CN106444378A - Plant culture method and system based on IoT (Internet of things) big data analysis - Google Patents
Plant culture method and system based on IoT (Internet of things) big data analysis Download PDFInfo
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Abstract
The invention provides a plant culture method and system based on IoT (Internet of things) big data analysis, wherein the method comprises the steps of collecting plant species, soil humidity, a soil pH value, illumination intensity, environment temperature, environment humidity, images, watering quantity, fertilizing amount and fertilizing types; forming influence factor matrices X; uploading the influence factor matrices X to a server, wherein the watering quantity, the fertilizing amount and the fertilizing types form decision variables; building a complicated non-linear relationship between plant health indexes and all influence factor matrices X of plants in the server by an Elman neural network, and obtaining a plant culture model; optimizing the plant culture model by an NSGA-II algorithm, and obtaining a group of optimal solutions of the decision variables; using the group of optimal solutions of the decision variables as a plant recommendation decision X*, and issuing the recommendation decision to terminal equipment of a user through the server for displaying; culturing the plants by the user according to the recommendation decision displayed by the terminal equipment. By using the plant culture method and system provided by the invention, an optimal plant culture scheme can be determined; better living environment is created for the plants.
Description
Technical field
The present invention relates to plant intelligent cultivation field, and in particular to a kind of plant cultivation based on the analysis of Internet of Things big data
Method and system.
Background technology
With the fast development of national economy, potted plant enters thousand as a kind of mode for increasing residence comfort
Ten thousand families.But as most plants owner lacks planting plants experience, plant long term growth is made in the environment of subhealth state.The opposing party
Face, as the interior space is limited, according to own situation, plant owner can require that plant has different dense degree, it is to avoid space wave
Take.
At present, the problem of urgent need to resolve is to set up a set of comprehensive plant cultivation model, and plant health index is fed back
To user, user can adjust plant cultivation scheme in time.Between each factor of impact plant health degree often
The complexity of height and non-linear is embodied, there is certain difficulty using conventional prediction, analysis method.
Content of the invention
The present invention is by providing a kind of plant cultivating method based on the analysis of Internet of Things big data and system, existing to solve
Because suitable growing environment cannot be provided for plant during plant cultivation in technology, and plant growing situation is caused to deviate expection
The problem of index.
For solving the above problems, the present invention is employed the following technical solutions and is achieved：
On the one hand, the plant cultivating method based on the analysis of Internet of Things big data that the present invention is provided, including：
Step S1：The species, soil moisture of herborization, soil pH value, intensity of illumination, ambient temperature, ambient humidity, figure
Picture, irrigation amount, dose, Fertilizer Type simultaneously constitute influence factor matrix X, and upload onto the server；Wherein, irrigation amount, fertilising
Amount and Fertilizer Type constitute decision variable；
Step S2：Referred to plant health using each influence factor's matrix X of Elman neural network plant in server
Complex nonlinear relation between number, obtains plant cultivation model；
Step S3：Using II algorithm of NSGA-, plant cultivation model is optimized, obtain decision variable one group is optimum
Solution；
Step S4：Using the group optimal solution of decision variable as plant recommendation decision-making X*User is issued to by server
Terminal unit shown；
Step S5：User cultivates plant according to the recommendation decision-making that terminal unit shows.
On the other hand, the plant cultivation system based on the analysis of Internet of Things big data that the present invention is provided, including：
Data acquisition unit, for the species of herborization, growth period, soil moisture, soil pH value, intensity of illumination,
Ambient temperature, ambient humidity, image, irrigation amount, dose, Fertilizer Type simultaneously constitute influence factor matrix X, and be uploaded to service
Device；Wherein, irrigation amount, dose and the Fertilizer Type constitute decision variable；
Unit set up by plant cultivation model, in server using Elman neural network plant respectively affect because
Complex nonlinear relation between prime matrix X and plant health index, obtains plant cultivation model；
Decision variable optimal solution acquiring unit, for being optimized to plant cultivation model using II algorithm of NSGA-, is obtained
One group of optimal solution of decision variable, and using the group optimal solution of decision variable as plant recommendation decision-making X*；
Recommend decision-making issuance unit, for passing through server by the recommendation decision-making X of plant*It is issued to the terminal unit of user
Shown.
Compared with prior art, the present invention is provided the plant cultivating method based on the analysis of Internet of Things big data and system
Advantage is：Using Elman neural network plant cultivation model, II algorithm optimization plant cultivation model of NSGA- is recycled, really
The irrigation amount of plant, dose, the optimal value of fertilization type are determined, and immediate feedback are to user, allows user whenever and wherever possible can
Understand plant the present situation, realize intelligent cultivation.
Description of the drawings
Fig. 1 is that the flow process of the plant cultivating method based on the analysis of Internet of Things big data according to the embodiment of the present invention is illustrated
Figure；
Fig. 2 is to be predicted the outcome figure according to the health index of the embodiment of the present invention；
Fig. 3 is the health index prediction-error image according to the embodiment of the present invention；
Fig. 4 is the user interface schematic diagram according to the embodiment of the present invention.
Specific embodiment
Fig. 1 shows the flow process of the plant cultivating method based on the analysis of Internet of Things big data according to embodiments of the present invention.
As shown in figure 1, the plant cultivating method based on the analysis of Internet of Things big data of the present invention, including：
Step S1：The species of herborization, growth period, soil moisture, soil pH value, intensity of illumination, ambient temperature, ring
Border humidity, image, irrigation amount, dose, Fertilizer Type simultaneously constitute influence factor matrix X, and upload onto the server；Wherein, pour
The water yield, dose and Fertilizer Type constitute decision variable.
Health index y to plant is obtained by statistics1The maximum variable of impact is：Floristics x1, growth period x2、
Soil moisture x3, soil pH value x4, intensity of illumination x5, ambient temperature x6, ambient humidity x7, image x8, irrigation amount x9, dose
x10, Fertilizer Type x11, totally 11 variables；Wherein, soil moisture x3, soil pH value x4, intensity of illumination x5, ambient temperature x6, environment
Humidity x7, image x8By corresponding sensor measurement data, floristics, growth period are build-in attribute, by user input, to pour
The water yield, dose, Fertilizer Type are decision variable.
Ambient temperature x of plant6Obtained by temperature sensor collection；Soil moisture x of plant3With ambient humidity x7Logical
Cross the acquisition of humidity sensor acquisition；Intensity of illumination x of plant5Obtained by illuminance sensor collection；The soil pH value x of plant4
Obtained by the collection of soil pH meter；Using sample circuit respectively with temperature sensor, humidity sensor, illuminance sensor, soil
Earth pH meter is attached, and the ring that temperature sensor, humidity sensor, illuminance sensor, soil pH meter are collected respectively
Border temperature, ambient humidity, soil moisture, intensity of illumination, P in soil H-number are converted into digital signal.
Plant is obtained by photographic head collection in the characteristic image of current time, and photographic head converts image information into numeral
Signal.
In the present invention, server is preferably Cloud Server.
Step S2：Referred to plant health using each influence factor's matrix X of Elman neural network plant in server
Complex nonlinear relation between number, obtains plant cultivation model.
X is setk=[xk1,xk2,L,xkM] (k=1,2, L, S) be input vector, N be training sample number,
For during the g time iteration between input layer M and hidden layer I
Weighted vector, WJPG () is weighted vector during the g time iteration between hidden layer J and output layer P, WJCWhen () is the g time iteration g
Hidden layer J and the weighted vector Y for accepting between layer Ck(g)=[yk1(g),yk2(g),L,ykP(g)] (k=1,2, L, S) be the g time
The reality output of network, d during iterationk=[dk1,dk2,L,dkP] (k=1,2, L, S) be desired output, iterationses g be 500.
Using between each influence factor's matrix X of Elman neural network plant and plant health index in server
Complex nonlinear relation, obtain plant cultivation model, including：
Step S21：Initialization, if iterationses g initial value is 0, to be assigned to W respectivelyMI(0)、WJP(0)、WJC(0) one (0,1)
Interval random value；
Step S22：Stochastic inputs sample Xk；
Step S23：To input sample Xk, reality output Y of forward calculation Elman per layer of neuron of neutral netk(g)；
Step S24：According to desired output dkWith reality output Yk(g), calculation error E (g)；
Step S25：Whether error in judgement E (g) is less than default error amount, if greater than or be equal to, enter step S26,
If it is less, entering step S29；
Step S26：Whether iterationses g+1 is judged more than maximum iteration time, if it does, step S29 is entered, no
Then, step S27 is entered；
Step S27：To input sample XkThe partial gradient δ of backwards calculation Elman per layer of neuron of neutral net；
Step S28：Modified weight amount Δ W is calculated, and revises weights；G=g+1 is made, jumps to step S23；
Wherein, Δ Wij=η δij, η is the learning efficiency；Wij(g+1)=Wij(g)+ΔWij(g)；
Step S29：Judge whether to complete the training of all samples；If it is, completing modeling；If not, jumping to step
S22.
In the design of Elman neutral net, the number of hidden nodes number be the pass for determining Elman neural network model quality
Difficult point in key, and the design of Elman neutral net, determines the nodes of hidden layer here using trial and error procedure.
In formula, it is input layer number that p is hidden neuron nodes, n, and it is 1-10 that m is output layer neuron number, k
Between constant.The arrange parameter of Elman neutral net is as shown in table 2 below.
Table 2Elman neutral net arrange parameter
| Object function | Health index |
| Iterationses | 500 |
| Hidden layer transmission function | Tansig |
| Output layer transmission function | Purelin |
| Node in hidden layer | 15 |
By said process, Elman neural network prediction effect is obtained as shown in Figures 2 and 3.The base of intelligent plant cultivation
Plinth is the foundation of model, and model accuracy directly affects output result.By to the analysis of Fig. 2 and 3, the pre- maximum of health index is surveyed
Error is -3.5%, model prediction accuracy height, meets modeling demand.
Step S3：Using II algorithm of NSGA- (Non-dominated Sorting Genetic Algorithm- II, band
The genetic algorithm of the non-dominated ranking of elitism strategy) plant cultivation model is optimized, obtain decision variable one group is optimum
Solution.
Obtain one group of optimal solution of decision variable, that is, obtain the irrigation amount of plant, dose, one group of Fertilizer Type
Optimal value.
The step of plant cultivation model being optimized using II algorithm of NSGA- is included：
Step S31：Initialization system parameter；Wherein, the systematic parameter include population scale N, maximum genetic algebra G,
Crossover probability P and mutation probability Q.
Step S32：The new population Q that produce t generationtWith its parent population PtMerge composition population Rt, population RtSize
For 2N；If first generation population, then using first generation population as population Rt.
Step S33：To population RtNon-dominated ranking is carried out, obtains a series of non-dominant collection Zi, and calculate non-dominant collection Zi
In each individual crowding, produce new parent population Pt+1.
The detailed process of step S33 is as follows：
Step S331：Population R is judged using fitness functiontIn all individuality between mutual dominance relation；Wherein,
D (i) .n represents the individual amount of i-th individuality of domination, and D (i) .p represents by the individual collections of i-th individual domination；If individuality i
Domination j, then be put into D (i) .p set by individual j, and the value of D (j) .n adds 1；Operate successively, obtain population RtIn all individuality D
The information of (i) .n and D (i) .p.
Step S332：By population RtIn all D (i) .n values for 0 individuality, i.e. such individuality not by other individuality domination,
The ground floor of non-dominant layer is put into, the individuality that D (i) .n value is 1 is put into the second layer of non-dominant layer, is operated successively, until inciting somebody to action
The population RtIn till all individualities are put into different non-dominant layers；Individuality in the same number of plies shares identical virtuality fitness
Value, series is less, and virtual fitness value is lower, individual more excellent in the layer, by the number of plies of non-dominant layer by order from small to large
It is ranked up.
Step S333：Due to all individual shared same virtuality fitness values in each layer, when needs are selected in same layer
When more excellent individuality is selected, its crowding is calculated.
Crowding i of each pointdInitial value is set to 0；For each target, to the population RtNon-dominated ranking is carried out, order
The population RtTwo individual crowdings on border are infinite, to the population RtMiddle others individuality carries out the meter of crowding
Calculate：
Wherein, idRepresent the crowding of i point,Represent j-th target function value of i+1 point,Represent the of i-1 point
J target function value.
Step S334：After quick non-dominated ranking and crowding are calculated, population RtIn each individual i be owned by
Two attributes：Non-dominant sequence i that non-dominated ranking determinesrankWith crowding id.According to the two attributes, crowding can be defined
Comparison operator：Individual i is compared with individual j, if the non-dominant layer residing for individuality i is better than the non-dominant layer residing for individuality j,
That is irank＜ jrank, or, individual i and individual j has identical grade, and individuality i is longer than the crowding distance of individual j, i.e. irank=
jrankAnd id＞ jd, then individuality i triumph.
Step S335：Individuality and parent population P due to progeny populationt+1Individuality be included in population RtIn, then pass through
The later non-dominant collection Z of non-dominated ranking1In the individuality that includes be RtIn best, so first by non-dominant collection Z1It is put into parent
Population Pt+1；If parent population Pt+1Individual amount without departing from population scale N, then by the non-dominant collection Z of next stage2It is put into father
For population Pt+1, until by non-dominant collection Z3It is put into parent population Pt+1When, parent population Pt+1Individual amount exceed population scale
N, to non-dominant collection Z3In individuality be compared using crowding comparison operator, { num (Z before taking3)-(num(Pt+1)-N) individual
Individuality, makes parent population Pt+1Individual amount reach population scale N.
Step S34：To parent population Pt+1Carry out intersecting, the basic genetic that makes a variation operation obtains progeny population Qt+1.
To parent population Pt+1The process for carrying out criss-cross inheritance operation is：
By parent population Pt+1It is right that interior all individualities are mixed at random, to individual per a pair, generates a random number, if
The random number of certain a pair of individuality is less than crossover probability P, then exchange this to the chromosome dyad between individuality.
To parent population Pt+1Enter row variation basic genetic operation process be：
To parent population Pt+1In each is individual, generate a random number, if certain individual random number is less than variation
Probability Q, then it is other genic values to change the genic value on some or certain some locus of the individuality.
Step S35：Genetic algebra adds 1, judges whether genetic algebra reaches maximum genetic algebra G, if it is, output is current
Globally optimal solution；If not, jumping to step S32 to carry out double counting, until genetic algebra reaches maximum genetic algebra G it is
Only.
Step S4：Using the group optimal solution of decision variable as plant recommendation decision-making X*User is issued to by server
Terminal unit shown.
Various kinds of sensors gathered a secondary data and uploads onto the server per 2 hours, and server connects data and by plant cultivation
Model provides irrigation amount, dose and the fertilization type that plant is currently recommended.
Step S5：User cultivates plant according to the recommendation decision-making that terminal unit shows.
User can open intelligent plant cultivation interface (as shown in Figure 4) on the terminal device, the interface display plant
Brief information, the brief information of plant includes image and the current health index of plant, and user can arrange the reason of plant at interface
Think health index, ideal, recommendation irrigation amount, dose, Fertilizer Type are issued by server, user can pass through mobile phone long-distance operating
Complete automatic watering function, fertilising.
The current health index of plant is obtained by being optimized to plant cultivation model based on II algorithm of NSGA-, plant
Current health index is corresponding with the one of decision variable group of optimal solution.
The plant cultivating method based on the analysis of Internet of Things big data that the present invention is provided, first, using sensor, photographic head
Deng hardware herborization index parameter, plant image, irrigation amount, dose, Fertilizer Type, then, by the data for collecting
Reach server to be stored, using Elman neural network influence factor's matrix X and plant health index in server
Between complex nonlinear relation, obtain plant cultivation model, using II algorithm of NSGA-, plant cultivation model is optimized,
One group of optimal value of each decision variable is obtained, and using this group optimal solution as PC the or APP terminal for recommending decision-making to be issued to user,
Finally, user can realize remote auto cultivation according to the irrigation amount of recommendation decision-making decision plant, dose, fertilization type.The party
Method can determine the plant cultivation scheme of optimum, be that plant has built more preferable living environment.
Corresponding with said method, the present invention also provides a kind of plant cultivation system based on the analysis of Internet of Things big data.
The plant cultivation system based on the analysis of Internet of Things big data that the present invention is provided, including：
Data acquisition unit, for the species of herborization, growth period, soil moisture, soil pH value, intensity of illumination,
Ambient temperature, ambient humidity, image, irrigation amount, dose, Fertilizer Type simultaneously constitute influence factor matrix X, and be uploaded to service
Device；Wherein, irrigation amount, dose and Fertilizer Type constitute decision variable.The process of data acquisition unit gathered data is with reference to upper
State step S1.
Unit set up by plant cultivation model, in server using Elman neural network plant respectively affect because
Complex nonlinear relation between prime matrix X and plant health index, obtains plant cultivation model.Plant cultivation model is set up single
Unit sets up the detailed process of plant cultivation model with reference to above-mentioned steps S2.
Decision variable optimal solution acquiring unit, for being optimized to plant cultivation model using II algorithm of NSGA-, is obtained
One group of optimal solution of decision variable, and using the group optimal solution of decision variable as plant recommendation decision-making X*.Decision variable is most
The detailed process that excellent solution acquiring unit obtains decision variable optimal solution refers to above-mentioned steps S3.
Recommend decision-making issuance unit, for passing through server by the recommendation decision-making X of plant*It is issued to the terminal unit of user
Shown.
User is cultivated to plant according to the recommendation decision-making that terminal unit shows.
It should be pointed out that described above is not limitation of the present invention, the present invention is also not limited to the example above,
Change, modification, interpolation or replacement that those skilled in the art are made in the essential scope of the present invention, also should
Belong to protection scope of the present invention.
Claims (8)
1. a kind of based on Internet of Things big data analysis plant cultivating method, it is characterised in that comprise the steps：
Step S1：The species of herborization, growth period, soil moisture, soil pH value, intensity of illumination, ambient temperature, environmental wet
Degree, image, irrigation amount, dose, Fertilizer Type simultaneously constitute influence factor matrix X, and upload onto the server；Wherein, described pour
The water yield, the dose and the Fertilizer Type constitute decision variable；
Step S2：Referred to plant health using each influence factor's matrix X of Elman neural network plant in the server
Complex nonlinear relation between number, obtains plant cultivation model；
Step S3：Using II algorithm of NSGA-, the plant cultivation model is optimized, obtains one group of the decision variable most
Excellent solution；
Step S4：Using the group optimal solution of the decision variable as the plant recommendation decision-making X*By under the server
The terminal unit for being sent to user is shown；
Step S5：The user cultivates the plant according to the recommendation decision-making that the terminal unit shows.
2. according to claim 1 based on Internet of Things big data analysis plant cultivating method, it is characterised in that the plant
Thing cultivates X in modelk=[xk1,xk2,L,xkM] (k=1,2, L, S) be input vector, S for training sample number, WMI(g)
For weighted vector during the g time iteration between input layer M and hidden layer I, WJP(g) be the g time iteration when hidden layer J and output layer P it
Between weighted vector, WJCG () is hidden layer J and the weighted vector that accepts between layer C, Y during the g time iterationk(g)=[yk1(g),yk2
(g),L,ykP(g)] (k=1,2, L, S) be reality output during the g time iteration, dk=[dk1,dk2,L,dkP] (k=1,2, L,
S) it is desired output；And,
The step of setting up the plant cultivation model includes：
Step S21：Initialization, if iterationses g initial value is 0, to be assigned to W respectivelyMI(0)、WJP(0)、WJC(0) one (0,1) interval
Random value；
Step S22：Stochastic inputs sample Xk；
Step S23：To input sample Xk, reality output Y of per layer of neuron of Elman neutral net described in forward calculationk(g)；
Step S24：According to desired output dkWith reality output Yk(g), calculation error E (g)；
Step S25：Whether error in judgement E (g) is less than default error amount, if greater than or be equal to, enter step S26, if
It is less than, then enters step S29；
Step S26：Judge that iterationses g+1, whether more than maximum iteration time, if it does, step S29 is entered, otherwise, enters
Enter step S27；
Step S27：To input sample XkThe partial gradient δ of per layer of neuron of Elman neutral net described in backwards calculation；
Step S28：Modified weight amount Δ W is calculated, and revises weights；G=g+1 is made, jumps to step S23；
Wherein, Δ Wij=η δij, η is the learning efficiency；Wij(g+1)=Wij(g)+ΔWij(g)；
Step S29：Judge whether to complete the training of all samples；If it is, completing modeling；If not, jumping to step S22.
3. according to claim 1 based on Internet of Things big data analysis plant cultivating method, it is characterised in that utilize
The step of II algorithm of NSGA- is optimized to the plant cultivation model, including：
Step S31：Initialization system parameter；Wherein, the systematic parameter includes population scale N, maximum genetic algebra G, intersection
Probability P and mutation probability Q；
Step S32：The new population Q that produce t generationtWith its parent population PtMerge composition population Rt, population RtSize be 2N；
If first generation population, then using first generation population as the population Rt；
Step S33：To the population RtNon-dominated ranking is carried out, obtains a series of non-dominant collection Zi, and calculate the non-dominant
Collection ZiIn each individual crowding, produce new parent population Pt+1；
Step S34：To the parent population Pt+1Carry out intersecting, the basic genetic that makes a variation operation obtains progeny population Qt+1；
Step S35：Genetic algebra adds 1, judges whether genetic algebra reaches the maximum genetic algebra G, if it is, output is current
Globally optimal solution；If not, jump to step S32 double counting is carried out, until genetic algebra reaches the maximum genetic algebra G
Till.
4. according to claim 3 based on Internet of Things big data analysis plant cultivating method, it is characterised in that step
S33 includes：
Step S331：The population R is judged using fitness functiontIn all individuality between mutual dominance relation；Wherein, D
I () .n represents the individual amount of i-th individuality of domination, D (i) .p represents by the individual collections of i-th individual domination；If individuality i
Domination j, then be put into D (i) .p set by individual j, and the value of D (j) .n adds 1；Operate successively, obtain all individuality D (i) .n and D
The information of (i) .p；
Step S332：By the population RtIn all D (i) .n values for 0 individuality, be put into the ground floor of non-dominant layer, by the kind
Group RtIn all D (i) .n values be put into the second layer of non-dominant layer for 1 individuality, until by the population RtIn all individualities be put into
Till different non-dominant layers, the number of plies of non-dominant layer is ranked up by order from small to large；
Step S333：For each target, to the population RtNon-dominated ranking is carried out, makes the population RtTwo of border
The crowding of body is infinite, to the population RtMiddle others individuality carries out the calculating of crowding：
Wherein, idRepresent the crowding of i point,Represent j-th target function value of i+1 point,Represent j-th of i-1 point
Target function value；
Step S334：Non-dominant sequence i that non-dominated ranking is determinedrankWith crowding idAs the population RtIn per each and every one
Two attributes of body i, define crowding comparison operator：Individual i is compared with individual j, if the non-dominant layer residing for individuality i
Better than non-dominant layer, i.e. i residing for individuality jrank＜ jrank, or individuality i and individual j has an identical grade, and individuality i than
The crowding distance of body j is long, i.e. irank=jrankAnd id＞ jd, then individuality i triumph；
Step S335：By non-dominant collection Z1It is put into the parent population Pt+1；If the parent population Pt+1Individual amount do not surpass
Go out the population scale N, then by the non-dominant collection Z of next stage2It is put into the parent population Pt+1, until by non-dominant collection Z3It is put into
The parent population Pt+1When, the parent population Pt+1Individual amount exceed the population scale N, to the non-dominant collection Z3
In individuality be compared using the crowding comparison operator, { num (Z before taking3)-(num(Pt+1)-N) individuality, make institute
State parent population Pt+1Individual amount reach the population scale N.
5. according to claim 3 based on Internet of Things big data analysis plant cultivating method, it is characterised in that to described
Parent population Pt+1The process for carrying out criss-cross inheritance operation is：
By the parent population Pt+1It is right that interior all individualities are mixed at random, to individual per a pair, generates a random number, if
The random number of certain a pair of individuality is less than the crossover probability P, then exchange this to the chromosome dyad between individuality.
6. according to claim 3 based on Internet of Things big data analysis plant cultivating method, it is characterised in that to described
Parent population Pt+1Enter row variation basic genetic operation process be：
To the parent population Pt+1In each is individual, generate a random number, if certain individual random number is less than described
Mutation probability Q, then it is other genic values to change the genic value on some or certain some locus of the individuality.
7. according to any one of claim 1-6 based on Internet of Things big data analysis plant cultivating method, its feature
It is,
The ambient temperature of the plant is gathered using temperature sensor；
Ambient humidity and the soil moisture of the plant are gathered using humidity sensor；
The intensity of illumination of the plant is gathered using illuminance sensor；
P in soil H-number using the above-mentioned plant of P in soil H meter collection；
Feature of the plant in current time is gathered using photographic head, and convert image information into digital signal；And,
Using sample circuit respectively with the temperature sensor, the humidity sensor, the illuminance sensor, the soil
PH meter is attached, and the temperature sensor, the humidity sensor, the illuminance sensor, P in soil H are counted
The ambient temperature that collects respectively, ambient humidity, soil moisture, intensity of illumination, P in soil H-number are converted into digital signal.
8. a kind of based on Internet of Things big data analysis plant cultivation system, including：
Data acquisition unit, for the species of herborization, growth period, soil moisture, soil pH value, intensity of illumination, environment
Temperature, ambient humidity, image, irrigation amount, dose, Fertilizer Type simultaneously constitute influence factor matrix X, and upload onto the server；
Wherein, the irrigation amount, the dose and the Fertilizer Type constitute decision variable；
Unit set up by plant cultivation model, in the server using Elman neural network plant respectively affect because
Complex nonlinear relation between prime matrix X and plant health index, obtains plant cultivation model；
Decision variable optimal solution acquiring unit, for being optimized to the plant cultivation model using II algorithm of NSGA-, is obtained
One group of optimal solution of the decision variable, and using the group optimal solution of the decision variable as the plant recommendation decision-making
X*；
Recommend decision-making issuance unit, for by the server by the recommendation decision-making X of the plant*It is issued to the terminal of user
Equipment is shown.
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