CN106295865A - A kind of Forecasting Methodology of rice yield - Google Patents
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- 235000007164 Oryza sativa Nutrition 0.000 title claims abstract description 176
- 235000009566 rice Nutrition 0.000 title claims abstract description 152
- 238000000034 method Methods 0.000 title claims abstract description 42
- 240000007594 Oryza sativa Species 0.000 title claims description 134
- 238000012360 testing method Methods 0.000 claims abstract description 8
- 238000004519 manufacturing process Methods 0.000 claims abstract description 6
- 241000209094 Oryza Species 0.000 claims abstract 43
- 230000012010 growth Effects 0.000 claims description 26
- 230000003203 everyday effect Effects 0.000 claims description 11
- 230000035800 maturation Effects 0.000 claims description 11
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 5
- 230000001419 dependent effect Effects 0.000 claims description 4
- 238000010606 normalization Methods 0.000 claims description 4
- 240000008467 Oryza sativa Japonica Group Species 0.000 abstract description 17
- 238000000611 regression analysis Methods 0.000 abstract description 4
- 101001095088 Homo sapiens Melanoma antigen preferentially expressed in tumors Proteins 0.000 description 8
- 102100037020 Melanoma antigen preferentially expressed in tumors Human genes 0.000 description 8
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- 238000012417 linear regression Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000008635 plant growth Effects 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
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Abstract
The invention discloses the Forecasting Methodology of a kind of rice yield, optional test community；Obtain the rice leaf normalized differential vegetation index in experimental plot, and ask for adding and value of all of rice leaf normalized differential vegetation index, obtain NDVI and add and be worth；Obtain the rice leaf photochemical reflectance index in experimental plot, and ask for adding and value of all of rice leaf photochemical reflectance index, obtain PRI and add and be worth；Obtain the rice yield in experimental plot；Utilizing the multiple regression analysis method of SPSS software, analyzing rice yield and NDVI add and value, PRI add the linear relationship between value, it is thus achieved that Rice Yield Prediction model；According to Rice Yield Prediction model prediction rice yield.The method of the present invention can monitor japonica rice blade NDVI and the PRI relation in different growing Yu japonica rice change of production timely, the businessization being capable of japonica rice the yield by estimation is run, efficiently, quickly, accurate estimation rice yield, japonica rice yield can be predicted accurately and japonica rice growing way is effectively followed the tracks of.
Description
Technical field
The invention belongs to plant growth information monitoring technical field, be specifically related to the Forecasting Methodology of a kind of rice yield.
Background technology
In rice research field, the vegetation index obtained according to the spectral signature computing of plant sensitive band, it is possible to quickly,
Lossless, quantitatively characterize paddy growth situation, monitoring Oryza sativa L. growing way and prediction rice yield.Since normalized differential vegetation index
(Normalized difference vegetation index, NDVI) since 1973 are proposed first by scientist, just
With stability by expert and the extensive concern of scholar, since becoming nearly more than 20 years, one of most vegetation index, quilt are used
More it is used for research crop growing state and Granule weight；Scientific research finds, blade is positioned at the reflection at 531nm and 570nm
Rate change can send out the efficiency of light energy utilization (Light use efficiency, LUE) mirroring blade, well based on the two ripple
The reflectance of section constructs photochemical reflectance index (Photochemical reflectance index, PRI), and successfully
Establish the relation of PRI and LUE；LUE is an important Ecological concepts, is again estimation net primary productivity (net primary
Productivity, NPP) key variables, NPP and crop biomass cumulant have direct relation, pass through harvest index
Just can get crop yield, be also light use efficiency model and the regional scale pass with Remote sensing parameters model monitoring vegetation productivity
Bond parameter, is studied monitoring crop growth and Granule weight by more being used for.
In reality the yield by estimation application, Oryza sativa L. the yield by estimation mostly realizes with high-altitude remote sensing for data source, but, because of by underlay
The impact such as face, atmospheric effect, phenological calendar is bigger so that the yield by estimation effect tends not to produce a desired effect.By crop leaf light
Spectrum then can slacken or eliminate these interference factors, but utilizes the agricultural output assessment of Spectra of The Leaves method the rarest.Accordingly, it would be desirable to
Develop a kind of new can the method for accurate estimation rice yield.
Summary of the invention
The Forecasting Methodology of a kind of rice yield that the present invention provides, NDVI and PRI surveyed by vegetation index measuring instrument
Data, and utilize the method for statistical analysis to obtain Rice Yield Prediction model, and utilize this Oryza sativa L. model accurate estimation Oryza sativa L. to produce
Amount.
It is an object of the invention to provide the Forecasting Methodology of a kind of rice yield, comprise the following steps:
Optional test community；
In paddy growth natural law, obtain the rice leaf normalized differential vegetation index in experimental plot every day, and ask for owning
The adding and value of rice leaf normalized differential vegetation index, obtain NDVI and add and be worth；
In paddy growth natural law, obtain the rice leaf photochemical reflectance index in experimental plot every day, and ask for owning
The adding and value of rice leaf photochemical reflectance index, obtain PRI and add and be worth；
Obtain the rice yield in experimental plot；
Add with NDVI simultaneously and be worth and PRI adds and is worth as independent variable, with rice yield as dependent variable, utilize SPSS software
Multiple regression analysis method, analyzing rice yield and NDVI add and value, PRI add the linear relationship between value, it is thus achieved that rice yield
Forecast model；
According to Rice Yield Prediction model prediction rice yield；
Described Rice Yield Prediction model is as follows:
Wherein, y is rice yield；N is paddy growth natural law, xiFor rice leaf normalized differential vegetation index；ziFor Rice Leaf
Sheet photochemical reflectance index；β1Span be 1.499～5.292；β2Span be 3.063～4.391；β3Take
Value scope is-5.205～-2.783.
Preferably, when Oryza sativa L. is in tillering stage, Rice Yield Prediction model is:
Preferably, when Oryza sativa L. is in jointing-booting stage, Rice Yield Prediction model is:
Preferably, when Oryza sativa L. is in Filling stage, Rice Yield Prediction model is:
Preferably, when Oryza sativa L. is in the period of maturation, Rice Yield Prediction model is:
Preferably, in the Forecasting Methodology of above-mentioned rice yield, described rice leaf normalized differential vegetation index is according to following step
Rapid acquisition:
Choosing two sampled points in experimental plot, in described sampled point, all blades are as sample objects, obtain each
Single blade normalized differential vegetation index of blade；
Calculate in described sampled point the meansigma methods of vaned single blade normalized differential vegetation index, it is thus achieved that each sampled point
Single-point normalized differential vegetation index；
Calculate the meansigma methods of the single-point normalized differential vegetation index of all sampled points, it is thus achieved that normalization vegetation refers to rice leaf
Number.
Preferably, in the Forecasting Methodology of above-mentioned rice yield, described rice leaf photochemical reflectance index is according to following step
Rapid acquisition:
Choosing two sampled points in experimental plot, in described sampled point, all blades are as sample objects, obtain each
Single blade photochemical reflectance index of blade；
Calculate in described sampled point the meansigma methods of vaned single blade photochemical reflectance index, it is thus achieved that each sampled point
Single-point photochemical reflectance index；
Calculate the meansigma methods of the single-point photochemical reflectance index of all sampled points, it is thus achieved that photochemistry vegetation refers to rice leaf
Number.
Preferably, in the Forecasting Methodology of above-mentioned rice yield, described rice leaf normalized differential vegetation index and rice leaf
The acquisition time of photochemical reflectance index is the 10:00-14:00 of every day.
The Forecasting Methodology of the rice yield that the present invention provides, NDVI and the PRI numerical value surveyed by vegetation index measuring instrument,
And utilizing the method for statistical analysis to obtain Rice Yield Prediction model, it is possible to monitoring japonica rice blade NDVI and PRI be not timely
Relation with period of duration Yu japonica rice change of production, it is possible to the businessization realizing japonica rice the yield by estimation is run, efficient, quick, accurate estimation
Rice yield, can predict accurately japonica rice yield and effectively follow the tracks of japonica rice growing way.
Detailed description of the invention
Below in conjunction with specific embodiment, the present invention is described in detail, but should not be construed as the restriction of the present invention.
The Forecasting Methodology of a kind of rice yield that the present invention provides, comprises the following steps:
Optional test community；
In paddy growth natural law, obtain the rice leaf normalized differential vegetation index in experimental plot every day, and ask for owning
The adding and value of rice leaf normalized differential vegetation index, obtain NDVI and add and be worth；
In paddy growth natural law, obtain the rice leaf photochemical reflectance index in experimental plot every day, and ask for owning
The adding and value of rice leaf photochemical reflectance index, obtain PRI and add and be worth；
Obtain the rice yield in experimental plot；
Add with NDVI simultaneously and be worth and PRI adds and is worth as independent variable, with rice yield as dependent variable, utilize SPSS software
Multiple regression analysis method, analyzing rice yield and NDVI add and value, PRI add the linear relationship between value, it is thus achieved that rice yield
Forecast model；
According to Rice Yield Prediction model prediction rice yield；
Described Rice Yield Prediction model is as follows:
Wherein, y is rice yield；N is paddy growth natural law, xiFor rice leaf normalized differential vegetation index；ziFor Rice Leaf
Sheet photochemical reflectance index；β1Span be 1.499～5.292；β2Span be 3.063～4.391；β3Take
Value scope is-5.205～-2.783.
It should be noted that described paddy growth natural law is to count from the first of Rice Cropping day, growth natural law increases by sky
Adding, the whole growth cycle of Oryza sativa L. includes tillering stage, jointing-booting stage, Filling stage, period of maturation.When rice yield estimated by needs
Time, first calculate the growth natural law of current Oryza sativa L., judge rice plant of tillering stage, jointing-booting stage, Filling stage by growth natural law
Or in the period of maturation, then at the Rice Yield Prediction model corresponding according to each period, the yield of Oryza sativa L. is accurately estimated.
The when of it should be noted that build Rice Yield Prediction model, β1、β2And β3Being unknown number, other parameters are
Knowing number, according to the data of detection, linear regression modeling obtains the model formation of Oryza sativa L. different times, determines the β in formula1、β2With
β3, the when of applying this model formation, y has reformed into unknown number, and other parameter is datum, and anti-reckoning obtains Oryza sativa L. product
Amount.
Embodiment 1
Providing below a kind of test area is the Northeast, and test material is the Forecasting Methodology of the yield of Shen rice 47 kind,
Specifically implement according to following steps:
Step 1, optional test community, and in experimental plot, choose two sampled points, and two sampled points divide uniformly
Being distributed in described experimental plot, each sampled point is the square region of 1m × 1m.
Step 2, in paddy growth natural law, every day, 12:00 obtained the rice leaf normalized differential vegetation index in experimental plot,
And ask for adding and value of all of rice leaf normalized differential vegetation index, obtain NDVI and add and be worth；
Wherein, described rice leaf normalized differential vegetation index obtains according to following steps:
In described sampled point, all blades are as sample objects, obtain single blade normalized differential vegetation index of each blade；
Calculate in described sampled point the meansigma methods of vaned single blade normalized differential vegetation index, it is thus achieved that each sampled point
Single-point normalized differential vegetation index；
Calculate the meansigma methods of the single-point normalized differential vegetation index of all sampled points, it is thus achieved that normalization vegetation refers to rice leaf
Number.
Step 3, in paddy growth natural law, every day, 14:00 obtained the rice leaf photochemical reflectance index in experimental plot,
And ask for adding and value of all of rice leaf photochemical reflectance index, obtain PRI and add and be worth.
Wherein, described rice leaf photochemical reflectance index obtains according to following steps:
In described sampled point, all blades are as sample objects, obtain single blade photochemical reflectance index of each blade；
Calculate in described sampled point the meansigma methods of vaned single blade photochemical reflectance index, it is thus achieved that each sampled point
Single-point photochemical reflectance index；
Calculate the meansigma methods of the single-point photochemical reflectance index of all sampled points, it is thus achieved that photochemistry vegetation refers to rice leaf
Number.
Step 4, when Oryza sativa L. maturation, obtains the rice yield in experimental plot.
Step 5, adds with NDVI simultaneously and is worth and PRI adds and is worth as independent variable, with rice yield as dependent variable, utilize SPSS
The multiple regression analysis method of software, analyzing rice yield and NDVI add and value, PRI add the linear relationship between value, it is thus achieved that water
Rice Production Forecast Models.
Described Rice Yield Prediction model is as follows:
Wherein, y is rice yield；N is paddy growth natural law, xiFor rice leaf normalized differential vegetation index；ziFor Rice Leaf
Sheet photochemical reflectance index；β1Span be 1.499～5.292；β2Span be 3.063～4.391；β3Take
Value scope is-5.205～-2.783.
It should be noted that described rice leaf normalized differential vegetation index and the collection of rice leaf photochemical reflectance index
The 10:00-14:00 that time is every day is the most permissible, and such as 10:00,11:00,12:00,13:00 and 14:00 etc. are the most permissible, by
The most identical with embodiment 1 with obtained result in the step of construction method, Gu the most not to acquisition time 10:00,11:
00, the step of 12:00,13:00 and 14:00 repeats.
Step 6, according to Rice Yield Prediction model prediction rice yield.
The method using embodiment 1, is analyzed the different times of paddy growth, obtains following result:
(1) when Oryza sativa L. is in tillering stage, Rice Yield Prediction model:
Individually add with NDVI and be worth as variable, individually with PRI add and be worth as variable, and while add with NDVI and be worth, PRI adds
It is variable with value, carries out models fitting, obtain the result shown in table 1.
With coefficient of determination (coeffcient of determination, R2) the forecast model goodness of fit is tested,
Coefficient of determination the highest explanation japonica rice blade vegetation index the yield by estimation effect is the best；With precision index: average absolute percent error (mean
Absolute percentage error, MAPE), MAPE be by NDVI is added and is worth and PRI add and be worth substitute into regression equation
The degree of deviation between predictive value and the true measurement obtained carrys out the accuracy of judgment models inspection, and its value is the least, represents prediction
Value is closer to actual value.As shown in Table 1, add with NDVI simultaneously and be worth, PRI adds and is worth as variable, carrying out models fitting, obtain
Tillering stage Rice Yield Prediction modelR2The highest, MAPE is minimum, for
This optimum prediction in period model.
Table 1 Rice Yield Prediction in tillering stage model
Note: y is rice yield, and n is paddy growth natural law, xiFor rice leaf normalized differential vegetation index；ziFor rice leaf
Photochemical reflectance index.
(2) when Oryza sativa L. is in jointing-booting stage, Rice Yield Prediction model:
Individually add with NDVI and be worth as variable, individually with PRI add and be worth as variable, and while add with NDVI and be worth, PRI adds
It is variable with value, carries out models fitting, obtain the result shown in table 2.
Table 2 jointing-booting stage Rice Yield Prediction model
Note: y is rice yield, and n is paddy growth natural law, xiFor rice leaf normalized differential vegetation index；ziFor rice leaf
Photochemical reflectance index.
Add with NDVI simultaneously and be worth, PRI adds and is worth as variable, carries out models fitting, and the jointing-booting stage Oryza sativa L. obtained produces
Amount forecast modelR2The highest, MAPE% is minimum, optimal for this period
Forecast model.
(3) when Oryza sativa L. is in Filling stage, Rice Yield Prediction model:
Individually add with NDVI and be worth as variable, individually with PRI add and be worth as variable, and while add with NDVI and be worth, PRI adds
It is variable with value, carries out models fitting, obtain the result shown in table 3.Add with NDVI simultaneously and be worth, PRI adds and is worth as variable, enters
Row models fitting, the Filling stage Rice Yield Prediction model obtained
R2The highest, MAPE% is minimum, for this optimum prediction in period model.
Table 3 Filling stage Rice Yield Prediction model
Note: y is rice yield, and n is paddy growth natural law, xiFor rice leaf normalized differential vegetation index；ziFor rice leaf
Photochemical reflectance index.
(4) when Oryza sativa L. is in the period of maturation, Rice Yield Prediction model:
Individually add with NDVI and be worth as variable, individually with PRI add and be worth as variable, and while add with NDVI and be worth, PRI adds
It is variable with value, carries out models fitting, obtain the result shown in table 4.Add with NDVI simultaneously and be worth, PRI adds and is worth as variable, enters
Row models fitting, the period of maturation Rice Yield Prediction model obtained
R2The highest, MAPE% is minimum, for this optimum prediction in period model.
Table 4 period of maturation Rice Yield Prediction model
Note: y is rice yield, and n is paddy growth natural law, xiFor rice leaf normalized differential vegetation index；ziFor rice leaf
Photochemical reflectance index.
In order to check and compare precision and the universality of above-mentioned Rice Yield Prediction model, be utilized respectively NDVI add and be worth,
PRI adds and is worth, yield data, examines the model (add with NDVI simultaneously and be worth, PRI adds and is worth as independent variable) of embodiment 1
Testing and compare, result shows, in the Rice Yield Prediction model of each growth period, and R2More than 0.78, MAPE% is less than 2.42,
The especially R in jointing-booting stage, Filling stage and period of maturation2Being all higher than 0.91, MAPE% is respectively less than 1.48, and each model is described
The linear relationship of formula is good.
It addition, by above-mentioned analysis, with individually add with NDVI and value as variable, independent add with PRI and value is as variable
Compare, add with NDVI simultaneously and be worth, PRI adds and is worth the model formation obtained for variable matching and has high coefficient of determination.By
In its data having considered different leaves NDVI and PRI, considerably increase the precision of prediction of model, thus at forecast sample
Time show higher stability, it is possible to the businessization realizing japonica rice the yield by estimation is run, efficiently, quickly, accurate estimation rice yield,
Japonica rice yield can be predicted accurately and japonica rice growing way is effectively followed the tracks of.
Period of maturation is the critical period that japonica rice ultimate output is formed, and now NDVI adds and be worth with PRI that add and be worth can be the most anti-
Reflect the last growth conditions of japonica rice, more directly can effectively estimate the ultimate output of japonica rice, tillering stage, jointing-booting stage, heading
The model of pustulation period may be used for auxiliary prediction rice yield.
By contrast actual production data and estimated output data, after Oryza sativa L. maturation, the actual rice yield of embodiment 1 is
964.41kg/hm2, utilize the Rice Yield Prediction model of embodiment 1
The rice yield estimated is 978.68kg/hm2, individually with the model of NDVI data matching
The rice yield estimated is 1068.96kg/hm2, individually with PRI modelEstimate
Rice yield is 1062.59kg/hm2, wherein,Closer to reality
Rice yield, be respectively increased 9.36% and 8.70% than the precision of other two models.
Although preferred embodiments of the present invention have been described, but those skilled in the art once know basic creation
Property concept, then can make other change and amendment to these embodiments.So, claims are intended to be construed to include excellent
Select embodiment and fall into all changes and the amendment of the scope of the invention.
Obviously, those skilled in the art can carry out various change and the modification essence without deviating from the present invention to the present invention
God and scope.So, if these amendments of the present invention and modification belong to the scope of the claims in the present invention and equivalent technologies thereof
Within, then the present invention is also intended to comprise these change and modification.
Claims (8)
1. the Forecasting Methodology of a rice yield, it is characterised in that comprise the following steps:
Optional test community；
In paddy growth natural law, obtain the rice leaf normalized differential vegetation index in experimental plot every day, and ask for all of water
Adding and value of rice blade normalized differential vegetation index, obtain NDVI and add and be worth；
In paddy growth natural law, obtain the rice leaf photochemical reflectance index in experimental plot every day, and ask for all of water
Adding and value of rice blade photochemical reflectance index, obtain PRI and add and be worth；
Obtain the rice yield in experimental plot；
Add with NDVI simultaneously and be worth and PRI adds and is worth as independent variable, with rice yield as dependent variable, utilize the polynary of SPSS software
Regression analysis, analyzing rice yield and NDVI add and value, PRI add the linear relationship between value, it is thus achieved that Rice Yield Prediction
Model；
According to Rice Yield Prediction model prediction rice yield；
Described Rice Yield Prediction model is as follows:
Wherein, y is rice yield；N is paddy growth natural law, xiFor rice leaf normalized differential vegetation index；ziFor rice leaf light
Chemistry vegetation index；β1Span be 1.499～5.292；β2Span be 3.063～4.391；β3Value model
Enclose for-5.205～-2.783.
The Forecasting Methodology of rice yield the most according to claim 1, it is characterised in that when Oryza sativa L. is in tillering stage, water
Rice Production Forecast Models is:
The Forecasting Methodology of rice yield the most according to claim 1, it is characterised in that when Oryza sativa L. is in jointing-booting stage
Time, Rice Yield Prediction model is:
The Forecasting Methodology of rice yield the most according to claim 1, it is characterised in that when Oryza sativa L. is in Filling stage
Time, Rice Yield Prediction model is:
The Forecasting Methodology of rice yield the most according to claim 1, it is characterised in that when Oryza sativa L. is in the period of maturation, water
Rice Production Forecast Models is:
The Forecasting Methodology of rice yield the most according to claim 1, it is characterised in that described rice leaf normalization vegetation
Index obtains according to following steps:
Choosing two sampled points in experimental plot, in described sampled point, all blades are as sample objects, obtain each blade
Single blade normalized differential vegetation index；
Calculate in described sampled point the meansigma methods of vaned single blade normalized differential vegetation index, it is thus achieved that the list of each sampled point
Point normalized differential vegetation index；
Calculate the meansigma methods of the single-point normalized differential vegetation index of all sampled points, it is thus achieved that rice leaf normalized differential vegetation index.
The Forecasting Methodology of rice yield the most according to claim 1, it is characterised in that described rice leaf photochemistry vegetation
Index obtains according to following steps:
Choosing two sampled points in experimental plot, in described sampled point, all blades are as sample objects, obtain each blade
Single blade photochemical reflectance index；
Calculate in described sampled point the meansigma methods of vaned single blade photochemical reflectance index, it is thus achieved that the list of each sampled point
Point photochemical reflectance index；
Calculate the meansigma methods of the single-point photochemical reflectance index of all sampled points, it is thus achieved that rice leaf photochemical reflectance index.
The Forecasting Methodology of rice yield the most according to claim 1, it is characterised in that described rice leaf normalization vegetation
The acquisition time of index and rice leaf photochemical reflectance index is the 10:00-14:00 of every day.
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| CN107122739A (en) * | 2017-01-23 | 2017-09-01 | 东北农业大学 | The agricultural output assessment model of VI time-serial positions is reconstructed based on Extreme mathematical modelings |
| CN107122739B (en) * | 2017-01-23 | 2020-07-17 | 东北农业大学 | Crop yield estimation model for reconstructing VI time series curve based on Extreme mathematical model |
| CN109117977A (en) * | 2018-06-29 | 2019-01-01 | 浙江大学 | Rice yield estimation by remote sensing method based on opposite remote sensing variable and fractional yield information |
| CN109117977B (en) * | 2018-06-29 | 2021-06-29 | 浙江大学 | Rice remote sensing yield estimation method based on relative remote sensing variable and relative yield information |
| CN109840855A (en) * | 2019-03-06 | 2019-06-04 | 平顶山学院 | A method of reproduction initial stage prediction tomato whether the underproduction |
| CN110243406A (en) * | 2019-06-21 | 2019-09-17 | 武汉思众空间信息科技有限公司 | Crop Estimation Method, device, electronic equipment and storage medium |
| CN110243406B (en) * | 2019-06-21 | 2021-06-15 | 武汉思众空间信息科技有限公司 | Crop yield estimation method and device, electronic equipment and storage medium |
| CN110736710A (en) * | 2019-11-07 | 2020-01-31 | 航天信德智图（北京）科技有限公司 | corn yield evaluation method based on NDVI time sequence |
| CN110736710B (en) * | 2019-11-07 | 2022-12-09 | 航天信德智图（北京）科技有限公司 | NDVI time sequence-based corn yield evaluation method |
| CN111536930A (en) * | 2020-05-07 | 2020-08-14 | 安徽农业大学 | Method for evaluating yield of double-cropping rice machine-transplanted early rice variety |
| CN112840977A (en) * | 2020-12-31 | 2021-05-28 | 航天信息股份有限公司 | Method and system for predicting wheat yield based on key growth period of wheat |
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