CN103777520A - Crop chlorophyll content online detection-based drip irrigation automatic control method - Google Patents
Crop chlorophyll content online detection-based drip irrigation automatic control method Download PDFInfo
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- CN103777520A CN103777520A CN201210411274.0A CN201210411274A CN103777520A CN 103777520 A CN103777520 A CN 103777520A CN 201210411274 A CN201210411274 A CN 201210411274A CN 103777520 A CN103777520 A CN 103777520A
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Abstract
A crop chlorophyll content online detection-based drip irrigation automatic control method is provided. According to the crop chlorophyll content online detection-based drip irrigation automatic control method of the invention, leaf chlorophyll content which is closely related to crop growth and water demand in crop physiological parameters is directly and dynamically detected; optimal feed water quantity is predicted and estimated through a drip irrigation prediction model; dynamic calculation of the optimal feed water quantity can be realized through a dynamic water quantity adjustment module and an optimal water quantity calculation module; and optimal control of feed water quantity adjustment can be realized through a water quantity dynamic adjustment ranger fuzzy control module. With the crop chlorophyll content online detection-based drip irrigation automatic control method adopted, the prediction, optimization and adaptive control of feed water quantity can be realized, and feed water adaptive control of different kinds of crops at different regions, in different seasons and under different climate and weather conditions can be realized, and irrigation water can be saved, and at the same time, heath growth of the crops can be ensured.
Description
Technical field
The present invention relates to a kind of agriculture automatic dripping irrigation control method, especially, based on the online drip irrigation control method detecting of crop chlorophyll content, belong to the technical field such as agricultural engineering and automatic control.
Background technology
Under the background of electronic technology and automatic control technology develop rapidly, industrialized agriculture has obtained significant progress, and particularly drip irrigation technique is that agriculture water saving, joint fertilizer has been made huge contribution.Existing drip irrigation control technology is mainly two kinds, and one is to carry out timing controlled by setting-up time, and another kind is that the humidity by detecting soil (or matrix) is carried out FEEDBACK CONTROL.Timing controlled need to arrange drip irrigation cycle and drip irrigation time period according to the particular growth of locality, specific environment parameter (aerial temperature and humidity, gas concentration lwevel, substrate temperature etc.), specific crop species and crop, therefore need long-term planting experiment to accumulate relevant drip irrigation experience, and be difficult to accurately control drip irrigation.And the drip irrigation control method of feeding back based on soil (or matrix) Humidity Detection, by threshold values and lower threshold values in humidity are set, when the soil moisture detecting lower than set lower threshold values time open solenoid valve and carry out drip irrigation, control solenoid valve when the humidity detecting during higher than upper threshold values and stop drip irrigation, the humidity that dynamically maintains soil (or matrix) with this is between upper threshold values and lower threshold values, the problem that this control exists is the optimum growh that has had influence on crop in the time that soil moisture is low, if controlled humidity is too high, can waste water, therefore be also difficult to accomplish save water resource in guaranteeing crop optimum growing environment.Therefore needing research one badly can more effective drip irrigation control method.
Summary of the invention
For overcoming the defect of prior art, the present invention proposes a kind of drip irrigation autocontrol method of, detection of dynamic online based on crop chlorophyll content, can realize the optimum growh of crop, and effective saving water resource.
For achieving the above object, the present invention is by the following technical solutions:
A kind of drip irrigation control method of the present invention:
Step 1: the detected value c of crop blade face chlorophyll content is input to " drip irrigation amount forecast model ";
Step 2: " drip irrigation amount forecast model " output drip irrigation amount predicted value
to " dynamically water yield adjusting module ", " the dynamic setting range fuzzy control model of the water yield " output water yield setting range Δ w is to " dynamically water yield adjusting module ";
Step 3: " dynamically water yield adjusting module " is according to what input
w exists with Δ
scope in adjust confluent w take mw as incremented, from
start take mw as incremented until be no more than
real-time confluent w and chlorophyll detected value c are input to " optimum water yield computing module ", if wherein w≤0 o'clock, not drip irrigation in confluent w adjustment process simultaneously;
Step 4: when " optimum water yield computing module " adjusted according to confluent in step 3, the series of values of confluent w and chlorophyll detected value c calculates optimum confluent w*;
Step 5: optimum confluent w* is input to " drip irrigation amount forecast model " for dynamically adjusting the parameter of " drip irrigation amount forecast model ";
Step 6: the drip irrigation amount predicted value of " drip irrigation amount forecast model " output
poor with the output w* of " optimum water yield computing module "
be input to " the dynamic setting range fuzzy control model of the water yield ", the differential dw*/dt of the output w* of " optimum water yield computing module " is also input to " the dynamic setting range fuzzy control model of the water yield ", and " the dynamic setting range fuzzy control model of the water yield " is according to these two input numerical value output water yield setting range Δ w;
Step 7: forward step 1 to.
The calculating of optimum water yield w* in step 4 of the present invention, " dynamically water yield adjusting module " and " optimum water yield computing module " course of work are:
Set numerical value i=0, feedwater increment mw=1 milliliter, array A and array B;
Step is 1.: if
?
Control drip irrigation (the drip irrigation water yield is mw), otherwise A[i]=0, drip irrigation stopped;
Step is 2.: detect crop blade face chlorophyll content c, and make B[i]=c;
Step is 3.: make i=i+1, if
forward step 1 to, otherwise forward step 4 to;
Step is 4.: array B is carried out to differential calculation according to data sequence, and record the 1st data sorting that differential result is 0, be made as j, B ' (j)=0 and B ' (j-1) ≠ 0, make w*=A (j).
" drip irrigation amount forecast model " described in step 5 of the present invention adopts generalized regression nerve net (GRNN), builds the GRNN network structure of single-input single-output, and the dynamic state of parameters adjustment process of GRNN net is as follows:
Set numerical value k=0, set array C and array D;
Step a: wait new optimum water yield w* to be entered;
Step b: if k≤15, C[k]=w*, k=k+1, proceeds to step a; 15 of k > proceed to step c else if;
Step c:D[15]=w*, set numerical value m=1, D[15-m]=C[k-m];
Steps d: if m < 16 proceeds to step c, otherwise proceed to step e;
Step e: as sequence input time, train the parameter of GRNN net with array D;
Step f: calculate the output data of next time by GRNN net, i.e. drip irrigation amount predicted value
Step g: k=k+1, is transferred to step a.
Wherein, the training of GRNN network parameters in step e only needs the smooth factor of training, and training method is according to following steps:
(i) make the smooth factor with increment incremental variations in certain scope;
(ii), in learning sample, remove one, two sample, by remaining sample training neural network, with this, two samples test;
(iii) network model that use builds calculates the Error Absolute Value of test sample book, i.e. predicated error;
(iiii) repeating step (ii), (iii), until all training samples are all once for test, try to achieve the mean value of predicated error and sets it as the objective function E of optimizing.
In step 6 of the present invention, " the dynamic setting range fuzzy control model of the water yield " adopts Mamdani Fuzzy controller, and its input quantity is respectively drip irrigation amount predicted value
difference ew and optimum water yield w with the output w* of " optimum water yield computing module "
*variation dw
*/ dt, ew and dw
*/ dt passes through respectively quantizing factor k
ewand k
dwprocess wherein ew '=k
ew* ew, dw '=k
dw* (dw
*/ dt), then be input in fuzzy controller, first carry out Fuzzy processing, the fuzzy language variable of ew ' and dw ' is all divided into 5 subitems: " negative large " (NB), " negative little " (NS), " zero " (ZE), " just little " (PS), " honest " (PB), the membership function of each subitem is being taken as Gaussian function on domain separately, fuzzy inference rule is as shown in table 1, Δ w ' exports Δ w again after de-fuzzy ", Δ w " is multiplied by scale factor k
wrear output water yield setting range Δ w.
Table 1 fuzzy inference rule
Compared with prior art, beneficial effect of the present invention is as follows:
Existing drip irrigation control technology, as controlled by the time and passing through Temperature and Humidity Control drip irrigation etc., all to predict water requirements of crops by indirect mode, all need that specific crop is carried out to a large amount of planting experiments in early stage and obtain empirical data, then control on this basis the good effect that is determining plantation of empirical data; In addition, to different crops, the strategy of drip irrigation control and mode otherness are larger, and therefore universality is poor; Moreover to different weather, season and region, existing control cannot realize adaptive control.
The present invention is by direct, in detection of dynamic plant physiology parameter with plant growth with need the closely-related chlorophyll content in leaf blades of water, carry out the prediction of optimum confluent estimates by " drip irrigation amount forecast model ", realize the dynamic calculation of optimum confluent by " dynamically water yield adjusting module " and " optimum water yield computing module ", and realize by " the dynamic setting range fuzzy control model of the water yield " optimal control that confluent is adjusted, can realize the prediction of confluent, optimize and adaptive control, can realize different geographical, Various Seasonal, the feedwater adaptive control of variety classes crop under Different climate and DIFFERENT METEOROLOGICAL CONDITIONS and saving irrigation water, guarantee that plant growth is good simultaneously.
Accompanying drawing explanation
Fig. 1 is the principle of work block diagram of the inventive method;
Fig. 2 is the process flow diagram that in the present invention, GRNN network parameters is dynamically adjusted;
Fig. 3 is the theory diagram of fuzzy controller in the present invention.
Embodiment
Below in conjunction with drawings and the specific embodiments, the invention will be further described.
As shown in Figure 1, a kind of drip irrigation control method:
Step 1: the detected value c of crop blade face chlorophyll content is input to " drip irrigation amount forecast model ";
Step 2: " drip irrigation amount forecast model " output drip irrigation amount predicted value
to " dynamically water yield adjusting module ", " the dynamic setting range fuzzy control model of the water yield " output water yield setting range Δ w is to " dynamically water yield adjusting module ";
Step 3: " dynamically water yield adjusting module " is according to what input
w exists with Δ
scope in adjust confluent w take mw as incremented, from
start take mw as incremented until be no more than
real-time confluent w and chlorophyll detected value c are input to " optimum water yield computing module ", if wherein w≤0 o'clock, not drip irrigation in confluent w adjustment process simultaneously;
Step 4: when " optimum water yield computing module " adjusted according to confluent in step 3, the series of values of confluent w and chlorophyll detected value c calculates optimum confluent w*;
Step 5: optimum confluent w* is input to " drip irrigation amount forecast model " for dynamically adjusting the parameter of " drip irrigation amount forecast model ";
Step 6: the drip irrigation amount predicted value of " drip irrigation amount forecast model " output
poor with the output w* of " optimum water yield computing module "
be input to " the dynamic setting range fuzzy control model of the water yield ", the differential dw*/dt of the output w* of " optimum water yield computing module " is also input to " the dynamic setting range fuzzy control model of the water yield ", and " the dynamic setting range fuzzy control model of the water yield " is according to these two input numerical value output water yield setting range Δ w;
Step 7: forward step 1 to.
Embodiment 2
In the present invention, the calculating of optimum water yield w*, " dynamically water yield adjusting module " and " optimum water yield computing module " course of work are:
Set numerical value i=0, feedwater increment mw=1 milliliter, array A and array B;
Step is 1.: if
?
Control drip irrigation (the drip irrigation water yield is mw), otherwise A[i]=0, drip irrigation stopped;
Step is 2.: detect crop blade face chlorophyll content c, and make B[i]=c;
Step is 3.: make i=i+1, if
forward step 1 to, otherwise forward step 4 to;
Step is 4.: array B is carried out to differential calculation according to data sequence, and record the 1st data sorting that differential result is 0, be made as j, B ' (j)=0 and B ' (j-1) ≠ 0, make w*=A (j).
Embodiment 3
As shown in Figure 2, " the drip irrigation amount forecast model " described in the present invention adopts generalized regression nerve net (GRNN), builds the GRNN network structure of single-input single-output, and the dynamic state of parameters adjustment process idiographic flow of GRNN net is:
Set numerical value k=0, set array C and array D;
Step a: wait new optimum water yield w* to be entered;
Step b: if k≤15, C[k]=w*, k=k+1, proceeds to step a; 15 of k > proceed to step c else if;
Step c:D[15]=w*, set numerical value m=1, D[15-m]=C[k-m];
Steps d: if m < 16 proceeds to step c, otherwise proceed to step e;
Step e: as sequence input time, train the parameter of GRNN net with array D;
Step f: calculate the output data of next time by GRNN net, i.e. drip irrigation amount predicted value
Step g: k=k+1, is transferred to step a.
Wherein, the training of GRNN network parameters in step e only needs the smooth factor of training, and training method is according to following steps:
(ii) make the smooth factor with increment incremental variations in certain scope;
(ii), in learning sample, remove one, two sample, by remaining sample training neural network, with this, two samples test;
(iii) network model that use builds calculates the Error Absolute Value of test sample book, i.e. predicated error;
(iiii) repeating step (ii), (iii), until all training samples are all once for test, try to achieve the mean value of predicated error and sets it as the objective function E of optimizing.
Embodiment 4
As shown in Figure 3, " the dynamic setting range fuzzy control model of the water yield " of the present invention adopts Mamdani Fuzzy controller, and its input quantity is respectively drip irrigation amount predicted value
difference ew and optimum water yield w with the output w* of " optimum water yield computing module "
*variation dw
*/ dt, ew and dw
*/ dt passes through respectively quantizing factor k
ewand k
dwprocess wherein ew '=k
ew* ew, dw '=k
dw* (dw*/dt), then be input in fuzzy controller, first carry out Fuzzy processing, the fuzzy language variable of ew ' and dw ' is all divided into 5 subitems: " negative large " (NB), " negative little " (NS), " zero " (ZE), " just little " (PS), " honest " (PB), the membership function of each subitem is being taken as Gaussian function on domain separately, fuzzy inference rule is as shown in table 2, Δ w ' exports Δ w again after de-fuzzy ", Δ w " exports water yield setting range Δ w after being multiplied by scale factor kw.
Table 2 fuzzy inference rule
Claims (2)
1. the automatic dripping irrigation control method that proportion of crop planting is used, is characterized by:
Step 1: the detected value c of crop blade face chlorophyll content is input to " drip irrigation amount forecast model ";
Step 2: " drip irrigation amount forecast model " output drip irrigation amount predicted value
to " dynamically water yield adjusting module ", " the dynamic setting range fuzzy control model of the water yield " output water yield setting range Δ w is to " dynamically water yield adjusting module ";
Step 3: " dynamically water yield adjusting module " is according to what input
w exists with Δ
scope in adjust confluent w take mw as incremented, from
start take mw as incremented until be no more than
real-time confluent w and chlorophyll detected value c are input to " optimum water yield computing module ", if wherein w≤0 o'clock, not drip irrigation in confluent w adjustment process simultaneously;
Step 4: when " optimum water yield computing module " adjusted according to confluent in step 3, the series of values of confluent w and chlorophyll detected value c calculates optimum confluent w*;
Step 5: optimum confluent w* is input to " drip irrigation amount forecast model " for dynamically adjusting the parameter of " drip irrigation amount forecast model ";
Step 6: the drip irrigation amount predicted value of " drip irrigation amount forecast model " output
poor with the output w* of " optimum water yield computing module "
be input to " the dynamic setting range fuzzy control model of the water yield ", the differential dw*/dt of the output w* of " optimum water yield computing module " is also input to " the dynamic setting range fuzzy control model of the water yield ", and " the dynamic setting range fuzzy control model of the water yield " is according to these two input numerical value output water yield setting range Δ w;
Step 7: forward step 1 to.
2. the calculating of optimum water yield w* in automatic dripping irrigation control method claimed in claim 1, is characterized by according to following steps and calculates:
Set numerical value i=0, feedwater increment mw=1 milliliter, array A and array B;
Step is 1.: if
?
Control drip irrigation (the drip irrigation water yield is mw), otherwise A[i]=0, drip irrigation stopped;
Step is 2.: detect crop blade face chlorophyll content c, and make B[i]=c;
Step is 3.: make i=i+1, if
forward step 1 to, otherwise forward step 4 to;
Step is 4.: array B is carried out to differential calculation according to data sequence, and record the 1st data sorting that differential result is 0, be made as j, B ' (j)=0 and B ' (j-1) ≠ 0, make w*=A (j).
" drip irrigation amount forecast model " claimed in claim 1, is characterized by and adopt generalized regression nerve net (GRNN), builds the GRNN network structure of single-input single-output, and the dynamic state of parameters adjustment process of GRNN net is calculated according to following steps:
Set numerical value k=0, set array C and array D;
Step a: wait new optimum water yield w* to be entered;
Step b: if k≤15, C[k]=w*, k=k+1, proceeds to step a; 15 of k > proceed to step c else if;
Step c:D[15]=w*, set numerical value m=1, D[15-m]=C[k-m];
Steps d: if m < 16 proceeds to step c, otherwise proceed to step e;
Step e: as sequence input time, train the parameter of GRNN net with array D;
Step f: calculate the output data of next time by GRNN net, i.e. drip irrigation amount predicted value
Step g: k=k+1, is transferred to step a.
Wherein, the training of GRNN network parameters in step e only needs the smooth factor of training, and training method is according to following steps:
(i) make the smooth factor with increment incremental variations in certain scope;
(ii), in learning sample, remove one, two sample, by remaining sample training neural network, with this, two samples test;
(iii) network model that use builds calculates the Error Absolute Value of test sample book, i.e. predicated error;
(iiii) repeating step (ii), (iii), until all training samples are all once for test, try to achieve the mean value of predicated error and sets it as the objective function E of optimizing.
" the dynamic setting range fuzzy control model of the water yield " claimed in claim 1, is characterized by: adopt Mamdani Fuzzy controller, its input quantity is respectively drip irrigation amount predicted value
difference ew and optimum water yield w with the output w* of " optimum water yield computing module "
*variation dw
*/ dt, ew and dw
*/ dt passes through respectively quantizing factor k
ewand k
dwprocess wherein ew '=k
ew* ew, dw '=k
dw* (dw
*/ dt), then be input in fuzzy controller, first carry out Fuzzy processing, the fuzzy language variable of ew ' and dw ' is all divided into 5 subitems: " negative large " (NB), " negative little " (NS), " zero " (ZE), " just little " (PS), " honest " (PB), the membership function of each subitem is being taken as Gaussian function on domain separately, fuzzy inference rule is as shown in the table, Δ w ' exports Δ w again after de-fuzzy ", Δ w " is multiplied by scale factor k
wrear output water yield setting range Δ w.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108633697A (en) * | 2018-05-15 | 2018-10-12 | 重庆科技学院 | A kind of foster culture method of the intelligent plant based on the daily data analysis of plant and cloud |
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| US4663703A (en) * | 1985-10-02 | 1987-05-05 | Westinghouse Electric Corp. | Predictive model reference adaptive controller |
| CN101422124A (en) * | 2008-12-12 | 2009-05-06 | 河北工业大学 | Greenhouse intelligent drip-irrigation device |
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| ES2377394A1 (en) * | 2010-02-10 | 2012-03-27 | Treelogic Telem�?Tica Y Lógica Racional Para La Empresa Europea S.L. | Irrigation system based on artificial intelligence. (Machine-translation by Google Translate, not legally binding) |
-
2012
- 2012-10-25 CN CN201210411274.0A patent/CN103777520B/en not_active Expired - Fee Related
Patent Citations (4)
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| US4663703A (en) * | 1985-10-02 | 1987-05-05 | Westinghouse Electric Corp. | Predictive model reference adaptive controller |
| CN101422124A (en) * | 2008-12-12 | 2009-05-06 | 河北工业大学 | Greenhouse intelligent drip-irrigation device |
| ES2377394A1 (en) * | 2010-02-10 | 2012-03-27 | Treelogic Telem�?Tica Y Lógica Racional Para La Empresa Europea S.L. | Irrigation system based on artificial intelligence. (Machine-translation by Google Translate, not legally binding) |
| CN202035353U (en) * | 2011-01-14 | 2011-11-16 | 吉林省农机装备科技创新中心 | Accurate drip irrigation system with independent consciousness |
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| CN108633697A (en) * | 2018-05-15 | 2018-10-12 | 重庆科技学院 | A kind of foster culture method of the intelligent plant based on the daily data analysis of plant and cloud |
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