CN104134003A - Crop single yield prediction method based on knowledge and data common drive - Google Patents
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Abstract
The invention discloses a crop single yield prediction method based on knowledge and data common drive. The method includes the following steps that a crop single yield production potential submodel based on data drive is used, and single yield production potential E of crops is obtained according to input environment data; by means of a crop model based on a process, a crop single yield submodel based on knowledge drive is built, and then the single yield y of the crops is obtained according to the single yield production potential E of the crops and internal mechanism parameters of the crops; by means of area historical environment data and the single yield y of the crops, model parameters theta d and theta k are obtained through identification so that the single yield of the crops in a certain area is predicted according to meteorological data of the area in the future. By means of the method, historical environment data and a crop knowledge model can be used to the largest extent, the model parameters are estimated systematically, potential analysis is more accurate, and the method is easy to use and reliable and assists in greenhouse crop production, environment conditioning and cultivation management.
Description
Technical field
The invention belongs to data processing method and general botany technical field, relate in particular to a kind of based on knowledge and the common crop yield amount Forecasting Methodology driving of data.
Background technology
Chamber crop production, environment conditioning and cultivation management need reliable crop yield amount Forecasting Methodology.As a rule, crop yield amount Forecasting Methodology, according to whether comprising the knowledge relevant to crop growth rule, can be divided into crop yield amount Forecasting Methodology and the crop yield amount Forecasting Methodology based on knowledge driving based on data-driven.
Crop yield amount Forecasting Methodology based on data-driven is considered as black box by the complicated growth behavior of crop, growth course is not done to consider, the structure of its method place one's entire reliance upon history environment data and crop yield amount data (not comprising the knowledge that any crop is relevant), as adopted the crop yield amount Forecasting Methodology based on artificial neural network.This method can be processed by data mode of learning the nonlinear relationship of mechanism the unknown, and can reach higher precision of prediction to the data under certain environment growth.But the method can not the existing crop knowledge of maximum using removes to improve the precision of prediction of crop yield amount, and the complexity of model promotes rapidly when increasing output variable.
Be different from the method based on data-driven, the crop yield amount Forecasting Methodology driving based on knowledge is the model of setting up based on crop growth rule, the dynamic process such as biomass generation, distribution, leaf area formation that comprises crop, as the plant function structural model (GreenLab) coming based on cooperation beween China and France research and development, this model is a kind of general plant growth model, be successfully applied to the industrial crops such as corn, wheat, cucumber, tomato, and can have assisted chamber crop production, environment conditioning and cultivation management.But this model can not maximum using environmental data removes to improve the precision of prediction of crop yield amount.
Be different from traditional crop yield amount Forecasting Methodology, for maximum using crop knowledge and environmental data improve crop yield amount precision of prediction, the present invention proposes a kind of based on knowledge and the common crop yield amount Forecasting Methodology driving of data, can assist chamber crop production, environment conditioning and cultivation management.
Summary of the invention
The object of the present invention is to provide a kind of reliable crop yield amount Forecasting Methodology, and make the method can the existing crop knowledge of maximum using and environmental data, improve crop yield amount precision of prediction, and can assist chamber crop production, environment conditioning and cultivation management.
For achieving the above object, the invention provides a kind of crop yield amount Forecasting Methodology based on knowledge and the common driving of data, the method comprises the following steps:
Step 1, utilizes the crop yield amount production potential submodel based on data-driven, obtains the single rate production potential E of crop according to the environmental data of input;
Step 2, utilizes the crop modeling based on process, builds the crop yield quantum model obtaining based on knowledge driving, then according to the mechanism parameter of the single rate production potential E of described crop and crop inherence, obtains the single rate y of crop;
Step 3, utilizes the single rate y of the crop that region history environment data and described step 2 obtain, and identification obtains the model parameter θ of the described crop yield amount production potential submodel based on data-driven
dmodel parameter θ with the described crop yield quantum model driving based on knowledge
k, the single rate of this area crops is predicted according to the weather data in following certain region.
What the present invention proposed takes full advantage of based on knowledge and the common crop yield amount Forecasting Methodology driving of data method and the advantage separately of the method based on data-driven driving based on knowledge, can maximum using crop knowledge and environmental data, improve the reliability of crop yield amount precision of prediction.Difference with the prior art of the present invention is mainly reflected in the present invention and adopts the method based on data-driven, the crop yield amount production potential submodel of structure based on data-driven, the crop modeling of employing based on process, the crop yield quantum model that structure drives based on knowledge, two submodels adopt the mode of series connection to be coupled.Therefore, the present invention more flexibly applicating history environmental data builds the crop yield amount production potential model based on data-driven, and in conjunction with crop growth rule (crop knowledge), from mechanism angle, crop yield amount is predicted.Experiment shows, the advantage of the two that the inventive method is integrated, and there is higher crop yield amount precision of prediction.
The present invention utilizes region history environment data and crop yield amount data, picks out model parameter, for the single rate prediction of following this area crops.The present invention can maximum using history environment data and crop knowledge model, making model parameter obtain systematization estimates, Potentials is more accurate, is easy to use, and this is also for chamber crop production, environment conditioning and cultivation management provide method more reliably.
Accompanying drawing explanation
Fig. 1 is the theory diagram that the present invention is based on knowledge and the common crop yield amount Forecasting Methodology driving of data.
Fig. 2 is the block diagram based on the common crop yield amount Forecasting Methodology driving of knowledge and data according to an embodiment of the invention.
Embodiment
For making the object, technical solutions and advantages of the present invention clearer, below in conjunction with specific embodiment, and with reference to accompanying drawing, the present invention is described in more detail.
Fig. 1 is the theory diagram that the present invention is based on knowledge and the common crop yield amount Forecasting Methodology driving of data, as shown in Figure 1, said method comprising the steps of:
Step 1, utilizes the crop yield amount production potential submodel based on data-driven, obtains the single rate production potential E of crop according to the environmental data of input;
This step has reflected the impact of climatic environment for crop yield amount.
Wherein, described environmental data includes but not limited to temperature, illumination, CO
2the weather data such as concentration, relative humidity.
Wherein, the described crop yield amount production potential submodel based on data-driven is that the method structure based on data-driven obtains, and it is expressed as:
E＝f
d(x,θ
d)，
Wherein, f
d() represents the method based on data-driven, and x is the environmental data of input, and E is the production potential of crop yield amount under this environmental baseline, θ
dparameter for submodel E.
Step 2, utilizes the crop modeling based on process, builds the crop yield quantum model obtaining based on knowledge driving, then according to the mechanism parameter of the single rate production potential E of described crop and crop inherence, obtains the single rate y of crop;
Wherein, the described crop yield quantum model driving based on knowledge is expressed as:
y＝f
k(E,θ
k)，
Wherein, f
k() represents the crop modeling based on process, θ
kfor the parameter of submodel y, i.e. the mechanism parameter of crop inherence.
Described step 1 and step 2 also can be thought the crop yield amount production potential submodel based on data-driven and the crop yield quantum model series coupled based on knowledge driving, obtain based on knowledge and the common crop yield amount forecast model driving of data, for this model, input environment data, can obtain the single rate of crop.
Step 3, utilizes the single rate y of the crop that region history environment data and described step 2 obtain, and identification obtains the model parameter θ of the described crop yield amount production potential submodel based on data-driven
dmodel parameter θ with the described crop yield quantum model driving based on knowledge
k, for the single rate of this area crops being predicted according to the weather data in following certain region.
In an embodiment of the present invention, utilize gradient descent method identification to obtain based on knowledge and the common crop yield amount prediction model parameters θ driving of data
dand θ
k.
Fig. 2 is the block diagram based on the common crop yield amount Forecasting Methodology driving of knowledge and data according to an embodiment of the invention, as shown in Figure 2, in an embodiment of the present invention, based on radial basis function neural network (RBFN), build the crop yield amount production potential submodel based on data-driven, based on plant function structural model (GreenLab) or garden crop universal model (HortiSim), build the crop yield quantum model driving based on knowledge.
Based on radial basis function neural network (RBFN), building the crop yield amount production potential submodel based on data-driven obtaining can be expressed as:
E＝f
d(x,θ
d)＝Φ(x)θ
d，
Wherein, θ
dfor the weighting parameter of RBFN, Φ (x)=[φ
1(x), φ
2(x) ..., φ
h(x)] be radial basis function, and
In plant function structural model (GreenLab), the biomass increment Q (i) of i growth cycle is expressed as:
Wherein, E (i) is i growth cycle crop yield amount production potential under this cycle weather environment condition, r and Sp are inherent mechanism parameter of growing, and usually according to actual production data, estimate to set the total leaf area of crop that S (i) is i growth cycle.
For growth cycle i, the biomass table of Different Crop organ o accumulation is shown:
Wherein, P
ofor the strong parameter in relative storehouse of crop organ o, usually according to crop actual production data, estimate to set N
ofor the number of crop organ o, f
ofor crop organ o spread function, the biomass increment that Q () is different growth periods, N
o() is the number of organ o, and j is the growth age of organ o, and k is subscript, and crop organ o generally includes blade b, internode e and fruit f.GreenLab model belongs to common plant function structural model, as space is limited, about the more details of GreenLab model, repeats no more.
It should be noted that, for different industrial crops, the calculating object of crop yield amount is slightly different.Such as for tomato crop, what we paid close attention to is the output of tamato fruit, and its single rate computing formula is:
For romaine lettuce, what we mainly paid close attention to is the blade of romaine lettuce, and its single rate computing formula is:
Above-described specific embodiment; object of the present invention, technical scheme and beneficial effect are further described; institute is understood that; the foregoing is only specific embodiments of the invention; be not limited to the present invention; within the spirit and principles in the present invention all, any modification of making, be equal to replacement, improvement etc., within all should being included in protection scope of the present invention.
Claims (10)
1. based on knowledge and the common crop yield amount Forecasting Methodology driving of data, it is characterized in that, the method comprises the following steps:
Step 1, utilizes the crop yield amount production potential submodel based on data-driven, obtains the single rate production potential E of crop according to the environmental data of input;
Step 2, utilizes the crop modeling based on process, builds the crop yield quantum model obtaining based on knowledge driving, then according to the mechanism parameter of the single rate production potential E of described crop and crop inherence, obtains the single rate y of crop;
Step 3, utilizes the single rate y of the crop that region history environment data and described step 2 obtain, and identification obtains the model parameter θ of the described crop yield amount production potential submodel based on data-driven
dmodel parameter θ with the described crop yield quantum model driving based on knowledge
k, the single rate of this area crops is predicted according to the weather data in following certain region.
2. method according to claim 1, is characterized in that, described environmental data includes but not limited to weather data.
3. method according to claim 1, is characterized in that, the described crop yield amount production potential submodel based on data-driven is that the method structure based on data-driven obtains, and it is expressed as:
E＝f
d(x,θ
d)，
Wherein, f
d() represents the method based on data-driven, and x is the environmental data of input, and E is the production potential of crop yield amount under corresponding environmental baseline, θ
dparameter for submodel E.
4. method according to claim 1, is characterized in that, the described crop yield quantum model driving based on knowledge is expressed as:
y＝f
k(E,θ
k)，
Wherein, f
k() represents the crop modeling based on process, θ
kfor the parameter of submodel y, i.e. the mechanism parameter of crop inherence.
5. method according to claim 1, is characterized in that, in described step 3, utilizes gradient descent method identification to obtain the model parameter θ of the crop yield amount production potential submodel based on data-driven
dmodel parameter θ with the crop yield quantum model driving based on knowledge
k.
6. method according to claim 1, is characterized in that, based on radial basis function neural network (RBFN), builds the crop yield amount production potential submodel based on data-driven.
7. method according to claim 6, is characterized in that, based on radial basis function neural network (RBFN), building the crop yield amount production potential submodel based on data-driven obtaining can be expressed as:
E＝f
d(x,θ
d)＝Φ(x)θ
d，
Wherein, θ
dfor the weighting parameter of RBFN, Φ (x)=[φ
1(x), φ
2(x) ..., φ
h(x)] be radial basis function, and
8. method according to claim 1, is characterized in that, based on plant function structural model (GreenLab) or garden crop universal model (HortiSim), builds the crop yield quantum model driving based on knowledge.
9. method according to claim 8, is characterized in that, in described plant function structural model, the biomass increment Q (i) of i growth cycle is expressed as:
Wherein, E (i) is i growth cycle crop yield amount production potential under this cycle weather environment condition, and r and Sp are inherent mechanism parameter of growing, the total leaf area of crop that S (i) is i growth cycle;
For growth cycle i, the biomass table of Different Crop organ o accumulation is shown:
Wherein, P
ofor the strong parameter in relative storehouse of crop organ o, N
ofor the number of crop organ o, f
ofor crop organ o spread function, the biomass increment that Q () is different growth periods, N
o() is the number of organ o, and j is the growth age of organ o, and k is subscript.
10. method according to claim 9, is characterized in that, described crop organ o includes but not limited to blade b, internode e and fruit f.
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| CN112308284A (en) * | 2019-08-02 | 2021-02-02 | 丰田自动车株式会社 | Crop characteristic prediction system and method, and computer readable storage medium |
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| CN104656451A (en) * | 2015-01-21 | 2015-05-27 | 中国科学院自动化研究所 | Optimal regulating and controlling method based on crop model for environmental factors in closed system |
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| 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 |
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| CN105513096A (en) * | 2015-11-18 | 2016-04-20 | 青岛农业大学 | Method for estimating biomass of winter wheat |
| CN106570768A (en) * | 2016-11-02 | 2017-04-19 | 江苏大学 | Representation method for greenhouse tomato sink strength |
| CN106570768B (en) * | 2016-11-02 | 2019-08-27 | 江苏大学 | A kind of characterizing method that greenhouse tomato library is strong |
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| CN112308284A (en) * | 2019-08-02 | 2021-02-02 | 丰田自动车株式会社 | Crop characteristic prediction system and method, and computer readable storage medium |
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