter of crop growth in real time by using a planting service perception terminal;
step S2: uploading the parameters to a cloud computing unit through an information transmission network, wherein the parameters are used as input quantities and input into a trained AI model;
step S3: calculating the required irrigation water delivery amount in real time through the trained AI model;
step S4: and the cloud computing unit controls the switch of the water pump motor according to the computed irrigation water delivery quantity.
4. A neural network model training method, which is used for the crop growth irrigation water delivery method according to claim 3, characterized in that the AI model is obtained by training a neural network model.
5. The training method of claim 4 is used for agricultural cloud services.
6. A method for agricultural cloud service using the system of any one of claims 1-3, characterized by: the method comprises the following steps:
step 1: the planting service perception terminal acquires the growth state of the plant and the parameter information of the surrounding environment, and transmits the information to the cloud computing unit through the information transmission network;
step 2: the cloud computing unit collects and processes information and forwards data to the user terminal as required;
and step 3: the user terminal receives the data of the cloud computing unit and sends a service request to the cloud computing unit according to the service requirement;
and 4, step 4: the cloud computing unit receives a service request of the user terminal, calls a corresponding module to execute processing by utilizing AI screening and feeds back a processing result to the user terminal;
and 5: and the user terminal receives and displays the feedback result.
7. A neural network model training method, wherein an objective function adopted by the neural network model training is as follows:
the objective function Cost in the formula (1) represents the growth degree of crops, and preferably the root growth length; h1, h2, T1 mean the real-time root moisture, blade moisture h2 and root tensile stress T1 measured by the sensors, respectively; the above measurement value may be an accumulated value of a period of time Δ t; alpha is alpha1、α2And alpha3Coefficients of respective parameters h1, h2, T1; y isgMeans the expected length of crop growth over a period of time; y is0When t is 0, the length of the root of the crop in the initial state; and for the objective function Cost, an iterative calculation mode is adopted, and a condition which is required to be met when Cost is Cost (min) is obtained.
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