CN111855589A - Remote sensing inversion model and method for rice leaf nitrogen accumulation - Google Patents
Remote sensing inversion model and method for rice leaf nitrogen accumulation Download PDFInfo
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Abstract
The invention provides a rice leaf nitrogen accumulation remote sensing inversion model, which is an extreme random tree model in Python language and further provides model parameters of the extreme random tree model. A remote sensing inversion method of the rice leaf nitrogen accumulation is also provided. The rice leaf nitrogen accumulation remote sensing inversion model can quickly and accurately acquire the rice leaf nitrogen accumulation information, overcomes the difficulty that the characteristic wave band of the rice leaf nitrogen accumulation is difficult to determine due to the spectrum superposition effect caused by complex rice components, greatly improves the precision of the rice leaf nitrogen accumulation inversion model, 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 measurement of rice leaf nitrogen accumulation, and specifically relates to a rice leaf nitrogen accumulation remote sensing inversion model and a method.
Background
The nitrogen accumulation amount of the rice leaves is the total accumulation amount of nitrogen elements in the rice leaves, is an important index reflecting the nitrogen nutrition condition of the rice, the growth vigor of the rice and the absorption and utilization efficiency of the rice to nitrogen fertilizers, and reflects the physiology, the growth vigor and the water and fertilizer condition of the rice (Shaoyinni. research on a rapid lossless acquisition technology of physiological characteristic information of the rice growth [ D ]. Zhejiang university, 2010).
The nitrogen accumulation amount of the rice leaves is monitored, so that the yield and the quality of rice production can be ensured, the nitrogen fertilizer application of the rice can be dynamically managed, the use amount of the nitrogen fertilizer in the rice production is reduced, and the environmental problem caused by the application of a large amount of the nitrogen fertilizer is relieved, so that remarkable economic and social benefits are generated (aging, Tianyong super, Yaoaxia, and the like; research on the nitrogen topdressing regulation and control effect of the rice based on the canopy reflection spectrum [ J ] Chinese agricultural science, 2010,43(20): 4149-. The traditional method for monitoring the nitrogen accumulation of the rice leaves mainly adopts a destructive sampling method, needs to be measured indoors, is time-consuming and labor-consuming, has poor timeliness, cannot acquire the nitrogen accumulation of the rice leaves in time, 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 the nitrogen accumulation amount of the rice leaves (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 rice leaf nitrogen accumulation information, and the method becomes a consensus of more and more rice production practitioners and researchers. The most common mode is to use a portable full-wave-band spectrometer to obtain rice growth information and select a characteristic wave band capable of reflecting the leaf nitrogen accumulation amount to construct an inversion model. In the process of constructing the rice leaf nitrogen accumulation inversion model, the spectral range measured by the full-waveband spectrometer covers 350-2500 nm, but the rice components are complex, the component spectral characteristic wavebands are partially overlapped, the determination of the rice leaf nitrogen 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 nitrogen accumulation based on the hyperspectral data.
Therefore, it is desirable to provide a rice leaf nitrogen accumulation remote sensing inversion model, which can quickly and accurately acquire rice leaf nitrogen accumulation information, overcome the difficulty that the characteristic wave band of rice leaf nitrogen accumulation is difficult to determine due to the spectrum superposition effect caused by complex rice components, and greatly improve the accuracy of the rice leaf nitrogen accumulation inversion model.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a rice leaf nitrogen accumulation remote sensing inversion model, which can quickly and accurately acquire rice leaf nitrogen accumulation information, overcomes the difficulty that a rice leaf nitrogen accumulation characteristic waveband is difficult to determine due to a spectrum superposition effect caused by complex rice components, greatly improves the accuracy of the rice leaf nitrogen accumulation inversion model, and is suitable for large-scale popularization and application.
The invention also aims to provide a rice leaf nitrogen 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 nitrogen accumulation, which can quickly and accurately acquire the rice leaf nitrogen accumulation information, overcomes the difficulty that the characteristic wave band of the rice leaf nitrogen 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 nitrogen accumulation, and is suitable for large-scale popularization and application.
The invention also aims to provide a rice leaf nitrogen accumulation remote sensing inversion method which is ingenious in design, simple and convenient to operate, low in cost and 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 nitrogen accumulation remote sensing inversion model, which is characterized in that the rice leaf nitrogen 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 _ split':0.02021840744032572, 'min _ input _ decode': 0.0, 'ccp _ alpha':0.0, 'min _ samples _ split':2, 'max _ decode': None, 'criterion': 'mse', 'min _ weight _ fragment _ leaf':0.0, 'min _ samples _ leaf':1, 'split': range ',' max _ defects ': auto', and 'max _ leaf _ nodes'.
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 leaf nitrogen amount with the base of 10, 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 38, 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 nitrogen accumulation, which is characterized by comprising the following steps:
(1) measuring the canopy reflectance of the rice;
(2) measuring the leaf nitrogen accumulation amount of the rice, and obtaining a logarithm taking 10 as a base of the leaf nitrogen accumulation amount by taking a logarithm taking 10 as a base of the leaf nitrogen 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 leaf nitrogen 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 nitrogen 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 nitrogen accumulation amount which is based on 10 as the output result, adopting the tuning value of the model parameter, obtaining a rice leaf nitrogen accumulation amount remote sensing inversion model after the training of the extreme random tree model is finished, storing the rice leaf nitrogen accumulation amount remote sensing inversion model by using a save method, and loading the rice leaf nitrogen accumulation amount remote sensing inversion model for use by using a load method if the rice leaf nitrogen 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 leaf nitrogen accumulation amount of the rice 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 total nitrogen content of the leaves, and multiplying the dry matter weight of the leaves in unit area by the total nitrogen content of the leaves to obtain the leaf nitrogen 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 full nitrogen content of the leaves is determined by adopting a half-micro Kjeldahl method.
Preferably, in the step (3), the model parameter tuning rank matrix is:
Params＝{'min_impurity_split','min_impurity_decrease','ccp_alpha','min_samples_split','max_depth','criterion','min_weight_fraction_leaf','min_samples_leaf','splitter','max_features','max_leaf_nodes'}。
preferably, in the step (4), the optimized values of the model parameters are:
'min_impurity_split':0.02021840744032572,'min_impurity_decrease':0.0,'ccp_alpha':0.0,'min_samples_split':2,'max_depth':None,'criterion':'mse','min_weight_fraction_leaf':0.0,'min_samples_leaf':1,'splitter':'random','max_features':'auto','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 nitrogen accumulation amount of the rice is specifically to measure the leaf nitrogen accumulation amount of the m sampling points.
More preferably, in the step (1), the m is 38, 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 nitrogen accumulation amount is an extreme random tree model in Python language, and model parameters of the extreme random tree model are as follows: ' min _ input _ split ':0.02021840744032572, ' min _ input _ split ':0.0, ' ccp _ alpha ':0.0, ' min _ samples _ split ':2, ' max _ split ': None, ' gradient ': mse ', ' min _ weight _ split _ leaf ':0.0, ' min _ samples _ leaf ':1, ' split ': range ', ' max _ defects ': auto ', ' max _ leaf _ node ':0.02021840744032572s' None, the model was examined, R2Above 0.83, the method can quickly and accurately acquire the rice leaf nitrogen accumulation information, overcomes the difficulty that the characteristic wave band of the rice leaf nitrogen accumulation is difficult to determine due to the spectrum superposition effect caused by complex rice components, greatly improves the precision of the rice leaf nitrogen accumulation inversion model, and is suitable for large-scale popularization and application.
2. The remote sensing inversion model of the rice leaf nitrogen accumulation amount is an extreme random tree model in Python language, and model parameters of the extreme random tree model are as follows: ' min _ input _ split ':0.02021840744032572, ' min _ input _ split ':0.0, ' ccp _ alpha ':0.0, ' min _ samples _ split ':2, ' max _ split ': None, ' criterion ': mse ', ' min _ weight _ fragment _ leaf ':0.0, ' min _ samples _ leaf ':1, ' split ': range ', ' max _ defects ': auto ', ' max _ leaf _ nodes ': None, the model is examined, R _ input _ gradient ':0.02021840744032572, the model is examined, R _ input _ gradient ' 0.02More than 0.83, 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 rice leaf nitrogen accumulation, which comprises the following steps: measuring the canopy reflectance of the rice; measuring the leaf nitrogen accumulation of the rice and taking the logarithm with the base 10 to obtain the logarithm with the base 10 of the leaf nitrogen 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 reflectivity of the canopy as input data and taking a logarithmic value of the accumulated quantity of the leaf nitrogen with the base 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; training an extreme random tree model by taking the canopy reflectivity as input data and taking a logarithmic value of the leaf nitrogen accumulation amount with the base of 10 as an output result and adopting an adjusted value of model parameters to obtain a rice leaf nitrogen accumulation amount remote sensing inversion model, inspecting the model, and performing R2Above 0.83, therefore, the method can quickly and accurately acquire the rice leaf nitrogen accumulation amount information, and overcome the defect that the characteristic wave band of the rice leaf nitrogen accumulation amount is difficult to determine due to the spectrum superposition effect caused by complex rice componentsAnd the inversion precision of the nitrogen accumulation of the rice leaves is greatly improved, and the method is suitable for large-scale popularization and application.
4. The invention discloses a remote sensing inversion method of rice leaf nitrogen accumulation, which comprises the following steps: measuring the canopy reflectance of the rice; measuring the leaf nitrogen accumulation of the rice and taking the logarithm with the base 10 to obtain the logarithm with the base 10 of the leaf nitrogen 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 reflectivity of the canopy as input data and taking a logarithmic value of the accumulated quantity of the leaf nitrogen with the base 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; training an extreme random tree model by taking the canopy reflectivity as input data and taking a logarithmic value of the leaf nitrogen accumulation amount with the base of 10 as an output result and adopting an adjusted value of model parameters to obtain a rice leaf nitrogen accumulation amount remote sensing inversion model, inspecting the model, and performing R2Above 0.83, 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 rice leaf nitrogen accumulation remote sensing inversion method of the 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 nitrogen accumulation remote sensing inversion model aiming at the requirement of estimating the rice leaf nitrogen accumulation based on hyperspectrum, and overcoming the difficulties that the characteristic wave band of the rice leaf nitrogen 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, wherein the rice leaf nitrogen 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 _ split':0.02021840744032572, 'min _ input _ decode': 0.0, 'ccp _ alpha':0.0, 'min _ samples _ split':2, 'max _ decode': None, 'criterion': 'mse', 'min _ weight _ fragment _ leaf':0.0, 'min _ samples _ leaf':1, 'split': range ',' max _ defects ': auto', and 'max _ leaf _ nodes'.
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 nitrogen accumulation of m sampling points of the rice, the m sampling points are uniformly distributed in a rice planting area, and the canopy reflectivity is the canopy reflectivity 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 38, the 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 rice leaf nitrogen accumulation, which comprises the following steps:
(1) measuring the canopy reflectance of the rice;
(2) measuring the leaf nitrogen accumulation amount of the rice, and obtaining a logarithm taking 10 as a base of the leaf nitrogen accumulation amount by taking a logarithm taking 10 as a base of the leaf nitrogen 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 leaf nitrogen accumulation amount with the base of 102Changing the value of a model parameter, R, of the extreme stochastic tree model2The larger the variation of (A), the more important the model parameters areThe larger the importance is, arranging the model parameters 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 nitrogen 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 nitrogen accumulation amount which is based on 10 as the output result, adopting the tuning value of the model parameter, obtaining a rice leaf nitrogen accumulation amount remote sensing inversion model after the training of the extreme random tree model is finished, storing the rice leaf nitrogen accumulation amount remote sensing inversion model by using a save method, and loading the rice leaf nitrogen accumulation amount remote sensing inversion model for use by using a load method if the rice leaf nitrogen 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 leaf nitrogen accumulation amount of the rice may specifically include any suitable method, and preferably, in the step (2), the step of measuring the leaf nitrogen accumulation amount of the rice 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 total nitrogen content of the leaves, and multiplying the dry matter weight of the leaves in unit area by the total nitrogen content of the leaves to obtain the leaf nitrogen accumulation amount.
In the step (2), the water-removing and the drying can adopt any suitable conditions, the total nitrogen content of the leaves can be measured by any suitable method, and 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 total nitrogen content of the leaves is measured by a semi-micro Kjeldahl method.
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_split','min_impurity_decrease','ccp_alpha','min_samples_split','max_depth','criterion','min_weight_fraction_leaf','min_samples_leaf','splitter','max_features','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_split':0.02021840744032572,'min_impurity_decrease':0.0,'ccp_alpha':0.0,'min_samples_split':2,'max_depth':None,'criterion':'mse','min_weight_fraction_leaf':0.0,'min_samples_leaf':1,'splitter':'random','max_features':'auto','max_leaf_nodes':None。
in order to make the precision of the rice leaf nitrogen accumulation remote sensing inversion model higher, 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 nitrogen accumulation amounts of the plurality of sampling points are determined, 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 nitrogen accumulation amount of the rice is specifically to measure the leaf nitrogen 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 38, 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 nitrogen accumulation of the rice leaves in the embodiment is based on actually measured hyperspectral data, adopts rice canopy reflectivity spectrum data and rice leaf nitrogen accumulation data 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, wherein the rice variety is No. 5, and the sampling period is a rice jointing period), and has 48 sampling points, and the 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 38 sampling points is used for model construction, and the data of 10 sampling points is used for model inspection. The flow of the remote sensing inversion method of the rice leaf nitrogen accumulation 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 field angle of 25 degrees is selected, a sensor probe points to a measuring target, namely a canopy, the vertical height of the sensor probe is about 1 meter from the top layer of the canopy, and the ground field rangeThe average value of the reflection spectrum measured 10 times is taken as the spectrum data of the sampling point when the diameter of the circle is 0.44 meter and the sunlight is faced. 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 nitrogen 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, the overground part plants are taken 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 (LD) of the leaves in unit area according to the sampling coverage area, and the unit is g/m2。
Pulverizing dry leaves, determining total Nitrogen Content (NC) of leaves by half-micro Kjeldahl method in unit of (% weight), and calculating leaf nitrogen accumulation LNA by the following formula to obtain leaf nitrogen accumulation in unit of g/m2。
LNA＝LD×NC。
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 nitrogen accumulation data, wherein the preprocessing comprises removing paired rice canopy reflectivity data and rice leaf nitrogen accumulation data containing a missing value and a null value. In order to reduce memory occupation during model training and improve calculation efficiency and model precision, data distribution conversion is carried out on the rice leaf nitrogen accumulation data, namely, a logarithmic value of the rice leaf nitrogen accumulation data with the base 10 is calculated, and a logarithmic value of the leaf nitrogen accumulation with the base 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, a default value of a 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 logarithmic value of the leaf nitrogen accumulation amount with the base of 102Then 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 logarithmic value data with the base of 10 of the corresponding leaf nitrogen accumulation amount, the model parameter tuning rank matrix obtained by calculation is as follows:
Params＝{'min_impurity_split','min_impurity_decrease','ccp_alpha','min_samples_split','max_depth','criterion','min_weight_fraction_leaf','min_samples_leaf','splitter','max_features','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 order matrix according to the obtained model parameters, optimizing data used for modeling, including actually-measured canopy reflectivity data and logarithmic value data with the base of 10 of the corresponding actually-measured leaf nitrogen accumulation amount, taking the actually-measured canopy reflectivity data as input data, taking the logarithmic value data with the base of 10 of the actually-measured leaf nitrogen accumulation amount as an output result, training an extreme random tree model, and sequentially optimizing the model parameters according to the model parameter optimizing order matrix to obtain complete parameters and values of the model, wherein the data comprises the following data:
'min_impurity_split':0.02021840744032572,'min_impurity_decrease':0.0,'ccp_alpha':0.0,'min_samples_split':2,'max_depth':None,'criterion':'mse','min_weight_fraction_leaf':0.0,'min_samples_leaf':1,'splitter':'random','max_features':'auto','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 the content of the first and second substances,
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 10 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 (a logarithmic value with the accumulated quantity of leaf nitrogen being 10 as the base), and obtaining a result shown in FIG. 3, wherein R of the model is2Is 0.8335. Model R using default parameters2Is 0.6144.
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 nitrogen accumulation remote sensing inversion model based on actual measurement hyperspectral remote sensing data, can quickly and accurately acquire the rice leaf nitrogen accumulation information based on the actual measurement rice canopy reflectivity data and the rice leaf nitrogen accumulation data collected on the spot, overcomes the difficulty that the characteristic wave band of the rice leaf nitrogen accumulation is difficult to determine due to the spectrum superposition effect caused by complex rice components, and adjusts an order matrix by constructing model parameters, adjusts and optimizes the model parameters by using a trial and error method, effectively reduces the phenomenon of linear model overfitting, greatly improves the inversion precision of the rice leaf nitrogen accumulation, is suitable for the quantitative inversion planting of the rice leaf nitrogen accumulation in different ecological regions, different varieties and main growth periods, thereby obtaining the states of rice nitrogen nutrition, physiological states and water and fertilizer supply, and improving the acquisition efficiency of growth information in the rice cultivation process, 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 inversion of the nitrogen accumulation of the rice leaves 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 nitrogen accumulation of the rice leaves are considered, and the rice leaf nitrogen accumulation information contained in different wave bands in remote sensing data is fully utilized to carry out the inversion of the nitrogen accumulation of the rice leaves;
(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 reflectivity of 350-2500 nm and the rice leaf nitrogen accumulation is constructed, overfitting phenomena caused by using models such as linear regression can be effectively reduced, and the speed and efficiency of rice leaf nitrogen accumulation 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 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 nitrogen accumulation amount of the rice leaves is simple and convenient to calculate, is suitable for remote sensing quantitative inversion of the nitrogen accumulation amount of the rice leaves in different ecological regions, different varieties and different growth periods, can accurately invert the nitrogen accumulation amount of the rice leaves, can quickly acquire the information of nitrogen nutrition, physiological conditions, growth vigor and the like of the rice, and provides scientific data for water and fertilizer operational management of rice planting and cultivation.
In conclusion, the rice leaf nitrogen accumulation remote sensing inversion model can quickly and accurately acquire the rice leaf nitrogen accumulation information, overcomes the difficulty that the characteristic wave band of the rice leaf nitrogen accumulation is difficult to determine due to the spectrum superposition effect caused by complex rice components, greatly improves the precision of the rice leaf nitrogen 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. The rice leaf nitrogen accumulation remote sensing inversion model is characterized in that the rice leaf nitrogen 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 _ split':0.02021840744032572, 'min _ input _ decode': 0.0, 'ccp _ alpha':0.0, 'min _ samples _ split':2, 'max _ decode': None, 'criterion': 'mse', 'min _ weight _ fragment _ leaf':0.0, 'min _ samples _ leaf':1, 'split': range ',' max _ defects ': auto', and 'max _ leaf _ nodes'.
2. The rice leaf nitrogen 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 rice and logarithmic values of leaf nitrogen accumulation with the base of 10, 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.
3. The remote sensing inversion model of rice leaf nitrogen accumulation as claimed in claim 2, wherein m is 38, the n characteristic wave bands are 2151 characteristic wave bands, and the 2151 characteristic wave bands are from 350nm to 2500 nm.
4. A remote sensing inversion method for rice leaf nitrogen accumulation is characterized by comprising the following steps:
(1) measuring the canopy reflectance of the rice;
(2) measuring the leaf nitrogen accumulation amount of the rice, and obtaining a logarithm taking 10 as a base of the leaf nitrogen accumulation amount by taking a logarithm taking 10 as a base of the leaf nitrogen 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 leaf nitrogen 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 nitrogen 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 nitrogen accumulation amount which is based on 10 as the output result, adopting the tuning value of the model parameter, obtaining a rice leaf nitrogen accumulation amount remote sensing inversion model after the training of the extreme random tree model is finished, storing the rice leaf nitrogen accumulation amount remote sensing inversion model by using a save method, and loading the rice leaf nitrogen accumulation amount remote sensing inversion model for use by using a load method if the rice leaf nitrogen accumulation amount remote sensing inversion model is required to be used.
5. The rice leaf nitrogen accumulation remote sensing inversion method 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 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 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 nitrogen accumulation amount according to claim 4, wherein in the step (2), the step of measuring the rice leaf nitrogen 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 total nitrogen content of the leaves, and multiplying the dry matter weight of the leaves in unit area by the total nitrogen content of the leaves to obtain the leaf nitrogen accumulation amount.
7. The remote sensing inversion method of the rice leaf nitrogen accumulation amount according to claim 6, characterized in that 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 a half-micro Kjeldahl method is adopted for measuring the total nitrogen content of the leaves.
8. The remote sensing inversion method for the rice leaf nitrogen accumulation amount according to claim 4, wherein in the step (3), the model parameter tuning rank matrix is as follows:
Params＝{'min_impurity_split','min_impurity_decrease','ccp_alpha','min_samples_split','max_depth','criterion','min_weight_fraction_leaf','min_samples_leaf','splitter','max_features','max_leaf_nodes'}。
9. the remote sensing inversion method of rice leaf nitrogen accumulation according to claim 8, wherein in the step (4), the tuning values of the model parameters are as follows:
'min_impurity_split':0.02021840744032572,'min_impurity_decrease':0.0,'ccp_alpha':0.0,'min_samples_split':2,'max_depth':None,'criterion':'mse','min_weight_fraction_leaf':0.0,'min_samples_leaf':1,'splitter':'random','max_features':'auto','max_leaf_nodes':None。
10. the remote sensing inversion method of rice leaf nitrogen accumulation according to claim 4, wherein in the step (1), the step of measuring the rice canopy reflectance is specifically measuring 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 nitrogen accumulation amount of the rice is specifically to measure the leaf nitrogen accumulation amount of the m sampling points.
11. The remote sensing inversion method of rice leaf nitrogen accumulation amount according to claim 10, wherein in the step (1), the m is 38, 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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