CN104134003B - The crop yield amount Forecasting Methodology that knowledge based drives jointly with data - Google Patents
The crop yield amount Forecasting Methodology that knowledge based drives jointly with data Download PDFInfo
- Publication number
- CN104134003B CN104134003B CN201410371605.1A CN201410371605A CN104134003B CN 104134003 B CN104134003 B CN 104134003B CN 201410371605 A CN201410371605 A CN 201410371605A CN 104134003 B CN104134003 B CN 104134003B
- Authority
- CN
- China
- Prior art keywords
- crop
- mrow
- data
- model
- crop yield
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 56
- 230000007613 environmental effect Effects 0.000 claims abstract description 18
- 230000007246 mechanism Effects 0.000 claims abstract description 7
- 230000012010 growth Effects 0.000 claims description 16
- 210000000056 organ Anatomy 0.000 claims description 12
- 239000002028 Biomass Substances 0.000 claims description 7
- 238000013528 artificial neural network Methods 0.000 claims description 5
- 235000013399 edible fruits Nutrition 0.000 claims description 3
- 230000035508 accumulation Effects 0.000 claims description 2
- 238000009825 accumulation Methods 0.000 claims description 2
- 238000011478 gradient descent method Methods 0.000 claims description 2
- 238000004519 manufacturing process Methods 0.000 abstract description 5
- 230000003750 conditioning effect Effects 0.000 abstract description 4
- 238000012272 crop production Methods 0.000 description 5
- 241000196324 Embryophyta Species 0.000 description 4
- 230000008901 benefit Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 241000208340 Araliaceae Species 0.000 description 2
- 244000106835 Bindesalat Species 0.000 description 2
- 235000000318 Bindesalat Nutrition 0.000 description 2
- 235000007688 Lycopersicon esculentum Nutrition 0.000 description 2
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 2
- 235000003140 Panax quinquefolius Nutrition 0.000 description 2
- 240000003768 Solanum lycopersicum Species 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 235000008434 ginseng Nutrition 0.000 description 2
- 240000008067 Cucumis sativus Species 0.000 description 1
- 235000010799 Cucumis sativus var sativus Nutrition 0.000 description 1
- 241001269238 Data Species 0.000 description 1
- 241000209140 Triticum Species 0.000 description 1
- 235000021307 Triticum Nutrition 0.000 description 1
- 240000008042 Zea mays Species 0.000 description 1
- 235000005824 Zea mays ssp. parviglumis Nutrition 0.000 description 1
- 235000002017 Zea mays subsp mays Nutrition 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 235000005822 corn Nutrition 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000008635 plant growth Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000012827 research and development Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Breeding Of Plants And Reproduction By Means Of Culturing (AREA)
Abstract
The invention discloses the crop yield amount Forecasting Methodology that a kind of knowledge based and data drive jointly, this method comprises the following steps：Using the crop yield amount productive potentialities submodel based on data-driven, the single rate productive potentialities E of crop is obtained according to the environmental data of input；Using the crop modeling of Kernel-based methods, structure obtains the crop yield quantum model of knowledge based driving, then according in the single rate productive potentialities E of crop and crop mechanism parameter obtain the single rate y of crop；Using regional historical environmental data and the single rate y of crop, identification obtains model parameter θdAnd θk, the single rate of the area crops is predicted with meteorological data according to certain following region.The present invention can maximally utilize history environment data and crop knowledge model, model parameter is set to obtain systematization estimation, Potentials are more accurate, easy to use, and this provides relatively reliable method for the production of auxiliary chamber crop, environment conditioning and cultivation management.
Description
Technical field
The invention belongs to data processing method and general botany technical field, more particularly to a kind of knowledge based and data
The crop yield amount Forecasting Methodology driven jointly.
Background technology
Chamber crop production, environment conditioning and cultivation management need reliable crop yield amount Forecasting Methodology.Generally come
Say, crop yield amount Forecasting Methodology can be divided into based on data according to whether comprising the knowledge related to crop growth rule
The crop yield amount Forecasting Methodology of crop yield amount Forecasting Methodology and the knowledge based driving of driving.
The complicated growth behavior of crop is considered as black box by the crop yield amount Forecasting Methodology based on data-driven, to growing
Journey is not considered, and the structure of its method places one's entire reliance upon history environment data and crop yield amount data (do not include any crop
Related knowledge), such as use the crop yield amount Forecasting Methodology based on artificial neural network.This method can be by data
Habit mode carrys out the unknown non-linear relation for the treatment of mechanism, and the data under certain environment growth can be reached with higher prediction essence
Degree.But this method can not maximally utilize existing crop knowledge and go to improve the precision of prediction of crop yield amount, and when increasing
The complexity of model is lifted rapidly when adding output variable.
Different from the method based on data-driven, the crop yield amount Forecasting Methodology of knowledge based driving is then to be based on crop
The model that growth-development law is established, including the dynamic process such as the biomass of crop produces, distribution, leaf area formation, are such as based on
Cooperation beween China and France research and development and come plant function structural model (GreenLab), the model is a kind of general plant growth mould
Type, the industrial crops such as corn, wheat, cucumber, tomato are successfully applied to, and chamber crop production, environment can be aided in adjust
Control and cultivation management.But the model can not maximally utilize environmental data and go to improve the precision of prediction of crop yield amount.
Traditional crop yield amount Forecasting Methodology is different from, in order to maximally utilize crop knowledge and environmental data to improve
Crop yield amount precision of prediction, the present invention propose the crop yield amount Forecasting Methodology that a kind of knowledge based drives jointly with data,
Chamber crop production, environment conditioning and cultivation management can be aided in.
The content of the invention
It is an object of the invention to provide a kind of reliable crop yield amount Forecasting Methodology, and enable the method to maximize
Using existing crop knowledge and environmental data, crop yield amount precision of prediction is improved, and chamber crop production, ring can be aided in
Border regulates and controls and cultivation management.
To achieve the above object, the present invention provides the crop yield amount prediction side that a kind of knowledge based drives jointly with data
Method, this method comprise the following steps：
Step 1, crop yield amount productive potentialities submodel is built using the method based on data-driven, it is expressed as：
E=fd(x,θd),
Wherein, fd() represents the method based on data-driven, and x is the environmental data of input, and E is under corresponding environmental condition
The productive potentialities of crop yield amount, θdFor submodel E parameter；
Step 2, the crop yield quantum model of knowledge based driving is obtained using the crop modeling of Kernel-based methods, structure,
It is expressed as：
Y=fk(E,θk),
Wherein, fk() represents the crop modeling of Kernel-based methods, θkFor submodel y parameter, i.e., in crop mechanism ginseng
Number；
Step 3, the per unit area yield quantum model y of the crop obtained using regional historical environmental data and the step 2, is recognized
To the model parameter θ of the crop yield amount productive potentialities submodel based on data-drivendWith knowledge based driving
The model parameter θ of crop yield quantum modelk, with according to the meteorological data in certain following region come the single rate to the area crops
It is predicted.
The crop yield amount Forecasting Methodology that knowledge based proposed by the present invention drives jointly with data, which takes full advantage of, to be based on
The method of Knowledge driving and method based on data-driven each the advantages of, crop knowledge and environment number can be maximally utilized
According to the reliability of raising crop yield amount precision of prediction.Difference with the prior art of the present invention is mainly reflected in use of the present invention
Method based on data-driven, the crop yield amount productive potentialities submodel based on data-driven is built, using Kernel-based methods
Crop modeling, the crop yield quantum model of structure knowledge based driving, two submodels are coupling in one by the way of series connection
Rise.Therefore, the present invention can crop yield amount production of the more flexible applicating history environmental data structure based on data-driven
Potential Model, and crop growth rule (crop knowledge) is combined, crop yield amount is predicted from mechanistic point.It is real
Test and show, the advantages of the inventive method is integrated with the two, and there is higher crop yield amount precision of prediction.
The present invention utilizes regional historical environmental data and crop yield amount data, model parameter is picked out, for future
The single rate prediction of the area crops.The present invention can maximally utilize history environment data and crop knowledge model, make model
Parameter obtain systematization estimation, Potentials are more accurate, easy to use, this also for chamber crop production, environment conditioning and
Cultivation management provides relatively reliable method.
Brief description of the drawings
Fig. 1 is the theory diagram for the crop yield amount Forecasting Methodology that knowledge based of the present invention drives jointly with data.
Fig. 2 is the crop yield amount Forecasting Methodology that knowledge based according to an embodiment of the invention drives jointly with data
Block diagram.
Embodiment
For the object, technical solutions and advantages of the present invention are more clearly understood, below in conjunction with specific embodiment, and reference
Accompanying drawing, the present invention is described in more detail.
The theory diagram for the crop yield amount Forecasting Methodology that Fig. 1 is knowledge based of the present invention to be driven jointly with data, such as Fig. 1
It is shown, it the described method comprises the following steps：
Step 1, crop yield amount productive potentialities submodel is built using the method based on data-driven, it is expressed as：
E=fd(x,θd),
Wherein, fd() represents the method based on data-driven, and x is the environmental data of input, and E is to make under the environmental condition
The productive potentialities of thing single rate, θdFor submodel E parameter.
The step reflects influence of the climatic environment for crop yield amount.
Wherein, the environmental data includes but is not limited to temperature, illumination, CO2The meteorological datas such as concentration, relative humidity.
Step 2, the crop yield quantum model of knowledge based driving is obtained using the crop modeling of Kernel-based methods, structure；
Wherein, the crop yield quantum model of the knowledge based driving is expressed as：
Y=fk(E,θk),
Wherein, fk() represents the crop modeling of Kernel-based methods, θkFor submodel y parameter, i.e., in crop mechanism ginseng
Number.
The step 1 and step 2 it is also assumed that by the crop yield amount productive potentialities submodel based on data-driven and
The crop yield quantum model series coupled of knowledge based driving, obtain the crop yield amount that knowledge based drives jointly with data
Forecast model, for the model, input environment data, you can obtain the single rate of crop.
Step 3, the per unit area yield quantum model y of the crop obtained using regional historical environmental data and the step 2, is recognized
To the model parameter θ of the crop yield amount productive potentialities submodel based on data-drivendWith knowledge based driving
The model parameter θ of crop yield quantum modelk, for according to the meteorological data in certain following region come the list to the area crops
Yield is predicted.
In an embodiment of the present invention, the crop for obtaining knowledge based and data drive jointly is recognized using gradient descent method
Single rate prediction model parameterses θdAnd θk。
Fig. 2 is the crop yield amount Forecasting Methodology that knowledge based according to an embodiment of the invention drives jointly with data
Block diagram, as shown in Fig. 2 in an embodiment of the present invention, being driven based on radial basis function neural network (RBFN) structure based on data
Dynamic crop yield amount productive potentialities submodel, based on plant function structural model (GreenLab) or garden crop universal model
(HortiSim) the crop yield quantum model of knowledge based driving is built.
The production of the crop yield amount based on data-driven obtained based on radial basis function neural network (RBFN) structure is latent
Power submodel is represented by：
E=fd(x,θd)=Φ (x) θd,
Wherein, θdFor RBFN weighting parameter, Φ (x)=[φ1(x),φ2(x),…,φh(x)] it is RBF,
And
In plant function structural model (GreenLab), the biomass increment Q (i) of i-th of growth cycle is expressed as：
Wherein, E (i) is i-th of growth cycle crop yield amount productive potentialities under the cycle weather circumstance condition, r and
Sp is the inherent mechanism parameter grown, and usually carries out estimation setting according to actual production data, and S (i) is i-th of growth week
The total leaf area of the crop of phase.
For growth cycle i, the biomass table of Different Crop organ o accumulations is shown as：
Wherein, PoFor the crop organ o strong parameter in relative storehouse, usually carry out estimation according to crop actual production data and set
Fixed, fo be crop organ's o spread functions, and Q () is the biomass increment of different growth periods, and No () is organ o number, j
For the organ o growth age, k is subscript, and crop organ o generally includes blade b, internode e and fruit f.GreenLab models belong to
Common plant function structural model, as space is limited, the more details on GreenLab models repeat no more.
It is it should be noted that slightly different for different industrial crops, the calculating object of crop yield amount.Such as
Tomato crop, we are concerned with the yield of tamato fruit, then its single rate calculation formula is：
For romaine lettuce, concern is primarily with the blade of romaine lettuce, then its single rate calculation formula to be for we：
Particular embodiments described above, the purpose of the present invention, technical scheme and beneficial effect are carried out further in detail
Describe in detail it is bright, should be understood that the foregoing is only the present invention specific embodiment, be not intended to limit the invention, it is all
Within the spirit and principles in the present invention, any modification, equivalent substitution and improvements done etc., it should be included in the guarantor of the present invention
Within the scope of shield.
Claims (6)
- A kind of 1. crop yield amount Forecasting Methodology that knowledge based and data drive jointly, it is characterised in that this method include with Lower step：Step 1, crop yield amount productive potentialities submodel is built using the method based on data-driven, it is expressed as：E=fd(x,θd),Wherein, fd() represents the method based on data-driven, and x is the environmental data of input, and E is crop under corresponding environmental condition The productive potentialities of single rate, θdFor submodel E parameter；Step 2, the crop yield quantum model of knowledge based driving, its table are obtained using the crop modeling of Kernel-based methods, structure It is shown as：Y=fk(E,θk),Wherein, fk() represents the crop modeling of Kernel-based methods, θkFor submodel y parameter, i.e., in crop mechanism parameter；Step 3, the per unit area yield quantum model y of the crop obtained using regional historical environmental data and the step 2, identification obtain institute State the model parameter θ of the crop yield amount productive potentialities submodel based on data-drivendWith the crop of knowledge based driving The model parameter θ of per unit area yield quantum modelk, with the meteorological data according to certain following region come to the progress of the single rate of the area crops Prediction；Wherein, the crop yield quantum based on plant function structural model or the structure knowledge based driving of garden crop universal model Model；In the plant function structural model, the biomass increment Q (i) of i-th of growth cycle is expressed as：<mrow> <mi>Q</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>E</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>&CenterDot;</mo> <mi>r</mi> <mo>&CenterDot;</mo> <mi>S</mi> <mi>p</mi> <mo>{</mo> <mn>1</mn> <mo>-</mo> <mi>exp</mi> <mo>&lsqb;</mo> <mo>-</mo> <mfrac> <mrow> <mi>S</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </mrow> <mrow> <mi>S</mi> <mi>p</mi> </mrow> </mfrac> <mo>&rsqb;</mo> <mo>}</mo> <mo>,</mo> </mrow>Wherein, E (i) is that i-th of growth cycle crop yield amount productive potentialities, r and Sp under the cycle weather circumstance condition are The mechanism parameter that inherence is grown, S (i) are the total leaf area of the crop of i-th of growth cycle；For growth cycle i, the biomass table of Different Crop organ o accumulations is shown as：<mrow> <msub> <mi>q</mi> <mi>o</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>=</mo> <munder> <mo>&Sigma;</mo> <mi>j</mi> </munder> <munderover> <mo>&Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>j</mi> </munderover> <msub> <mi>P</mi> <mi>o</mi> </msub> <msub> <mi>f</mi> <mi>o</mi> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mfrac> <mrow> <mi>Q</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>-</mo> <mi>j</mi> <mo>+</mo> <mi>k</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> <mrow> <munder> <mo>&Sigma;</mo> <mi>o</mi> </munder> <munderover> <mo>&Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>i</mi> <mo>-</mo> <mi>j</mi> <mo>+</mo> <mi>k</mi> <mo>-</mo> <mn>1</mn> </mrow> </munderover> <msub> <mi>N</mi> <mi>o</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>-</mo> <mi>j</mi> <mo>+</mo> <mi>k</mi> <mo>,</mo> <mi>k</mi> <mo>)</mo> </mrow> <msub> <mi>P</mi> <mi>o</mi> </msub> <msub> <mi>f</mi> <mi>o</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>-</mo> <mi>j</mi> <mo>+</mo> <mi>k</mi> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>,</mo> </mrow> Wherein, PoFor the relative of crop organ o The strong parameter in storehouse, foFor crop organ's o spread functions, Q () is the biomass increment of different growth periods, NoThat () is organ o Number, j are the organ o growth age, and k is subscript.
- 2. according to the method for claim 1, it is characterised in that the environmental data includes but is not limited to meteorological data.
- 3. according to the method for claim 1, it is characterised in that in the step 3, recognize to obtain base using gradient descent method In the model parameter θ of the crop yield amount productive potentialities submodel of data-drivendWith the crop yield quantum of knowledge based driving The model parameter θ of modelk。
- 4. according to the method for claim 1, it is characterised in that based on radial basis function neural network RBFN structures based on number According to the crop yield amount productive potentialities submodel of driving.
- 5. according to the method for claim 4, it is characterised in that build what is obtained based on radial basis function neural network RBFN Crop yield amount productive potentialities submodel based on data-driven is represented by：E=fd(x,θd)=Φ (x) θd,Wherein, θdFor RBFN weighting parameter, Φ (x)=[φ1(x),φ2(x),…,φh(x)] it is RBF, and
- 6. according to the method for claim 1, it is characterised in that the crop organ o includes but is not limited to blade b, internode e With fruit f.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410371605.1A CN104134003B (en) | 2014-07-30 | 2014-07-30 | The crop yield amount Forecasting Methodology that knowledge based drives jointly with data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410371605.1A CN104134003B (en) | 2014-07-30 | 2014-07-30 | The crop yield amount Forecasting Methodology that knowledge based drives jointly with data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104134003A CN104134003A (en) | 2014-11-05 |
| CN104134003B true CN104134003B (en) | 2018-01-30 |
Family
ID=51806678
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410371605.1A Active CN104134003B (en) | 2014-07-30 | 2014-07-30 | The crop yield amount Forecasting Methodology that knowledge based drives jointly with data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104134003B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110495408A (en) * | 2019-09-20 | 2019-11-26 | 重庆工商大学 | The fishes and shrimps ginseng cultivation decision system and device driven jointly based on data and knowledge |
Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104656451B (en) * | 2015-01-21 | 2017-06-09 | 中国科学院自动化研究所 | A kind of closed system envirment factor optimization regulating method based on crop modeling |
| JP2016146046A (en) * | 2015-02-06 | 2016-08-12 | 株式会社Ｊｓｏｌ | Predictor, prediction method and program |
| CN104732299A (en) * | 2015-04-03 | 2015-06-24 | 中国农业科学院农业信息研究所 | Maize yield combined prediction system and method |
| CN105494033B (en) * | 2015-10-30 | 2018-06-01 | 青岛智能产业技术研究院 | A kind of intelligent water-saving irrigation method based on crop demand |
| CN105513096A (en) * | 2015-11-18 | 2016-04-20 | 青岛农业大学 | Method for estimating biomass of winter wheat |
| CN106570768B (en) * | 2016-11-02 | 2019-08-27 | 江苏大学 | A kind of characterizing method that greenhouse tomato library is strong |
| CN107368687B (en) * | 2017-07-25 | 2020-01-21 | 中国农业科学院农业信息研究所 | Optimal selection method and device for meteorological unit production model |
| JP7163881B2 (en) * | 2019-08-02 | 2022-11-01 | トヨタ自動車株式会社 | Crop Characteristics Prediction System, Crop Characteristics Prediction Method, and Crop Characteristics Prediction Program |
| CN113011683A (en) * | 2021-04-26 | 2021-06-22 | 中国科学院地理科学与资源研究所 | Crop yield estimation method and system based on corrected crop model |
| CN113379155B (en) * | 2021-06-29 | 2022-08-12 | 哈尔滨工业大学 | Method for estimating development suitability of biomass energy based on village and town population prediction |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101480143A (en) * | 2009-01-21 | 2009-07-15 | 华中科技大学 | Method for predicating single yield of crops in irrigated area |
| CN103518516A (en) * | 2013-10-21 | 2014-01-22 | 贵州省辣椒研究所 | Method for measuring field yield of chilies |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FI20085764L (en) * | 2008-08-08 | 2010-03-26 | Ravintoraisio Oy | A method for monitoring the environmental effects of crop production |
-
2014
- 2014-07-30 CN CN201410371605.1A patent/CN104134003B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101480143A (en) * | 2009-01-21 | 2009-07-15 | 华中科技大学 | Method for predicating single yield of crops in irrigated area |
| CN103518516A (en) * | 2013-10-21 | 2014-01-22 | 贵州省辣椒研究所 | Method for measuring field yield of chilies |
Non-Patent Citations (4)
| Title |
|---|
| 不同植物密度番茄生长行为的结构功能模型模拟;杨丽丽 等;《农业机械学报》;20091031;第40卷(第10期);第156-160页 * |
| 加工番茄产量组合预测模型研究;韩泽群 等;《中国农学通报》;20130131;第29卷(第3期);全文 * |
| 奇台县粮食生产潜力变化过程分析;卢文娟 等;《干旱区资源与环境》;20110228;第25卷(第2期);全文 * |
| 结构-功能模型在林木竞争研究中的应用;刁军 等;《世界林业研究》;20130430;第26卷(第2期);全文 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110495408A (en) * | 2019-09-20 | 2019-11-26 | 重庆工商大学 | The fishes and shrimps ginseng cultivation decision system and device driven jointly based on data and knowledge |
| CN110495408B (en) * | 2019-09-20 | 2021-08-17 | 重庆工商大学 | Fish, shrimp and ginseng breeding decision system and device based on common driving of data and knowledge |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104134003A (en) | 2014-11-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104134003B (en) | The crop yield amount Forecasting Methodology that knowledge based drives jointly with data | |
| Mahmoud et al. | An advanced approach for optimal wind power generation prediction intervals by using self-adaptive evolutionary extreme learning machine | |
| CN102736596B (en) | Multi-scale greenhouse environment control system based on crop information fusion | |
| CN110084367A (en) | A kind of Forecast of Soil Moisture Content method based on LSTM deep learning model | |
| CN101480143B (en) | Method for predicating single yield of crops in irrigated area | |
| CN110084417A (en) | A kind of strawberry greenhouse environment parameter intelligent monitor system based on GRNN neural network | |
| CN105494033B (en) | A kind of intelligent water-saving irrigation method based on crop demand | |
| CN105678629A (en) | Planting industry problem solution system based on internet of things | |
| Lin et al. | Intelligent greenhouse system based on remote sensing images and machine learning promotes the efficiency of agricultural economic growth | |
| CN107466816A (en) | A kind of irrigation method based on dynamic multilayer extreme learning machine | |
| Priya et al. | An IoT based gradient descent approach for precision crop suggestion using MLP | |
| Jiayu et al. | Application of intelligence information fusion technology in agriculture monitoring and early-warning research | |
| Liu et al. | Prediction of soil moisture based on extreme learning machine for an apple orchard | |
| CN107423850A (en) | Region corn maturity period Forecasting Methodology based on time series LAI curve integral areas | |
| CN110119086A (en) | A kind of tomato greenhouse environmental parameter intelligent monitoring device based on ANFIS neural network | |
| Wen et al. | Application of ARIMA and SVM mixed model in agricultural management under the background of intellectual agriculture | |
| CN105184400A (en) | Tobacco field soil moisture prediction method | |
| Cao et al. | igrow: A smart agriculture solution to autonomous greenhouse control | |
| Wang et al. | Cotton growth model under drip irrigation with film mulching: A case study of Xinjiang, China | |
| CN115529987A (en) | Air port regulating and controlling method, device, equipment and storage medium for crop facility | |
| CN114859734A (en) | Greenhouse environment parameter optimization decision method based on improved SAC algorithm | |
| Zhang et al. | Responses and sensitivities of maize phenology to climate change from 1971 to 2020 in Henan Province, China | |
| Zhangzhong et al. | Development of an evapotranspiration estimation method for lettuce via mobile phones using machine vision: Proof of concept | |
| Mezouari et al. | A Hadoop based framework for soil parameters prediction | |
| CN117236650A (en) | Intelligent fluid dynamic adjustment method based on deep learning |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |