CN111829965A - Remote sensing inversion model and method for starch accumulation amount of rice overground part - Google Patents
Remote sensing inversion model and method for starch accumulation amount of rice overground part Download PDFInfo
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Abstract
The invention provides a remote sensing inversion model of starch accumulation of the overground part of rice, 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 amount of the overground part of the rice. The remote sensing inversion model of the starch accumulation of the overground part of the rice can quickly and accurately acquire the starch accumulation information of the overground part of the rice, overcomes the difficulty that the characteristic wave band of the starch accumulation of the overground part of the rice 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 starch accumulation of the overground part of the rice, and is ingenious in design, simple and convenient to calculate, easy to realize, 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 measurement of starch accumulation of rice overground parts, and specifically relates to a remote sensing inversion model and method for the starch accumulation of the rice overground parts.
Background
The rice aboveground starch accumulation amount is the total accumulation amount of starch in the rice aboveground, is an important parameter for quantifying photosynthesis of the rice aboveground to fix carbon dioxide and synthesize carbohydrate, is an important index of the physiological condition and the growth vigor of rice, and reflects the physiological condition, the growth vigor and the rich water condition of the rice (Zhoudouqin. monitoring of rice nitrogen nutrition and seed quality based on a canopy reflection spectrum [ D ]. Nanjing agriculture university, 2007).
The method has the advantages that the starch accumulation amount of the overground part of the rice is monitored, so that the yield and the quality of rice production can be guaranteed, the water and fertilizer control of the rice can be dynamically managed, the rice production efficiency is improved, and remarkable economic and social benefits are generated. The traditional method for monitoring the starch accumulation amount of the overground part of the rice 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 overground part of the rice, 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 rice growth information such as the starch accumulation amount of the overground part of the rice can be obtained by using the change of the spectrum. 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 starch accumulation information of the overground part of the rice, and the information becomes a consensus of more and more rice production practitioners and researchers (Zhoudouqin. rice nitrogen nutrition and grain quality monitoring based on canopy reflection spectrum [ D ]. Nanjing agriculture university, 2007). The most common mode is to use a portable full-waveband spectrometer to obtain growth information such as the starch accumulation amount of the overground part of the rice and select a characteristic waveband capable of reflecting the starch accumulation amount of the overground part to construct an inversion model. In the process of constructing the rice overground part starch accumulation inversion model, the spectral range measured by the full-waveband spectrometer covers 350-2500 nm, but because the components of the rice are complex, the characteristic wave bands of the component spectra are partially overlapped, the determination of the characteristic spectrum of the rice overground part starch accumulation is difficult, and meanwhile, the rapid processing of hyperspectral data becomes an urgent technical problem to be solved for estimating the rice overground part starch accumulation based on the hyperspectral data.
Therefore, it is desirable to provide a remote sensing inversion model of the starch accumulation of the overground part of rice, which can quickly and accurately acquire the starch accumulation information of the overground part of rice, overcome the difficulty that the characteristic waveband of the starch accumulation of the overground part of rice is difficult to determine due to the spectrum superposition effect caused by complex rice components, and greatly improve the precision of the inversion model of the starch accumulation of the overground part of rice.
Disclosure of Invention
In order to overcome the defects in the prior art, one object of the present invention is to provide a remote sensing inversion model of starch accumulation in rice overground parts, which can quickly and accurately obtain the starch accumulation information in rice overground parts, overcome the difficulty that the characteristic waveband of starch accumulation in rice overground parts is difficult to determine due to the spectrum superposition effect caused by complex rice components, greatly improve the precision of the inversion model of starch accumulation in rice overground parts, and is suitable for large-scale popularization and application.
The invention also aims to provide a remote sensing inversion model of the starch accumulation amount of the overground part of the rice, 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 starch accumulation of the overground part of the rice, which can quickly and accurately acquire the starch accumulation information of the overground part of the rice, overcome the difficulty that the characteristic wave band of the starch accumulation of the overground part of the rice is difficult to determine due to the spectrum superposition effect caused by complex rice components, greatly improve the inversion precision of the starch accumulation of the overground part of the rice, 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 overground part of the rice, 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 remote sensing inversion model of starch accumulation in rice overground parts, which is characterized in that the remote sensing inversion model of starch accumulation in rice overground parts is an extreme random tree model in Python language, and model parameters of the extreme random tree model are as follows: ' min _ samples _ leaf ' 4, ' min _ weight _ fraction _ leaf ' 0.035618029098943474, ' min _ input _ split ' 0.0046954861925470655, ' criterion ' mse ', ' max _ features ' auto ', ' ccp _ alpha ' 0.00047054761925470663, ' min _ input _ gradient ' 0.0002021839744032572, ' split ' range ', ' min _ samples _ split ' 2, ' max _ depth ' 45, ' 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 an overground starch accumulation amount, the m sampling points are uniformly distributed in a rice planting area, and the canopy reflectivity is a 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 starch accumulation amount of rice overground parts, which is characterized by comprising the following steps:
(1) measuring the canopy reflectance of the rice;
(2) measuring the starch accumulation amount of the overground part of the rice:
(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 the starch accumulation amount of the overground part2Changing the value of a model parameter, R, of the extreme stochastic tree model2The larger the change of the model parameter is, the larger 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 the overground part starch accumulation amount 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 using the canopy reflectivity as the input data and the overground part starch accumulation as the output result and adopting the adjusted value of the model parameter, obtaining a remote sensing inversion model of the rice overground part starch accumulation after the training of the extreme random tree model is finished, storing the remote sensing inversion model of the rice overground part starch accumulation by using a save method, and loading the remote sensing inversion model of the rice overground part starch accumulation by using a load method if the remote sensing inversion model of the rice overground part starch accumulation 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 starch accumulation amount of the overground part of the rice specifically comprises:
collecting the overground part plants of the rice, deactivating enzyme, drying to constant weight to obtain dry overground part plants, and measuring the weight of the dry overground part plants to obtain data, namely the dry weight of the overground part plants;
and crushing the dry overground part plants, measuring the starch content of the overground part plants, and multiplying the dry weight of the overground part plants by the starch content of the overground part plants to obtain the overground part starch accumulation amount.
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 of the overground plant is carried out by a light rotation colorimetry.
Preferably, in the step (3), the model parameter tuning rank matrix is:
Params＝{'min_samples_leaf','min_weight_fraction_leaf','min_impurity_split','criterion','max_features','ccp_alpha','min_impurity_decrease','splitter','min_samples_split','max_depth','max_leaf_nodes'}。
preferably, in the step (4), the optimized values of the model parameters are:
'min_samples_leaf':4,'min_weight_fraction_leaf':0.035618029098943474,'min_impurity_split':0.0046954861925470655,'criterion':'mse','max_features':'auto','ccp_alpha':0.00047054761925470663,'min_impurity_decrease':0.0002021839744032572,'splitter':'random','min_samples_split':2,'max_depth':45,'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 starch accumulation amount on the above-ground part of the rice is specifically to measure the starch accumulation amount on the above-ground part of the m spots.
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 starch accumulation amount on the overground part of the rice is an extreme random tree model in Python language, and model parameters of the extreme random tree model are as follows: 'min _ samples _ leaf' 4, 'min _ weight _ fraction _ leaf' 0.035618029098943474, 'min _ input _ split' 0.0046954861925470655, 'criterion' mse ',' max _ features 'auto', 'ccp _ alpha' 0.00047054761925470663, 'min _ input _ gradient' 0.0002021839744032572, 'split' range ',' min _ samples _ split '2,' max _ depth '45,' max _ leaf _ nodes 'None, the model is tested, R _ weight _ fraction _ leaf' 0.0356180290989434742Above 0.85, therefore, the method can quickly and accurately acquire the starch accumulation amount information of the overground part of the rice, and overcome the problem of the overground part of the rice deposition caused by the spectrum superposition effect caused by complex rice componentsThe method has the advantages that the characteristic wave band of the accumulation amount of the starch is difficult to determine, the accuracy of an inversion model of the accumulation amount of the starch on the overground part of the rice is greatly improved, and the method is suitable for large-scale popularization and application.
2. The remote sensing inversion model of the starch accumulation amount on the overground part of the rice is an extreme random tree model in Python language, and model parameters of the extreme random tree model are as follows: 'min _ samples _ leaf' 4, 'min _ weight _ fraction _ leaf' 0.035618029098943474, 'min _ input _ split' 0.0046954861925470655, 'criterion' mse ',' max _ features 'auto', 'ccp _ alpha' 0.00047054761925470663, 'min _ input _ gradient' 0.0002021839744032572, 'split' range ',' min _ samples _ split '2,' max _ depth '45,' max _ leaf _ nodes 'None, the model is tested, R _ weight _ fraction _ leaf' 0.0356180290989434742Above 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 invention discloses a remote sensing inversion method of starch accumulation amount of overground part of rice, comprising the following steps: measuring the canopy reflectance of the rice; measuring the starch accumulation amount of the overground part of the rice: 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 reflectivity of the canopy as input data and the starch accumulation amount of the overground part as output results, and sequentially tuning model parameters according to a model parameter tuning order matrix to obtain tuning values of the model parameters; training an extreme random tree model by taking the canopy reflectivity as input data and the overground part starch accumulation as output results and adopting the adjusted values of model parameters to obtain a remote sensing inversion model of the rice overground part starch accumulation, inspecting the model, and performing R2Above 0.85, therefore, the method can quickly and accurately acquire the information of the starch accumulation amount of the overground part of the rice, overcomes the difficulty that the characteristic wave band of the starch accumulation amount of the overground part of the rice is difficult to determine due to the spectrum superposition effect caused by complex rice components, greatly improves the inversion precision of the starch accumulation amount of the overground part of the rice, and is suitable for large-scale popularization and application.
4. The invention discloses a remote sensing inversion method of starch accumulation amount of overground part of rice, comprising the following steps: measuring canopy reflectance of rice(ii) a Measuring the starch accumulation amount of the overground part of the rice: 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 reflectivity of the canopy as input data and the starch accumulation amount of the overground part as output results, and sequentially tuning model parameters according to a model parameter tuning order matrix to obtain tuning values of the model parameters; training an extreme random tree model by taking the canopy reflectivity as input data and the overground part starch accumulation as output results and adopting the adjusted values of model parameters to obtain a remote sensing inversion model of the rice overground part starch accumulation, inspecting 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 the remote sensing inversion method of starch accumulation amount on the overground part of rice of 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 measured values and the predicted values are both in g/m units2。
Detailed Description
The invention provides a remote sensing inversion model of the starch accumulation amount of the overground part of rice, aiming at the requirement of estimating the starch accumulation amount of the overground part of the rice based on hyperspectrum, overcoming the difficulties that the characteristic wave band of the starch accumulation amount of the overground part of the rice is difficult to determine and the characteristic wave band of hyperspectral data is time-consuming and labor-consuming in screening because of complex components of the rice, wherein the remote sensing inversion model of the starch accumulation amount of the overground part of the rice is an extreme random tree model of Python language, and the model parameters of the extreme random tree model are as follows: ' min _ samples _ leaf ' 4, ' min _ weight _ fraction _ leaf ' 0.035618029098943474, ' min _ input _ split ' 0.0046954861925470655, ' criterion ' mse ', ' max _ features ' auto ', ' ccp _ alpha ' 0.00047054761925470663, ' min _ input _ gradient ' 0.0002021839744032572, ' split ' range ', ' min _ samples _ split ' 2, ' max _ depth ' 45, ' max _ leaf _ nodes ' None.
The extreme random tree model may be trained by using any suitable data set, and preferably, the extreme random tree model is trained by using a data set of rice, the data set includes canopy reflectances and overground starch accumulation amounts of m sampling points of the rice, the m sampling points are uniformly distributed in a rice planting area, and the canopy reflectance is a canopy reflectance of n characteristic 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 overground part of the rice, which comprises the following steps:
(1) measuring the canopy reflectance of the rice;
(2) measuring the starch accumulation amount of the overground part of the rice:
(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 the starch accumulation amount of the overground part2Changing the value of a model parameter, R, of the extreme stochastic tree model2The larger the change of the model parameter is, the larger 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 the overground part starch accumulation amount 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 using the canopy reflectivity as the input data and the overground part starch accumulation as the output result and adopting the adjusted value of the model parameter, obtaining a remote sensing inversion model of the rice overground part starch accumulation after the training of the extreme random tree model is finished, storing the remote sensing inversion model of the rice overground part starch accumulation by using a save method, and loading the remote sensing inversion model of the rice overground part starch accumulation by using a load method if the remote sensing inversion model of the rice overground part starch accumulation 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 the overground part of the rice may specifically include any suitable method, and preferably, in the step (2), the step of measuring the starch accumulation amount of the overground part of the rice specifically includes:
collecting the overground part plants of the rice, deactivating enzyme, drying to constant weight to obtain dry overground part plants, and measuring the weight of the dry overground part plants to obtain data, namely the dry weight of the overground part plants;
and crushing the dry overground part plants, measuring the starch content of the overground part plants, and multiplying the dry weight of the overground part plants by the starch content of the overground part plants to obtain the overground part starch accumulation amount.
In the step (2), the water-removing and the drying can adopt any suitable conditions, and the starch content of the overground plant can be measured by any suitable method, and 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 starch content of the overground plant is measured by an optical rotation 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_samples_leaf','min_weight_fraction_leaf','min_impurity_split','criterion','max_features','ccp_alpha','min_impurity_decrease','splitter','min_samples_split','max_depth','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_samples_leaf':4,'min_weight_fraction_leaf':0.035618029098943474,'min_impurity_split':0.0046954861925470655,'criterion':'mse','max_features':'auto','ccp_alpha':0.00047054761925470663,'min_impurity_decrease':0.0002021839744032572,'splitter':'random','min_samples_split':2,'max_depth':45,'max_leaf_nodes':None。
in order to improve the precision of the remote sensing inversion model of the starch accumulation of the overground part of the rice, a plurality of sampling points of a rice planting area can be selected, and the canopy reflectances of a plurality of characteristic wave bands of the plurality of sampling points and the overground part starch accumulation of the plurality of sampling points are determined, preferably, in the step (1), the step of measuring the canopy reflectance 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 reflectance is the canopy reflectance of n characteristic wave bands; in the step (2), the step of measuring the starch accumulation amount on the above-ground part of the rice is specifically to measure the starch accumulation amount on the above-ground part of the m spots.
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 on the overground part of the rice in the embodiment is based on actually measured hyperspectral data, and adopts rice canopy reflectance spectrum data and rice starch accumulation amount data on the overground part of the rice, which are collected by 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), wherein the total number of the 48 sampling points are uniformly distributed and completely cover the whole area of the rice planting area. 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 starch accumulation amount of the overground part of the rice is shown in figure 1, and comprises the following steps:
1. and (4) performing spectral measurement.
The rice canopy spectrum measurement is carried out by using a field Spec Pro portable field hyperspectral radiometer produced by American ASD in clear weather, no wind or small wind speed within the time range of 10: 00-14: 00, and the sampling testers wear dark clothes to reduce the influence or interference on the spectrometer. 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 is also carried outThe reflectance of the standard white board used in this example was 99% with board calibration. 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 amount in overground part of rice
Collecting 6 rice overground part plants uniformly distributed in a spectral measurement view field of each sampling point, wrapping the plants by using absorbent paper, bringing the plants back to a laboratory, deactivating enzyme at 105 ℃ for 20 minutes, drying the plants at 85 ℃ until the plants are constant weight, obtaining dry overground part plants, measuring the weight of the dry overground part plants, obtaining the dry matter weight of the rice overground part plants, converting the dry matter weight (PD) of the rice overground part plants in unit area according to sampling coverage area, wherein the unit is g/m2。
Pulverizing dry aerial plant, determining Starch Content (SC) (% by weight) of aerial plant by using photoperiod method, and calculating the starch accumulation amount PSA of aerial plant by using the following formula to obtain the starch accumulation amount in g/m2。
PSA＝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 rice overground part starch accumulation data, wherein the preprocessing comprises removing paired rice canopy reflectivity data and rice overground part starch accumulation data containing deletion values and null values.
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 a decision coefficient R is calculated according to the inversion value and a true value2Then changing the value of the model parameter, 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 corresponding overground part starch accumulation data, the model parameter tuning order matrix obtained by calculation is as follows:
Params＝{'min_samples_leaf','min_weight_fraction_leaf','min_impurity_split','criterion','max_features','ccp_alpha','min_impurity_decrease','splitter','min_samples_split','max_depth','max_leaf_nodes'}。
where a max _ leaf _ nodes change does not cause a change in the accuracy of the model.
3.6 model construction
According to the obtained model parameter tuning order matrix, training an extreme random tree model by using data used for modeling, including actually-measured crown layer reflectivity data and corresponding actually-measured overground part starch accumulation data, taking the actually-measured crown layer reflectivity data as input data and the actually-measured overground part starch accumulation data as output results, and sequentially tuning model parameters according to the model parameter tuning order matrix to obtain complete parameters and values of the model, wherein the data comprises the following data:
'min_samples_leaf':4,'min_weight_fraction_leaf':0.035618029098943474,'min_impurity_split':0.0046954861925470655,'criterion':'mse','max_features':'auto','ccp_alpha':0.00047054761925470663,'min_impurity_decrease':0.0002021839744032572,'splitter':'random','min_samples_split':2,'max_depth':45,'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 the 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, and obtaining a result shown in figure 3, wherein R of the model is2For 0.9156, model R with default parameters is used2Is 0.4837.
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 remote sensing inversion model of the starch accumulation amount of the overground part of the rice based on the actually measured hyperspectral remote sensing data, the information of the starch accumulation amount of the overground part of the rice can be quickly and accurately obtained based on the actually measured reflectance data of the rice canopy and the starch accumulation amount data of the overground part of the rice collected on the spot, the difficulty that the characteristic wave band of the starch accumulation amount of the overground part of the rice is difficult to determine caused by the spectrum superposition effect caused by the complex rice components is overcome, the phenomenon of overfitting of a linear model is effectively reduced by constructing a model parameter optimization order matrix and optimizing the model parameter by using a trial and error method, the inversion precision of the starch accumulation amount of the overground part of the rice is greatly improved, the remote sensing inversion model is suitable for the quantitative inversion of the starch accumulation amount of the overground part of the rice in different ecological regions, different varieties and main growth periods, so as to obtain the physiological state, the growth information acquisition efficiency in the rice cultivation and planting process is improved, and basic scientific data are provided for the operation and planning of moisture fertilizers 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 inversion of the starch accumulation amount of the rice overground part based on the hyperspectrum, on the basis of comprehensively considering the information of the hyperspectral 350-2500 nm wave band range, the optical characteristics of various substance compositions and cell structures in the rice body are considered, particularly the influence and superposition effect of complex components on the characteristic wave band of the starch accumulation amount of the rice overground part are considered, and the rice overground part starch accumulation amount information contained in different wave bands in remote sensing data is fully utilized to carry out the inversion of the starch accumulation amount of the rice overground part;
(2) the method has the advantages that a machine learning algorithm of an extreme random tree is used, a model of the reflectance of 350-2500 nm and the starch accumulation amount of the overground part of the rice is constructed, overfitting phenomena caused by the use of models such as linear regression can be effectively reduced, and the speed and efficiency of the inversion of the starch accumulation amount of the overground part of the rice 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 R2In order to evaluate the parameters, a trial and error method is used for model parameter tuning, and on the basis of ensuring the parameter tuning effect, the speed of model training and parameter tuning is greatly improved.
(5) The inversion method for the starch accumulation amount of the overground part of the rice is simple and convenient to calculate, is suitable for remote sensing quantitative inversion of the starch accumulation amount of the overground part of the rice in different ecological regions, different varieties and different growth periods, can accurately invert the starch accumulation amount of the overground part of the rice, can quickly acquire the information of physiological conditions, growth vigor and the like of the rice in photosynthesis and the like, and simultaneously provides scientific data for water and fertilizer operational management of the yield and quality of rice planting and cultivation.
In conclusion, the remote sensing inversion model for the starch accumulation of the overground part of the rice can quickly and accurately acquire the starch accumulation information of the overground part of the rice, overcomes the difficulty that the characteristic wave band of the starch accumulation of the overground part of the rice is difficult to determine due to the spectrum superposition effect caused by complex rice components, greatly improves the precision of the inversion model for the starch accumulation of the overground part of the rice, and is ingenious in design, simple and convenient to calculate, easy to realize, 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. The remote sensing inversion model of the starch accumulation of the rice overground part is characterized in that the remote sensing inversion model of the starch accumulation of the rice overground part is an extreme random tree model of Python language, and model parameters of the extreme random tree model are as follows: ' min _ samples _ leaf ' 4, ' min _ weight _ fraction _ leaf ' 0.035618029098943474, ' min _ input _ split ' 0.0046954861925470655, ' criterion ' mse ', ' max _ features ' auto ', ' ccp _ alpha ' 0.00047054761925470663, ' min _ input _ gradient ' 0.0002021839744032572, ' split ' range ', ' min _ samples _ split ' 2, ' max _ depth ' 45, ' max _ leaf _ nodes ' None.
2. The remote sensing inversion model of starch accumulation of rice overground part according to claim 1, characterized in that the extreme random tree model is trained by a data set of rice, the data set comprises canopy reflectivity of m sampling points of the rice and the starch accumulation of the overground part, the m sampling points are uniformly distributed in a rice planting area, and the canopy reflectivity is the canopy reflectivity of n characteristic wave bands.
3. The remote sensing inversion model of starch accumulation amount on rice ground parts according to 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 overground parts is characterized by comprising the following steps:
(1) measuring the canopy reflectance of the rice;
(2) measuring the starch accumulation amount of the overground part of the rice:
(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 the starch accumulation amount of the overground part2Changing the value of a model parameter, R, of the extreme stochastic tree model2The larger the change of the model parameter is, the larger 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 the overground part starch accumulation amount 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 using the canopy reflectivity as the input data and the overground part starch accumulation as the output result and adopting the adjusted value of the model parameter, obtaining a remote sensing inversion model of the rice overground part starch accumulation after the training of the extreme random tree model is finished, storing the remote sensing inversion model of the rice overground part starch accumulation by using a save method, and loading the remote sensing inversion model of the rice overground part starch accumulation by using a load method if the remote sensing inversion model of the rice overground part starch accumulation is required to be used.
5. The remote sensing inversion method of starch accumulation amount on the overground part of rice as claimed in claim 4, wherein in the step (1), the measurement is carried out 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 for the starch accumulation amount of the overground part of rice as claimed in claim 4, wherein in the step (2), the step of measuring the starch accumulation amount of the overground part of rice specifically comprises:
collecting the overground part plants of the rice, deactivating enzyme, drying to constant weight to obtain dry overground part plants, and measuring the weight of the dry overground part plants to obtain data, namely the dry weight of the overground part plants;
and crushing the dry overground part plants, measuring the starch content of the overground part plants, and multiplying the dry weight of the overground part plants by the starch content of the overground part plants to obtain the overground part starch accumulation amount.
7. The remote sensing inversion method of starch accumulation amount on the overground part of rice as claimed in 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 starch content of the overground part plant is carried out by an optical colorimetry.
8. The remote sensing inversion method for starch accumulation amount on rice overground part according to claim 4, characterized in that in the step (3), the model parameter tuning rank matrix is:
Params＝{'min_samples_leaf','min_weight_fraction_leaf','min_impurity_split','criterion','max_features','ccp_alpha','min_impurity_decrease','splitter','min_samples_split','max_depth','max_leaf_nodes'}。
9. the remote sensing inversion method for starch accumulation amount on rice overground part according to claim 8, characterized in that in the step (4), the adjusted values of the model parameters are as follows:
'min_samples_leaf':4,'min_weight_fraction_leaf':0.035618029098943474,'min_impurity_split':0.0046954861925470655,'criterion':'mse','max_features':'auto','ccp_alpha':0.00047054761925470663,'min_impurity_decrease':0.0002021839744032572,'splitter':'random','min_samples_split':2,'max_depth':45,'max_leaf_nodes':None。
10. the remote sensing inversion method of starch accumulation amount on rice overground part according to claim 4, characterized in that in the step (1), the step of measuring the canopy reflectivity of rice is specifically to measure the canopy reflectivity of m sampling points in a 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 wave bands; in the step (2), the step of measuring the starch accumulation amount on the above-ground part of the rice is specifically to measure the starch accumulation amount on the above-ground part of the m spots.
11. The remote sensing inversion method for starch accumulation amount on rice ground parts according to claim 10, wherein in the step (1), the 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.
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