CN111855590A - Remote sensing inversion model and method for rice leaf starch accumulation - Google Patents
Remote sensing inversion model and method for rice leaf starch accumulation Download PDFInfo
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Abstract
The invention provides a rice leaf starch accumulation remote sensing inversion model, which is an extreme random tree model of Python language and further provides model parameters of the extreme random tree model. Also provides a remote sensing inversion method of the starch accumulation of the rice leaves. The remote sensing inversion model of the rice leaf starch accumulation amount can quickly and accurately acquire the information of the rice leaf starch accumulation amount, overcomes the difficulty that the characteristic wave band of the rice leaf starch accumulation amount is difficult to determine due to the spectrum superposition effect caused by complex rice components, greatly improves the precision of the inversion model of the rice leaf starch accumulation amount, and is ingenious in design, simple and convenient to calculate, easy to implement, low in cost and suitable for large-scale popularization and application.
Description
Technical Field
The invention relates to the technical field of agricultural remote sensing, in particular to the technical field of rice leaf starch accumulation measurement, and specifically relates to a rice leaf starch accumulation remote sensing inversion model and a rice leaf starch accumulation remote sensing inversion method.
Background
The rice leaf starch accumulation amount is the total accumulation amount of starch in rice leaves, is an important parameter for quantifying fixed carbon dioxide and synthesized carbohydrate in rice photosynthesis, is an important index of rice physiological conditions and rice growth vigor, and reflects the rice physiological conditions, the rice growth vigor and the water and fertilizer conditions.
The method can monitor the starch accumulation amount of the rice leaves, not only can ensure the yield and quality of rice production, but also can dynamically manage the water and fertilizer control of the rice, and improve the production efficiency of the rice, thereby generating obvious economic and social benefits (Wangxianzhen, Huangjing, plum, and the like, correlation analysis of biochemical parameters and hyperspectral remote sensing characteristic parameters of the rice [ J ] agricultural engineering report, 2003,19(002): 144-. The traditional method for monitoring the starch accumulation amount of the rice leaves mainly adopts a destructive sampling method, needs to be measured indoors, needs to invest a large amount of manpower, wastes time and labor, is poor in timeliness, cannot timely acquire the starch accumulation amount of the rice leaves, and is not beneficial to popularization and application.
In the physiological and biochemical processes of rice, the change of certain specific substances and cell structures in rice plants results in the change of rice reflectance spectra. Therefore, the change of the spectrum can be used for acquiring rice growth information such as rice leaf starch accumulation amount and the like (Zhoudouqin. monitoring of rice nitrogen nutrition and grain quality based on the canopy reflection spectrum [ D ]. Nanjing agriculture university, 2007). Currently, hyperspectrum is used for monitoring the growth state of rice in crop growth monitoring. With the development and popularization of the spectrum technology, the hyperspectral data can quickly and rapidly acquire the information of the starch accumulation amount of rice leaves, and the information becomes a consensus of more and more rice production practitioners and researchers. The most common mode is to use a portable full-waveband spectrometer to obtain growth information such as the starch accumulation amount of rice leaves and select a characteristic waveband capable of reflecting the starch accumulation amount of the leaves to construct an inversion model. In the process of constructing the rice leaf starch accumulation inversion model, the spectral range measured by the full-waveband spectrometer covers 350-1100 nm, but the rice components are complex, the component spectral characteristic wavebands are partially overlapped, the determination of the rice leaf starch accumulation characteristic spectrum is difficult, and meanwhile, the rapid processing of hyperspectral data becomes a technical problem to be solved urgently for estimating the rice leaf starch accumulation based on the hyperspectral data.
Therefore, it is desirable to provide a remote sensing inversion model of rice leaf starch accumulation, which can quickly and accurately acquire the information of rice leaf starch accumulation, overcome the difficulty that the characteristic wave band of rice leaf starch accumulation is difficult to determine due to the spectrum superposition effect caused by complex rice components, and greatly improve the accuracy of the inversion model of rice leaf starch accumulation.
Disclosure of Invention
In order to overcome the defects in the prior art, one object of the present invention is to provide a rice leaf starch accumulation remote sensing inversion model, which can quickly and accurately obtain rice leaf starch accumulation information, overcome the difficulty that the characteristic waveband of rice leaf starch accumulation is difficult to determine due to the spectrum superposition effect caused by complex rice components, greatly improve the accuracy of the rice leaf starch accumulation inversion model, and is suitable for large-scale popularization and application.
The invention also aims to provide a rice leaf starch accumulation remote sensing inversion model which is ingenious in design, simple and convenient to calculate, easy to realize, low in cost and suitable for large-scale popularization and application.
The invention also aims to provide a remote sensing inversion method of the rice leaf starch accumulation, which can quickly and accurately acquire the rice leaf starch accumulation information, overcomes the difficulty that the characteristic wave band of the rice leaf starch accumulation is difficult to determine due to the spectrum superposition effect caused by complex rice components, greatly improves the inversion precision of the rice leaf starch accumulation, and is suitable for large-scale popularization and application.
The invention also aims to provide a remote sensing inversion method of the starch accumulation amount of the rice leaves, which has the advantages of ingenious design, simple and convenient operation and low cost and is suitable for large-scale popularization and application.
In order to achieve the above object, in a first aspect of the present invention, there is provided a rice leaf starch accumulation remote sensing inversion model, which is characterized in that the rice leaf starch accumulation remote sensing inversion model is an extreme random tree model in Python language, and model parameters of the extreme random tree model are as follows: ' min _ input _ gradient ':0.0, ' ccp _ alpha ':0.00047054761925470663, ' max _ depth ':4, ' min _ input _ split ':0.0046954861925470655, ' min _ samples _ leaf ':1, ' min _ samples _ split ':2, ' min _ weight _ fragment _ leaf ':0.010209198720162859, ' split ': range ', ' max _ defects ': auto ', ' criterion ': mae ', and ' max _ leaf _ nodes ': None.
Preferably, the extreme random tree model is trained by using a rice data set, the data set includes canopy reflectances of m sampling points of the rice and logarithmic values of the accumulated amount of leaf starch with the base of 10, the m sampling points are uniformly distributed in a rice planting area, and the canopy reflectivity is the canopy reflectivity of n characteristic bands.
More preferably, m is 37, the n characteristic bands are 2151 characteristic bands, and the 2151 characteristic bands are from 350nm band to 2500nm band.
In a second aspect of the invention, the invention provides a remote sensing inversion method of rice leaf starch accumulation, which is characterized by comprising the following steps:
(1) measuring the canopy reflectance of the rice;
(2) measuring the leaf starch accumulation amount of the rice, and obtaining a logarithm taking 10 as a base of the leaf starch accumulation amount by taking a logarithm taking 10 as a base of the leaf starch accumulation amount:
(3) calculating by using the canopy reflectivity as input data and adopting an extreme random tree model of Python language to obtain an inversion value, and calculating a decision coefficient R according to the inversion value and a logarithmic value of the accumulation amount of the leaf starch with the base of 102Changing the value of a model parameter, R, of the extreme stochastic tree model2The larger the change of the model parameter is, the greater the importance of the model parameter is, the model parameter is arranged from large to small according to the importance to construct a model parameter tuning rank matrix;
(4) training the extreme random tree model by taking the canopy reflectivity as the input data and taking a logarithmic value of the leaf starch accumulation amount with the base of 10 as an output result, and sequentially tuning the model parameters according to the model parameter tuning order matrix to obtain tuning values of the model parameters;
(5) and training the extreme random tree model by taking the canopy reflectivity as the input data and taking a logarithmic value of the leaf starch accumulation amount which is based on 10 as the output result, adopting the tuning value of the model parameter, obtaining a rice leaf starch accumulation amount remote sensing inversion model after the training of the extreme random tree model is finished, storing the rice leaf starch accumulation amount remote sensing inversion model by using a save method, and loading the rice leaf starch accumulation amount remote sensing inversion model for use by using a load method if the rice leaf starch accumulation amount remote sensing inversion model is required to be used.
Preferably, in the step (1), the measurement is performed by using a hyperspectral radiometer, the measurement time is 10: 00-14: 00, the hyperspectral radiometer adopts a lens with a 25-degree field angle, a sensor probe of the portable field hyperspectral radiometer vertically points to the canopy of the rice and has a vertical height of 1 m from the top layer of the canopy, the ground field range diameter of the sensor probe is 0.44 m, the sensor probe faces the sunlight, the measurement is corrected by using a standard board, and the standard board is a standard white board with a reflectivity of 95% -99%.
Preferably, in the step (2), the step of measuring the accumulation amount of starch in leaves of the rice specifically comprises:
collecting the leaves of the rice, deactivating enzyme, drying to constant weight to obtain dry leaves, measuring the weight of the dry leaves to obtain the dry weight of the leaves, and converting the dry weight of the leaves into the dry weight of the leaves in unit area according to the sampling coverage area;
and crushing the dry leaves, measuring the starch content of the leaves, and multiplying the dry matter weight of the leaves in unit area by the starch content of the leaves to obtain the starch accumulation amount of the leaves.
More preferably, in the step (2), the water-removing temperature is 105 ℃, the water-removing time is 20-30 minutes, the drying temperature is 80-90 ℃, and the determination of the starch content in the leaves adopts a cyclone colorimetry.
Preferably, in the step (3), the model parameter tuning rank matrix is:
Params＝{'min_impurity_decrease','ccp_alpha','max_depth','min_impurity_split','min_samples_leaf','min_samples_split','min_weight_fraction_leaf','splitter','max_features','criterion','max_leaf_nodes'}。
preferably, in the step (4), the optimized values of the model parameters are:
'min_impurity_decrease':0.0,'ccp_alpha':0.00047054761925470663,'max_depth':4,'min_impurity_split':0.0046954861925470655,'min_samples_leaf':1,'min_samples_split':2,'min_weight_fraction_leaf':0.010209198720162859,'splitter':'random','max_features':'auto','criterion':'mae','max_leaf_nodes':None。
preferably, in the step (1), the step of measuring the canopy reflectance of the rice is specifically to measure the canopy reflectance of m sampling points of a rice planting area, the m sampling points are uniformly distributed in the rice planting area, and the canopy reflectance is the canopy reflectance of n characteristic bands; in the step (2), the step of measuring the leaf starch accumulation amount of the rice is specifically to measure the leaf starch accumulation amount of the m sampling points.
More preferably, in the step (1), the m is 37, the n characteristic bands are 2151 characteristic bands, and the 2151 characteristic bands are from 350nm to 2500 nm.
The invention has the following beneficial effects:
1. the remote sensing inversion model of the rice leaf starch accumulation amount is an extreme random tree model of Python language, and model parameters of the extreme random tree model are as follows: ' min _ input _ gradient ' 0.0, ' ccp _ alpha ' 0.00047054761925470663, ' max _ depth ' 4, ' min _ input _ split ' 0.0046954861925470655, ' min _ samples _ leaf ' 1, ' min _ samples _ split ' 2, ' min _ weight _ fraction _ leaf ':0.010209198720162859,' split ': random', 'max _ features': auto ',' criterion ': mae', 'max _ leaf _ nodes' None, the model was examined, R _ leaf _ nodes2Above 0.85, therefore, the method can quickly and accurately acquire the rice leaf starch accumulation information, overcomes the difficulty that the characteristic wave band of the rice leaf starch accumulation is difficult to determine due to the spectrum superposition effect caused by complex rice components, greatly improves the accuracy of the rice leaf starch accumulation inversion model, and is suitable for large-scale popularization and application.
2. The remote sensing inversion model of the rice leaf starch accumulation amount is an extreme random tree model of Python language, and model parameters of the extreme random tree model are as follows: ' min _ input _ gradient ' 0.0, ' ccp _ alpha ' 0.00047054761925470663, ' max _ depth ' 4, ' min _ input _ split ' 0.0046954861925470655, ' min _ samples _ leaf ' 1, ' min _ samples _ split ' 2, ' min _ weight _ fragment _ leaf ' 0.010209198720162859, ' split ' range ', ' max _ defects ' auuo ', ' criterion ' mae ', and ' max _ leaf _ nodes ' None, the model is examined, R is2Above 0.85, therefore, the method has the advantages of ingenious design, simple and convenient calculation, easy realization and low cost, and is suitable for large-scale popularization and application.
3. The remote sensing inversion method of the rice leaf starch accumulation amount comprises the following steps: measuring the canopy reflectance of the rice; measuring the leaf starch accumulation of the rice and taking the logarithm with the base 10 to obtain the logarithm with the base 10 of the leaf starch accumulation: taking the reflectivity of the canopy as input data, calculating by adopting an extreme random tree model of Python language to determine a coefficient R2Constructing a model parameter tuning order matrix; training an extreme random tree model by taking the canopy reflectivity as input data and taking a logarithmic value of the leaf starch accumulation amount with the base of 10 as an output result, and sequentially tuning model parameters according to a model parameter tuning order matrix to obtain tuning values of the model parameters; using the canopy reflectivity as input data, using the log value of the leaf starch accumulation amount with the base 10 as an output result, adopting the adjusted value of the model parameter, training an extreme random tree model to obtain a rice leaf starch accumulation amount remote sensing inversion model, checking the model, and performing R2Above 0.85, therefore, it can be fastThe method can accurately obtain the information of the starch accumulation amount of the rice leaves, overcome the difficulty that the characteristic wave band of the starch accumulation amount of the rice leaves is difficult to determine due to the spectrum superposition effect caused by complex rice components, greatly improve the inversion precision of the starch accumulation amount of the rice leaves, and is suitable for large-scale popularization and application.
4. The remote sensing inversion method of the rice leaf starch accumulation amount comprises the following steps: measuring the canopy reflectance of the rice; measuring the leaf starch accumulation of the rice and taking the logarithm with the base 10 to obtain the logarithm with the base 10 of the leaf starch accumulation: taking the reflectivity of the canopy as input data, calculating by adopting an extreme random tree model of Python language to determine a coefficient R2Constructing a model parameter tuning order matrix; training an extreme random tree model by taking the canopy reflectivity as input data and taking a logarithmic value of the leaf starch accumulation amount with the base of 10 as an output result, and sequentially tuning model parameters according to a model parameter tuning order matrix to obtain tuning values of the model parameters; using the canopy reflectivity as input data, using the log value of the leaf starch accumulation amount with the base 10 as an output result, adopting the adjusted value of the model parameter, training an extreme random tree model to obtain a rice leaf starch accumulation amount remote sensing inversion model, checking the model, and performing R2Above 0.85, therefore, the design is ingenious, the operation is simple and convenient, the cost is low, and the method is suitable for large-scale popularization and application.
These and other objects, features and advantages of the present invention will become more fully apparent from the following detailed description, the accompanying drawings and the claims, and may be realized by means of the instrumentalities, devices and combinations particularly pointed out in the appended claims.
Drawings
FIG. 1 is a schematic flow chart of a specific embodiment of a remote sensing inversion method of rice leaf starch accumulation amount according to the present invention.
FIG. 2 is a schematic diagram of a model building process of the embodiment shown in FIG. 1.
FIG. 3 is a diagram illustrating the results of model testing in the embodiment shown in FIG. 1, wherein the units of the measured value and the predicted value are lg (g/m)2)。
Detailed Description
The invention provides a rice leaf starch accumulation remote sensing inversion model aiming at the requirement of estimating the rice leaf starch accumulation based on hyperspectrum, and overcoming the difficulties that the characteristic wave band of the rice leaf starch accumulation is difficult to determine and the characteristic wave band of hyperspectral data is time-consuming and labor-consuming in screening because of complex rice components, the rice leaf starch accumulation remote sensing inversion model is an extreme random tree model of Python language, and the model parameters of the extreme random tree model are as follows: ' min _ input _ gradient ':0.0, ' ccp _ alpha ':0.00047054761925470663, ' max _ depth ':4, ' min _ input _ split ':0.0046954861925470655, ' min _ samples _ leaf ':1, ' min _ samples _ split ':2, ' min _ weight _ fragment _ leaf ':0.010209198720162859, ' split ': range ', ' max _ defects ': auto ', ' criterion ': mae ', and ' max _ leaf _ nodes ': None.
The extreme random tree model can be trained by any suitable data set, preferably, the extreme random tree model is trained by a data set of rice, the data set comprises logarithmic values with the base of 10 of canopy reflectivity and leaf starch accumulation of m sample points of the rice, the m sample points are uniformly distributed in a rice planting area, and the canopy reflectivity is the canopy reflectivity of n characteristic wave bands. The rice planting area can be a plurality of ecological points and a plurality of varieties of rice planting areas.
M and n are positive integers, which can be determined according to needs, and more preferably, m is 37, n characteristic bands are 2151 characteristic bands, and the 2151 characteristic bands are from 350nm to 2500 nm.
The invention also provides a remote sensing inversion method of the starch accumulation amount of the rice leaves, which comprises the following steps:
(1) measuring the canopy reflectance of the rice;
(2) measuring the leaf starch accumulation amount of the rice, and obtaining a logarithm taking 10 as a base of the leaf starch accumulation amount by taking a logarithm taking 10 as a base of the leaf starch accumulation amount:
(3) taking the canopy reflectivity as input data, and adopting an extreme random tree model of Python language to perform countingCalculating to obtain an inversion value, and calculating a determination coefficient R according to the inversion value and a logarithm value of the leaf starch accumulation amount with the base of 102Changing the value of a model parameter, R, of the extreme stochastic tree model2The larger the change of the model parameter is, the greater the importance of the model parameter is, the model parameter is arranged from large to small according to the importance to construct a model parameter tuning rank matrix;
(4) training the extreme random tree model by taking the canopy reflectivity as the input data and taking a logarithmic value of the leaf starch accumulation amount with the base of 10 as an output result, and sequentially tuning the model parameters according to the model parameter tuning order matrix to obtain tuning values of the model parameters;
(5) and training the extreme random tree model by taking the canopy reflectivity as the input data and taking a logarithmic value of the leaf starch accumulation amount which is based on 10 as the output result, adopting the tuning value of the model parameter, obtaining a rice leaf starch accumulation amount remote sensing inversion model after the training of the extreme random tree model is finished, storing the rice leaf starch accumulation amount remote sensing inversion model by using a save method, and loading the rice leaf starch accumulation amount remote sensing inversion model for use by using a load method if the rice leaf starch accumulation amount remote sensing inversion model is required to be used.
In the step (1), the measurement may be performed by any suitable spectrometer and method, preferably, in the step (1), the measurement is performed by using a hyperspectral radiometer, the measurement time is 10:00 to 14:00, the hyperspectral radiometer uses a lens with a field angle of 25 degrees, a sensor probe of the portable field hyperspectral radiometer vertically points to the canopy of the rice and has a vertical height of 1 meter from the top layer of the canopy, the ground field range diameter of the sensor probe is 0.44 meter, the sensor probe faces the sun, the measurement is corrected by using a standard board, and the standard board is a standard white board with a reflectivity of 95% to 99%.
In the step (2), the step of measuring the starch accumulation amount of rice leaves may specifically include any suitable method, and preferably, in the step (2), the step of measuring the starch accumulation amount of rice leaves specifically includes:
collecting the leaves of the rice, deactivating enzyme, drying to constant weight to obtain dry leaves, measuring the weight of the dry leaves to obtain the dry weight of the leaves, and converting the dry weight of the leaves into the dry weight of the leaves in unit area according to the sampling coverage area;
and crushing the dry leaves, measuring the starch content of the leaves, and multiplying the dry matter weight of the leaves in unit area by the starch content of the leaves to obtain the starch accumulation amount of the leaves.
In the step (2), any suitable conditions can be adopted for the enzyme deactivation and the drying, and any suitable method can be adopted for determining the starch content in the leaves, and preferably, in the step (2), the temperature for the enzyme deactivation is 105 ℃, the time for the enzyme deactivation is 20 minutes to 30 minutes, the temperature for the drying is 80 ℃ to 90 ℃, and the method for determining the starch content in the leaves is implemented by an optical colorimetry.
In the step (3), the model parameter tuning rank matrix is based on a decision coefficient R2Determining, preferably, in the step (3), that the model parameter tuning rank matrix is:
Params＝{'min_impurity_decrease','ccp_alpha','max_depth','min_impurity_split','min_samples_leaf','min_samples_split','min_weight_fraction_leaf','splitter','max_features','criterion','max_leaf_nodes'}。
in the step (4), the tuning values of the model parameters are sequentially determined according to the model parameter tuning rank matrix, and more preferably, in the step (4), the tuning values of the model parameters are:
'min_impurity_decrease':0.0,'ccp_alpha':0.00047054761925470663,'max_depth':4,'min_impurity_split':0.0046954861925470655,'min_samples_leaf':1,'min_samples_split':2,'min_weight_fraction_leaf':0.010209198720162859,'splitter':'random','max_features':'auto','criterion':'mae','max_leaf_nodes':None。
in order to improve the precision of the rice leaf starch accumulation remote sensing inversion model, a plurality of sampling points of a rice planting area can be selected, and the canopy reflectances of a plurality of characteristic bands of the plurality of sampling points and the leaf starch accumulation of the plurality of sampling points are measured, preferably, in the step (1), the step of measuring the canopy reflectivity of the rice is specifically to measure the canopy reflectances of m sampling points of the rice planting area, the m sampling points are uniformly distributed in the rice planting area, and the canopy reflectivity is the canopy reflectivity of n characteristic bands; in the step (2), the step of measuring the leaf starch accumulation amount of the rice is specifically to measure the leaf starch accumulation amount of the m sampling points.
In the step (1), m and n are positive integers, which can be determined as required, and more preferably, in the step (1), m is 37, the n characteristic bands are 2151 characteristic bands, and the 2151 characteristic bands are from 350nm to 2500 nm.
The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention.
Examples
The remote sensing inversion method for the starch accumulation amount of the rice leaves in the embodiment is based on actually measured hyperspectral data, 48 sampling points are adopted, and the sampling points are uniformly distributed and completely cover the whole area of the rice planting area, wherein the rice canopy reflectance spectrum data and the starch accumulation amount data of the rice leaves are collected in a rice planting area (a rice and wheat planting base in Huaian area of agricultural science research institute of Huaian city, Jiangsu province, the rice variety is No. 5, and the sampling period is a rice jointing period). The data of 48 sampling points are divided into two parts by a random method, wherein the data of 37 sampling points is used for model construction, and the data of 11 sampling points is used for model inspection. The flow of the remote sensing inversion method of the rice leaf starch accumulation is shown in figure 1, and comprises the following steps:
1. and (4) performing spectral measurement.
Rice canopy Spectroscopy Using FieldSpec Pro Portable field Hyperspectral produced by American ASDThe radiometer is selected to be carried out in clear weather, no wind or small wind speed within the time range of 10: 00-14: 00, the testers are sampled to wear dark clothes, and influence or interference on the spectrometer is reduced. During sampling, a lens with a 25-degree field angle is selected, a sensor probe vertically points to a measurement target, namely a canopy, the vertical height of the sensor probe is about 1 meter from the top layer of the canopy, the diameter of the ground field range is 0.44 meter, the average value of reflection spectra measured for 10 times is taken as the spectral data of the sampling point. And in the measurement process, the standard white board is corrected before and after the measurement of each sampling point. If the distribution of the environmental light field changes in the test process, the standard white board is also corrected, and the reflectivity of the standard white board used in the embodiment is 99%. Measured spectral data are random software RS of a field hyperspectral radiometer by using FieldSpec Pro portable3Or the ViewSpec Pro software checks, eliminates abnormal spectrum files, performs interpolation calculation on the spectrum data to obtain the spectrum data with the range of 350nm to 2500nm and the resolution of 1nm, calculates the average value of the parallel sampling spectrum of the spectrum, and finally derives the spectrum data and stores the spectrum data as an ASCII file.
2. Determination of starch accumulation in rice leaves
Collecting the overground part plants of the rice, the number of the overground part plants is 6, the overground part plants are wrapped by absorbent paper and brought back to a laboratory, the leaves are separated, the water is removed for 20 minutes at 105 ℃, then the leaves are dried at 85 ℃ until the weight is constant, the dry leaves are obtained, the weight of the dry leaves is measured, the obtained data is the dry weight of the rice leaves, the dry weight of the rice leaves is converted into the dry weight (PD) of the leaves in unit area according to the sampling coverage area, and the unit is g/m2。
Pulverizing dry leaves, measuring leaf Starch Content (SC) (% by weight) by using a spinning colorimetric method, and calculating leaf starch accumulation LSA by the following formula to obtain leaf starch accumulation with unit of g/m2。
LSA＝PD×SC。
3. Model construction
The model construction is implemented by adopting an extreme random tree model of Python language, please refer to FIG. 2, and the model construction mainly comprises the following steps:
3.1 data verification
And checking the acquired rice canopy reflectivity data, and rejecting abnormal whole spectral curve data. The abnormal spectrum in the invention means that adjacent spectrum changes by more than 100%, and spectrum values including null values and negative values are included.
3.2 preprocessing of data
And preprocessing the verified rice canopy reflectivity data and the verified rice leaf starch accumulation data, wherein the preprocessing comprises removing paired rice canopy reflectivity data and rice leaf starch accumulation data containing deletion values and null values. In order to reduce the memory occupation during model training and improve the calculation efficiency and the model precision, the data distribution conversion is carried out on the rice leaf starch accumulation data, namely, the logarithmic value of the rice leaf starch accumulation data with the base of 10 is calculated, and the logarithmic value of the rice leaf starch accumulation with the base of 10 is obtained.
3.3 partitioning of data sets
In order to ensure reasonable evaluation of model training and inversion results, a random method is used for dividing the whole data set into two parts, wherein 80% of data is used for model training, and 20% of data is used for effect evaluation after training.
3.4 partitioning of training data sets
In order to ensure the effect of model training, a random method is used, and a training data set is divided into 5 parts to train the model when the model is trained and iterated every time.
3.5 construction of model parameter tuning rank matrix
In the invention, the tuning of the model parameters in the model training process is very important, and in order to ensure that the best model tuning is obtained as much as possible, a trial-and-error method is used for tuning the model parameters. The present invention uses the coefficient of determination R2(R2The closer to 1, the better) as the test parameter, a parameter rank matrix for evaluating the weight of the model parameter is constructed. According to a training data set, firstly, the default value of the model parameter is used for calculation to obtain an inversion value, and according to the inversion value and a logarithmic value taking 10 as a base of the leaf starch accumulation amount, a decision coefficient R is calculated2And then varying the model parametersValue, R2The larger the change of the model parameter is, the greater the importance of the model parameter is, the model parameter is arranged from large to small according to the importance to construct a model parameter tuning rank matrix for subsequent calculation.
According to the crown layer reflectivity data in the training data set and the logarithmic value data with the base of 10 of the corresponding leaf starch accumulation amount, the model parameter tuning rank matrix obtained by calculation is as follows:
Params＝{'min_impurity_decrease','ccp_alpha','max_depth','min_impurity_split','min_samples_leaf','min_samples_split','min_weight_fraction_leaf','splitter','max_features','criterion','max_leaf_nodes'}。
where a max _ leaf _ nodes change does not cause a change in the accuracy of the model.
3.6 model construction
Optimizing the rank matrix according to the obtained model parameters, and using data used for modeling, including actually-measured crown layer reflectivity data and logarithmic value data with the base of 10 of the corresponding actually-measured leaf starch accumulation amount, taking the actually-measured crown layer reflectivity data as input data, taking the logarithmic value data with the base of 10 of the actually-measured leaf starch accumulation amount as an output result, training an extreme random tree model, and sequentially optimizing the model parameters according to the model parameter optimizing rank matrix to obtain complete parameters and values of the model, wherein the data comprises the following data:
'min_impurity_decrease':0.0,'ccp_alpha':0.00047054761925470663,'max_depth':4,'min_impurity_split':0.0046954861925470655,'min_samples_leaf':1,'min_samples_split':2,'min_weight_fraction_leaf':0.010209198720162859,'splitter':'random','max_features':'auto','criterion':'mae','max_leaf_nodes':None。
after the model training is finished, the save method is used for saving the model, and if the model is required to be used, the load method is operated for loading and using.
For a data set containing m samples and n characteristic wave bands, the model construction calculation process of the extreme random tree model of the Python language is as follows:
(1) constructing a plurality of decision trees by using all the training samples;
(2) when the decision tree is constructed, constructing the decision tree by using the characteristics with the best scores according to the evaluation scores;
(3) when the decision tree is constructed, uniformly and randomly generating bifurcation values of the decision tree in a characteristic experience range, and selecting the division point with the highest score as a node from all random division points without limiting the depth of the decision tree;
(4) after training is complete, prediction of the unknown sample x can be achieved by averaging the predictions of all the individual regression trees on x:
wherein,
for the final predicted value, B is the number of the constructed decision tree, fbTo construct a single decision tree, x is the sample data.
3.7 model test
Using 11 sampling points except for the constructed model to input hyperspectral data into the model, using the adjusted model parameters to calculate to obtain a predicted value, analyzing the relationship between the predicted value and an actual measured value (a logarithmic value with the accumulation amount of the leaf starch being 10), and obtaining a result shown in FIG. 3, wherein R of the model is2Is 0.9293. Model R using default parameters2Is 0.3247.
In the embodiment, Matlab software (version: R2020a 9.8.0.1380330) and Python (version:3.7.0) developed by MathWorks company in the United states are used for random division of training data and test data and construction, training and test of models, and the extreme random tree model of Python is called through the Matlab software.
Therefore, the invention provides a new rice leaf starch accumulation remote sensing inversion model based on actual measurement hyperspectral remote sensing data, the rice leaf starch accumulation information can be rapidly and accurately obtained based on actual measurement rice canopy reflectivity data and rice leaf starch accumulation data collected on the spot, the difficulty that the characteristic wave band of the rice leaf starch accumulation caused by the spectrum superposition effect caused by complex rice components is difficult to determine is overcome, the model parameters are optimized by constructing a model parameter optimization order matrix, the model parameters are optimized by using a trial-and-error method, the phenomenon of linear model overfitting is effectively reduced, the accuracy of the rice leaf starch accumulation inversion is greatly improved, the model is suitable for quantitative inversion of the rice leaf starch accumulation in different ecological regions, different varieties and main growth periods, the physiological state of rice and the state of water and fertilizer supply are obtained, the growth information acquisition efficiency in the rice cultivation process is improved, and provides basic scientific data for the operation and research of moisture fertilizer in rice production.
Compared with the prior art, the invention has the following advantages:
(1) the extreme random tree model (ET) used in the invention is suitable for the reversal of the rice leaf starch accumulation amount based on the hyperspectrum, on the basis of comprehensively considering the information of the hyperspectral 350-2500 nm wave band range, the influence and superposition effect of various substance compositions and cell structures in the rice body, especially the influence and superposition effect of complex components on the characteristic wave band of the rice leaf starch accumulation amount are considered, and the rice leaf starch accumulation amount information contained in different wave bands in remote sensing data is fully utilized to carry out the reversal of the rice leaf starch accumulation amount;
(2) the method has the advantages that a machine learning algorithm of an extreme random tree is used, a model of a logarithm value of the reflectance of 350-2500 nm and the starch accumulation amount of the rice leaves is constructed, overfitting phenomena caused by the use of models such as linear regression can be effectively reduced, and the speed and efficiency of rice leaf starch accumulation amount inversion based on hyperspectral information are improved;
(3) the independence of model training and model inspection is fully considered, the training data set and the inspection data set are divided by a random segmentation method, the training data set is only used for model training, and the inspection data set is only used for model inspection, so that the reasonability of model effect inspection is ensured.
(4) Since the parameter tuning of the model is very important to the calculation accuracy of the model, the invention constructs a model parameter rank matrix to determine the coefficient R2For evaluating the parameters, trial and error is usedAnd the model parameter tuning is performed, and the speed of model training and parameter tuning is greatly improved on the basis of ensuring the parameter tuning effect.
(5) The rice leaf starch accumulation inversion method provided by the invention is simple and convenient to calculate, is suitable for remote sensing quantitative inversion of rice leaf starch accumulation in different ecological regions, different varieties and different growth periods, can accurately invert the rice leaf starch accumulation, can quickly acquire information such as physiological conditions and growth vigor of rice, and simultaneously provides scientific data for water and fertilizer operational management of rice planting and cultivation.
In conclusion, the rice leaf starch accumulation remote sensing inversion model can quickly and accurately acquire the rice leaf starch accumulation information, overcomes the difficulty that the characteristic wave band of the rice leaf starch accumulation is difficult to determine due to the spectrum superposition effect caused by complex rice components, greatly improves the precision of the rice leaf starch accumulation inversion model, is ingenious in design, simple and convenient to calculate, easy to implement, low in cost and suitable for large-scale popularization and application.
It will thus be seen that the objects of the invention have been fully and effectively accomplished. The functional and structural principles of the present invention have been shown and described in the embodiments, and the embodiments may be modified without departing from the principles. Therefore, this invention includes all modifications encompassed within the spirit and scope of the claims.
Claims (11)
1. A remote sensing inversion model of rice leaf starch accumulation is characterized in that the remote sensing inversion model of rice leaf starch accumulation is an extreme random tree model of Python language, and model parameters of the extreme random tree model are as follows: ' min _ input _ gradient ':0.0, ' ccp _ alpha ':0.00047054761925470663, ' max _ depth ':4, ' min _ input _ split ':0.0046954861925470655, ' min _ samples _ leaf ':1, ' min _ samples _ split ':2, ' min _ weight _ fragment _ leaf ':0.010209198720162859, ' split ': range ', ' max _ defects ': auto ', ' criterion ': mae ', and ' max _ leaf _ nodes ': None.
2. The rice leaf starch accumulation remote sensing inversion model of claim 1, wherein the extreme random tree model is trained by a rice data set, the data set comprises canopy reflectances of m sample points of the rice and logarithmic values of the leaf starch accumulation with the base of 10, the m sample points are uniformly distributed in a rice planting area, and the canopy reflectance is the canopy reflectance of n characteristic wave bands.
3. The remote sensing inversion model of starch accumulation amount in rice leaves as claimed in claim 2, wherein m is 37, the n characteristic wave bands are 2151 characteristic wave bands, and the 2151 characteristic wave bands are from 350nm wave band to 2500nm wave band.
4. A remote sensing inversion method for starch accumulation of rice leaves is characterized by comprising the following steps:
(1) measuring the canopy reflectance of the rice;
(2) measuring the leaf starch accumulation amount of the rice, and obtaining a logarithm taking 10 as a base of the leaf starch accumulation amount by taking a logarithm taking 10 as a base of the leaf starch accumulation amount:
(3) calculating by using the canopy reflectivity as input data and adopting an extreme random tree model of Python language to obtain an inversion value, and calculating a decision coefficient R according to the inversion value and a logarithmic value of the accumulation amount of the leaf starch with the base of 102Changing the value of a model parameter, R, of the extreme stochastic tree model2The larger the change of the model parameter is, the greater the importance of the model parameter is, the model parameter is arranged from large to small according to the importance to construct a model parameter tuning rank matrix;
(4) training the extreme random tree model by taking the canopy reflectivity as the input data and taking a logarithmic value of the leaf starch accumulation amount with the base of 10 as an output result, and sequentially tuning the model parameters according to the model parameter tuning order matrix to obtain tuning values of the model parameters;
(5) and training the extreme random tree model by taking the canopy reflectivity as the input data and taking a logarithmic value of the leaf starch accumulation amount which is based on 10 as the output result, adopting the tuning value of the model parameter, obtaining a rice leaf starch accumulation amount remote sensing inversion model after the training of the extreme random tree model is finished, storing the rice leaf starch accumulation amount remote sensing inversion model by using a save method, and loading the rice leaf starch accumulation amount remote sensing inversion model for use by using a load method if the rice leaf starch accumulation amount remote sensing inversion model is required to be used.
5. The remote sensing inversion method of rice leaf starch accumulation amount according to claim 4, characterized in that in the step (1), the measurement is performed by using a hyperspectral radiometer, the measurement time is 10: 00-14: 00, the hyperspectral radiometer uses a lens with a field angle of 25 degrees, a sensor probe of the portable field hyperspectral radiometer is vertically directed to the canopy of the rice and has a vertical height of 1 meter from the top layer of the canopy, the diameter of the ground field range of the sensor probe is 0.44 meter, the sensor probe faces the sunlight, the measurement is corrected by using a standard board, and the standard board is a standard white board with a reflectivity of 95% -99%.
6. The remote sensing inversion method of rice leaf starch accumulation amount according to claim 4, wherein in the step (2), the step of measuring the rice leaf starch accumulation amount specifically comprises:
collecting the leaves of the rice, deactivating enzyme, drying to constant weight to obtain dry leaves, measuring the weight of the dry leaves to obtain the dry weight of the leaves, and converting the dry weight of the leaves into the dry weight of the leaves in unit area according to the sampling coverage area;
and crushing the dry leaves, measuring the starch content of the leaves, and multiplying the dry matter weight of the leaves in unit area by the starch content of the leaves to obtain the starch accumulation amount of the leaves.
7. The remote sensing inversion method of rice leaf starch accumulation amount according to claim 6, wherein in the step (2), the water-removing temperature is 105 ℃, the water-removing time is 20-30 minutes, the drying temperature is 80-90 ℃, and the determination of the leaf starch content adopts an optical colorimetry.
8. The remote sensing inversion method for rice leaf starch accumulation according to claim 4, wherein in the step (3), the model parameter tuning rank matrix is:
Params＝{'min_impurity_decrease','ccp_alpha','max_depth','min_impurity_split','min_samples_leaf','min_samples_split','min_weight_fraction_leaf','splitter','max_features','criterion','max_leaf_nodes'}。
9. the remote sensing inversion method for rice leaf starch accumulation according to claim 8, wherein in the step (4), the model parameters are adjusted by the following values:
'min_impurity_decrease':0.0,'ccp_alpha':0.00047054761925470663,'max_depth':4,'min_impurity_split':0.0046954861925470655,'min_samples_leaf':1,'min_samples_split':2,'min_weight_fraction_leaf':0.010209198720162859,'splitter':'random','max_features':'auto','criterion':'mae','max_leaf_nodes':None。
10. the remote sensing inversion method of rice leaf starch accumulation amount according to claim 4, wherein in the step (1), the step of measuring the rice canopy reflectance is specifically to measure the canopy reflectance of m sampling points in a rice planting area, the m sampling points are uniformly distributed in the rice planting area, and the canopy reflectance is the canopy reflectance of n characteristic wave bands; in the step (2), the step of measuring the leaf starch accumulation amount of the rice is specifically to measure the leaf starch accumulation amount of the m sampling points.
11. The remote sensing inversion method of starch accumulation amount in rice leaves as claimed in claim 10, wherein in said step (1), said m is 37, said n characteristic bands are 2151 characteristic bands, and said 2151 characteristic bands are from 350nm to 2500 nm.
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