CN112577908A - Rice leaf protein nitrogen content remote sensing inversion model and method based on TheilSen regression algorithm - Google Patents
Rice leaf protein nitrogen content remote sensing inversion model and method based on TheilSen regression algorithm Download PDFInfo
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Abstract
The invention provides a rice leaf protein nitrogen content remote sensing inversion model based on a TheilSen regression algorithm, which is a TheilSen regression model of Python language and further provides model parameters of the TheilSen regression model. Also provides a rice leaf protein nitrogen content remote sensing inversion method based on the TheilSen regression algorithm. The rice leaf protein nitrogen content remote sensing inversion model based on the TheilSen regression algorithm can quickly and accurately acquire the rice leaf protein nitrogen content information, overcomes the difficulty that the characteristic wave band of the rice leaf protein nitrogen content is difficult to determine due to the spectrum superposition effect caused by complex rice components, reduces the interference of outliers in modeling data to the model, and accordingly improves the accuracy of the rice leaf protein nitrogen content inversion model.
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 protein nitrogen content, and specifically relates to a rice leaf protein nitrogen content remote sensing inversion model and method based on TheilSen regression algorithm.
Background
The rice leaf protein nitrogen content is the content of protein nitrogen elements in rice leaves, is closely related to the nutritional state and the physiological state of rice nitrogen (Zhao Shijie, Shi-nan, Dong Xin-Chun. plant physiology experiment guidance. Beijing: Chinese agricultural science and technology publishing company, 2002:89-95), is an important index reflecting the nutritional state of rice nitrogen, the growth vigor of rice and the absorption and utilization efficiency of rice to nitrogen fertilizer, and reflects the physiological and growth vigor of rice and the response state of cultivation management measures of rice on the transportation and management of fertilizer and water in production (Yangjie, Tianyong, Zhuyan, etc. A novel vegetation index for estimating the protein nitrogen content in upper leaves of rice [ J ] Chinese agricultural science, 2009,42(8): 2695) 2706).
The method has the advantages that the protein nitrogen content of the rice leaves is monitored, the yield and the quality of rice production can be guaranteed, 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 large amount of nitrogen fertilizer application is relieved, so that remarkable economic and social benefits (aging, Tianyongtao, yaowa, and the like) are generated, and the research on the rice nitrogen topdressing regulation and control effect based on the canopy reflection spectrum [ J ] Chinese agricultural science, 2010,43(20): 4149-. The traditional method for monitoring the nitrogen content of the rice leaf protein mainly adopts destructive sampling, needs to be carried out indoors, wastes time and labor in the determination process, is poor in timeliness, cannot acquire the nitrogen content of the rice leaf protein 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 protein nitrogen content of the rice leaf and the like (Zhoudouqin. monitoring of rice nitrogen nutrition and grain quality based on the canopy reflection spectrum [ D ]. Nanjing agriculture university, 2007). Currently, hyperspectrum is used for monitoring the growth state of rice in crop growth monitoring. With the development and popularization of the spectrum technology, the hyperspectral data can quickly and rapidly acquire the nitrogen content information of the rice leaf protein, 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 protein nitrogen content to construct an inversion model. In the process of constructing the rice leaf protein nitrogen content 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 wave bands are partially overlapped, the determination of the rice leaf protein nitrogen content characteristic spectrum is difficult, and meanwhile, the distribution of the leaf protein nitrogen content and hyperspectral data is easy to generate outliers due to the complex rice leaf components, so that the construction of the linear model is influenced. In addition, the rapid processing of the hyperspectral data becomes an urgent technical problem to be solved for estimating the nitrogen content of the rice leaf protein based on the hyperspectral data.
Therefore, it is desirable to provide a rice leaf protein nitrogen content remote sensing inversion model, which can quickly and accurately acquire rice leaf protein nitrogen content information, overcome the difficulty that the characteristic wave band of rice leaf protein nitrogen content is difficult to determine due to the spectrum superposition effect caused by complex rice components, and reduce the interference of outliers in modeling data on the model, thereby improving the accuracy of the rice leaf protein nitrogen content inversion model.
Disclosure of Invention
In order to overcome the defects in the prior art, one object of the present invention is to provide a rice leaf protein nitrogen content remote sensing inversion model based on a TheilSen regression algorithm, which can quickly and accurately obtain rice leaf protein nitrogen content information, overcome the difficulty that the characteristic wave band of rice leaf protein nitrogen content is difficult to determine due to the spectrum superposition effect caused by complex rice components, and reduce the interference of outliers in modeling data on the model, thereby improving the accuracy of the rice leaf protein nitrogen content inversion model, and being suitable for large-scale popularization and application.
The invention also aims to provide a rice leaf protein nitrogen content remote sensing inversion model based on the TheilSen regression algorithm, which has the advantages of ingenious design, simple and convenient calculation, easy realization and low cost, and is suitable for large-scale popularization and application.
The invention also aims to provide a rice leaf protein nitrogen content remote sensing inversion method based on the TheilSen regression algorithm, which can quickly and accurately acquire rice leaf protein nitrogen content information, overcome the difficulty that the characteristic wave band of the rice leaf protein nitrogen content is difficult to determine due to the spectrum superposition effect caused by complex rice components, and reduce the interference of outliers in modeling data on a model, thereby improving the inversion precision of the rice leaf protein nitrogen content and being suitable for large-scale popularization and application.
The invention also aims to provide a rice leaf protein nitrogen content remote sensing inversion method based on the TheilSen regression algorithm, 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, a rice leaf protein nitrogen content remote sensing inversion model based on a TheilSen regression algorithm is provided, and is characterized in that the rice leaf protein nitrogen content remote sensing inversion model based on the TheilSen regression algorithm is a TheilSen regression model in Python language, and model parameters of the TheilSen regression model are as follows: 'max _ iter':358, 'max _ suppression': 6539, 'tol':5.488139551138208, 'n _ subsamples': None, 'n _ jobs': None, 'fit _ interrupt': True, 'copy _ X' and True.
Preferably, the TheilSen regression model is trained by using a rice data set, the data set comprises canopy reflectances and leaf protein nitrogen contents of m sample points of the rice, the m sample points are uniformly distributed in a rice planting area, and the canopy reflectivity is the canopy reflectivity of n characteristic wave bands.
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 rice leaf protein nitrogen content remote sensing inversion method based on TheilSen regression algorithm, which is characterized by comprising the following steps:
(1) measuring the canopy reflectance of the rice;
(2) measuring leaf protein nitrogen content of the rice;
(3) calculating by using the canopy reflectivity as input data and adopting a TheilSen regression model of Python language to obtain an inversion value, and calculating a decision coefficient R according to the inversion value and the leaf protein nitrogen content2Changing the value of the model parameter of the TheilSen regression model, 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;
(4) training the TheilSen regression model by taking the canopy reflectivity as the input data and the leaf protein nitrogen content as the output result, and sequentially optimizing the model parameters according to the model parameter optimization order matrix to obtain the optimization values of the model parameters;
(5) the canopy reflectivity is used as the input data, the leaf protein nitrogen content is used as the output result, the adjusted value of the model parameter is adopted to train the TheilSen regression model, after the TheilSen regression model is trained, a rice leaf protein nitrogen content remote sensing inversion model based on the TheilSen regression algorithm is obtained, the rice leaf protein nitrogen content remote sensing inversion model based on the TheilSen regression algorithm is stored by using a save method, and if the rice leaf protein nitrogen content remote sensing inversion model based on the TheilSen regression algorithm is needed, the rice leaf protein nitrogen content remote sensing inversion model based on the TheilSen regression algorithm is loaded by using a load method for use.
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 leaf protein nitrogen content of the rice specifically comprises:
collecting the leaves of the rice, deactivating enzyme, drying to constant weight to obtain dry leaves, crushing the dry leaves, and determining the protein nitrogen content of the leaves.
More preferably, in the step (2), the water-removing temperature is 105 ℃, the water-removing time is 20-30 minutes, the drying temperature is 80-90 ℃, and the measuring of the leaf protein nitrogen content adopts a trichloroacetic acid solution precipitation method and a semi-micro Kjeldahl method.
Preferably, in the step (3), the model parameter tuning rank matrix is:
Params＝{'max_iter','max_subpopulation','tol','n_subsamples','n_jobs','fit_intercept','copy_X'}。
preferably, in the step (4), the optimized values of the model parameters are:
'max_iter':358,'max_subpopulation':6539,'tol':5.488139551138208,'n_subsamples':None,'n_jobs':None，'fit_intercept':True,'copy_X':True。
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 protein nitrogen content of the rice is specifically to measure the leaf protein nitrogen content 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 rice leaf protein nitrogen content remote sensing inversion model based on the TheilSen regression algorithm is a TheilSen regression model of Python language, and the model parameters of the TheilSen regression model are as follows: ' max _ iter ':358, ' max _ suppression ':6539, ' tol ':5.488139551138208, ' n _ samples ': None, ' n _ jobs ': None, ' fit _ interrupt ': True, ' copy _ X ': True, the model was examined, R _ sub _ sampling ': True2Above 0.85, the method can quickly and accurately acquire the nitrogen content information of the rice leaf protein, overcomes the difficulty that the characteristic wave band of the nitrogen content of the rice leaf protein is difficult to determine due to the spectrum superposition effect caused by complex rice components, and reduces the interference of outliers in modeling data on a model, thereby improving the precision of the rice leaf protein nitrogen content inversion model and being suitable for large-scale popularization and application.
2. The rice leaf protein nitrogen content remote sensing inversion model based on the TheilSen regression algorithm is a TheilSen regression model of Python language, and the model parameters of the TheilSen regression model are as follows: ' max _ iter ':358, ' max _ suppression ':6539, ' tol ':5.488139551138208, ' n _ samples ': None, ' n _ jobs ': None, ' fit _ interrupt ': True, ' copy _ X ': True, the model was examined, R _ sub _ sampling ': True2Above 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 relates to a rice leaf protein nitrogen content remote sensing inversion method based on TheilSen regression algorithm, which comprises the following steps: measuring the canopy reflectance of the rice; measuring leaf protein nitrogen content of rice: calculating by using the reflectivity of the canopy as input data and adopting a TheilSen regression model of Python language to determine a coefficient R2Constructing a model parameter tuning order matrix; training a TheilSen regression model by taking the canopy reflectivity as input data and the leaf protein nitrogen content as an output result, and sequentially optimizing the model parameters according to the model parameter optimization order matrix to obtain the model parameter optimization values; with a canopy reflectivityTraining a TheilSen regression model by using leaf protein nitrogen content as an output result and adopting an adjusted value of model parameters for inputting data to obtain a rice leaf protein nitrogen content remote sensing inversion model based on the TheilSen regression algorithm, inspecting the model, and R2Above 0.85, the method can quickly and accurately acquire the nitrogen content information of the rice leaf protein, overcomes the difficulty that the characteristic wave band of the nitrogen content of the rice leaf protein is difficult to determine due to the spectrum superposition effect caused by complex rice components, and reduces the interference of outliers in modeling data on a model, thereby improving the inversion precision of the nitrogen content of the rice leaf protein, and being suitable for large-scale popularization and application.
4. The invention relates to a rice leaf protein nitrogen content remote sensing inversion method based on TheilSen regression algorithm, which comprises the following steps: measuring the canopy reflectance of the rice; measuring leaf protein nitrogen content of rice: calculating by using the reflectivity of the canopy as input data and adopting a TheilSen regression model of Python language to determine a coefficient R2Constructing a model parameter tuning order matrix; training a TheilSen regression model by taking the canopy reflectivity as input data and the leaf protein nitrogen content as an output result, and sequentially optimizing the model parameters according to the model parameter optimization order matrix to obtain the model parameter optimization values; training a TheilSen regression model by using the canopy reflectivity as input data and the leaf protein nitrogen content as an output result and adopting the adjusted values of model parameters to obtain a rice leaf protein nitrogen content remote sensing inversion model based on the TheilSen regression algorithm, 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 a rice leaf protein nitrogen content remote sensing inversion method based on theilSen regression algorithm.
FIG. 2 is a schematic diagram of a model building process of the embodiment shown in FIG. 1.
FIG. 3 is a graph showing the results of model tests of the embodiment shown in FIG. 1, wherein the measured values and the predicted values are in weight percent (% by weight).
Detailed Description
The invention provides a rice leaf protein nitrogen content remote sensing inversion model based on a TheilSen regression algorithm aiming at the requirements of estimating the rice leaf protein nitrogen content based on hyperspectrum, overcoming the difficulties that the characteristic wave band of the rice leaf protein nitrogen content is difficult to determine and the characteristic wave band of hyperspectral data is time-consuming and labor-consuming in screening due to complex rice components, wherein the rice leaf protein nitrogen content remote sensing inversion model based on the TheilSen regression algorithm is a TheilSen regression model of Python language, and the model parameters of the TheilSen regression model are as follows: 'max _ iter':358, 'max _ suppression': 6539, 'tol':5.488139551138208, 'n _ subsamples': None, 'n _ jobs': None, 'fit _ interrupt': True, 'copy _ X' and True.
The TheilSen regression model can be trained by any suitable data set, preferably, the TheilSen regression model is trained by a data set of rice, the data set comprises canopy reflectivities and leaf protein nitrogen contents 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 wave bands. The rice planting area can be a plurality of ecological points and a plurality of varieties of rice planting areas.
M and n are positive integers, which can be determined according to needs, and more preferably, m is 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 rice leaf protein nitrogen content remote sensing inversion method based on the TheilSen regression algorithm, which comprises the following steps:
(1) measuring the canopy reflectance of the rice;
(2) measuring leaf protein nitrogen content of the rice;
(3) with the saidThe canopy reflectivity is input data, a TheilSen regression model of Python language is adopted for calculation to obtain an inversion value, and a determination coefficient R is calculated according to the inversion value and the leaf protein nitrogen content2Changing the value of the model parameter of the TheilSen regression model, 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;
(4) training the TheilSen regression model by taking the canopy reflectivity as the input data and the leaf protein nitrogen content as the output result, and sequentially optimizing the model parameters according to the model parameter optimization order matrix to obtain the optimization values of the model parameters;
(5) the canopy reflectivity is used as the input data, the leaf protein nitrogen content is used as the output result, the adjusted value of the model parameter is adopted to train the TheilSen regression model, after the TheilSen regression model is trained, a rice leaf protein nitrogen content remote sensing inversion model based on the TheilSen regression algorithm is obtained, the rice leaf protein nitrogen content remote sensing inversion model based on the TheilSen regression algorithm is stored by using a save method, and if the rice leaf protein nitrogen content remote sensing inversion model based on the TheilSen regression algorithm is needed, the rice leaf protein nitrogen content remote sensing inversion model based on the TheilSen regression algorithm is loaded by using a load method for use.
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 protein nitrogen content of the rice may specifically include any suitable method, and preferably, in the step (2), the step of measuring the leaf protein nitrogen content of the rice specifically includes:
collecting the leaves of the rice, deactivating enzyme, drying to constant weight to obtain dry leaves, crushing the dry leaves, and determining the protein nitrogen content of the leaves.
In the step (2), the water-removing and the drying can adopt any suitable conditions, the nitrogen content of the leaf protein 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 nitrogen content of the leaf protein is measured by trichloroacetic acid solution precipitation and 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＝{'max_iter','max_subpopulation','tol','n_subsamples','n_jobs','fit_intercept','copy_X'}。
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:
'max_iter':358,'max_subpopulation':6539,'tol':5.488139551138208,'n_subsamples':None,'n_jobs':None，'fit_intercept':True,'copy_X':True。
in order to enable the precision of the rice leaf protein nitrogen content remote sensing inversion model based on the TheilSen regression algorithm to be higher, a plurality of sampling points of a rice planting area can be selected, and the canopy reflectivity of a plurality of characteristic wave bands of the sampling points and the leaf protein nitrogen content of the sampling points are measured, preferably, in the step (1), the step of measuring the canopy reflectivity of the rice is specifically to measure the canopy reflectivity 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 wave bands; in the step (2), the step of measuring the leaf protein nitrogen content of the rice is specifically to measure the leaf protein nitrogen content 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 rice leaf protein nitrogen content remote sensing inversion method based on the TheilSen regression algorithm in the embodiment is based on actually measured hyperspectral data, adopts rice canopy reflectance spectrum data and rice leaf protein nitrogen content data collected by a rice planting area (Huaian area rice and wheat planting base of agricultural science research institute of Huaian city, Jiangsu province, the rice variety is No. 5, and the sampling period is the rice jointing period), and has 48 sampling points, wherein 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 process of the rice leaf protein nitrogen content remote sensing inversion method based on the TheilSen regression algorithm 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 board is also corrected, and the reflectivity of the standard white board used in the embodiment is 99%. The measured spectrum data is checked by using field Spec Pro portable field hyperspectral radiometer random software RS3 or ViewSpec Pro software, abnormal spectrum files are removed, interpolation calculation is carried out on the spectrum data to obtain spectrum data with the range of 350 nm-2500 nm and the resolution of 1nm, the average value of parallel sampling spectra of the spectrum is calculated, and finally, the spectrum data is exported and stored as ASCII files.
2. Determination of protein nitrogen content in rice leaf
Collecting 6 plants on the overground part of the rice uniformly distributed in the spectral measurement field of each sampling point, wrapping the plants by using absorbent paper, bringing the plants back to a laboratory, separating leaves, deactivating enzymes at 105 ℃ for 20 minutes, drying the leaves at 85 ℃ until the weight of the leaves is constant, obtaining dry leaves, crushing the dry leaves, precipitating protein by using trichloroacetic acid solution, and measuring the nitrogen content in the precipitate by adopting a Kjeldahl method, namely the nitrogen content (PNC) of the leaf protein, which is expressed in terms of weight percent (Zhaojie, Shi-ann, Dong-New-pure plant physiology experiment guide, Beijing, China agricultural science and technology publisher, 2002:89-95, Liheng, plant physiology and biochemistry experiment principle and technology, advanced education publisher, 2000: 186).
3. Model construction
The model construction is carried out by adopting a TheilSen regression 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 protein nitrogen content data, wherein the preprocessing comprises removing paired rice canopy reflectivity data and rice leaf protein nitrogen content data containing a missing value and a null value.
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 when the model training is iterated each time, the training data set is divided into 5 parts, and the models are trained in sequence.
3.5 construction of model parameter tuning rank matrix
In the invention, the tuning of the model parameters in the model training process is very important, and in order to ensure that the best model tuning is obtained as much as possible, a trial-and-error method is used for tuning the model parameters. The present invention uses the coefficient of determination R2(R2The closer to 1, the better) as the test parameter, a parameter rank matrix for evaluating the weight of the model parameter is constructed. According to a training data set, firstly, the default value of the model parameter is used for calculation to obtain an inversion value, and according to the inversion value and the leaf protein nitrogen content, a decision coefficient R is calculated2Then 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 and the corresponding leaf protein nitrogen content data in the training data set, the model parameter tuning order matrix obtained by calculation is as follows:
Params＝{'max_iter','max_subpopulation','tol','n_subsamples','n_jobs','fit_intercept','copy_X'}。
wherein, the change of other model parameters of the TheilSen regression model can not cause the precision change of the TheilSen regression model.
3.6 model construction
Adjusting the order matrix according to the obtained model parameters, training a TheilSen regression model by using data used for modeling, including actually measured canopy reflectivity data and corresponding actually measured leaf protein nitrogen content data, as input data, and as output results, sequentially adjusting the model parameters according to the model parameter adjustment order matrix to obtain complete parameters and values of the model, wherein the data comprises the actually measured canopy reflectivity data and the corresponding actually measured leaf protein nitrogen content data, and the model parameters are adjusted as follows:
'max_iter':358,'max_subpopulation':6539,'tol':5.488139551138208,'n_subsamples':None,'n_jobs':None，'fit_intercept':True,'copy_X':True。
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.
3.7 model test
Using 10 sampling points except 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 (leaf protein nitrogen content), and obtaining a result shown in figure 3, wherein R of the model is2Is 0.8864. Model R using default parameters2Is 0.7013.
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 TheilSen regression model of Python is called through the Matlab software.
Therefore, the invention provides a new rice leaf protein nitrogen content remote sensing inversion model based on TheilSen regression algorithm based on actual measurement hyperspectral remote sensing data, can quickly and accurately acquire the rice leaf protein nitrogen content information based on the actual measurement rice canopy reflectance data and the rice leaf protein nitrogen content data collected on the spot, overcomes the difficulty that the characteristic wave band of the rice leaf protein nitrogen content is difficult to determine due to the spectrum superposition effect caused by the complex rice components, optimizes the model parameters by constructing a model parameter optimization order matrix and using a trial and error method to optimize the model parameters, effectively reduces the phenomenon of linear model overfitting, greatly improves the inversion precision of the rice leaf protein nitrogen content, is suitable for quantitative inversion of the rice leaf protein nitrogen content in different ecological regions, different varieties and main growth periods, thereby acquiring the rice nitrogen nutrition, physiological state and water fertilizer supply 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 TheilSen regression model used in the invention is suitable for the inversion of the nitrogen content of the rice leaf protein based on the hyperspectrum, on the basis of comprehensively considering the information of the 350-2500 nm waveband range of the hyperspectrum, 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 waveband of the nitrogen content of the rice leaf protein are considered, the information of the nitrogen content of the rice leaf protein contained in different wavebands in the remote sensing data is fully utilized, and the inversion of the nitrogen content of the rice leaf protein is carried out;
(2) the TheilSen regression machine learning algorithm is used for constructing a model of the reflectivity of 350-2500 nm and the protein nitrogen content of the rice leaf, so that the phenomenon of linear regression collapse caused by an outlier can be effectively reduced, the robustness and stability of the outlier are improved, and the speed and efficiency of the rice leaf protein nitrogen content 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 content of the rice leaf protein is simple and convenient to calculate, is suitable for remote sensing quantitative inversion of the nitrogen content of the rice leaf protein in different ecological regions, different varieties and different growth periods, can accurately invert the nitrogen content of the rice leaf protein, can quickly acquire the nitrogen nutrition, physiological conditions, growth vigor and other information of the rice, and provides scientific data for water and fertilizer operation management of rice planting and cultivation.
In conclusion, the rice leaf protein nitrogen content remote sensing inversion model based on the TheilSen regression algorithm can quickly and accurately acquire the rice leaf protein nitrogen content information, overcomes the difficulty that the characteristic wave band of the rice leaf protein nitrogen content is difficult to determine due to the spectrum superposition effect caused by complex rice components, and reduces the interference of outliers in modeling data on the model, so that the accuracy of the rice leaf protein nitrogen content inversion model is improved, the design is ingenious, the calculation is simple and convenient, the implementation is easy, the cost is low, and the rice leaf protein nitrogen content remote sensing inversion model is suitable for large-scale popularization and application.
It will thus be seen that the objects of the invention have been fully and effectively accomplished. The functional and structural principles of the present invention have been shown and described in the embodiments, and the embodiments may be modified without departing from the principles. Therefore, this invention includes all modifications encompassed within the spirit and scope of the claims.
Claims (11)
1. A rice leaf protein nitrogen content remote sensing inversion model based on a TheilSen regression algorithm is characterized in that the rice leaf protein nitrogen content remote sensing inversion model based on the TheilSen regression algorithm is a TheilSen regression model of Python language, and model parameters of the TheilSen regression model are as follows: 'max _ iter':358, 'max _ suppression': 6539, 'tol':5.488139551138208, 'n _ subsamples': None, 'n _ jobs': None, 'fit _ interrupt': True, 'copy _ X' and True.
2. The TheilSen regression algorithm-based rice leaf protein nitrogen content remote sensing inversion model as claimed in claim 1, wherein the TheilSen regression model is trained by using a rice data set, the data set comprises canopy reflectances of m sample points of the rice and leaf protein nitrogen content, the m sample points are uniformly distributed in a rice planting area, and the canopy reflectivity is the canopy reflectivity of n characteristic bands.
3. The TheilSen regression algorithm-based remote sensing inversion model of rice leaf protein nitrogen content according to claim 2, wherein m is 38, the n characteristic bands are 2151 characteristic bands, and the 2151 characteristic bands are from 350nm to 2500 nm.
4. A rice leaf protein nitrogen content remote sensing inversion method based on TheilSen regression algorithm is characterized by comprising the following steps:
(1) measuring the canopy reflectance of the rice;
(2) measuring leaf protein nitrogen content of the rice;
(3) calculating by using the canopy reflectivity as input data and adopting a TheilSen regression model of Python language to obtain an inversion value, and calculating a decision coefficient R according to the inversion value and the leaf protein nitrogen content2Changing the value of the model parameter of the TheilSen regression model, 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;
(4) training the TheilSen regression model by taking the canopy reflectivity as the input data and the leaf protein nitrogen content as the output result, and sequentially optimizing the model parameters according to the model parameter optimization order matrix to obtain the optimization values of the model parameters;
(5) the canopy reflectivity is used as the input data, the leaf protein nitrogen content is used as the output result, the adjusted value of the model parameter is adopted to train the TheilSen regression model, after the TheilSen regression model is trained, a rice leaf protein nitrogen content remote sensing inversion model based on the TheilSen regression algorithm is obtained, the rice leaf protein nitrogen content remote sensing inversion model based on the TheilSen regression algorithm is stored by using a save method, and if the rice leaf protein nitrogen content remote sensing inversion model based on the TheilSen regression algorithm is needed, the rice leaf protein nitrogen content remote sensing inversion model based on the TheilSen regression algorithm is loaded by using a load method for use.
5. The TheilSen regression algorithm-based rice leaf protein nitrogen content remote sensing inversion method of claim 4, wherein in the step (1), the measurement is performed by using a hyperspectral radiometer, the measurement time is 10: 00-14: 00, the hyperspectral radiometer uses a lens with a field angle of 25 degrees, a sensor probe of the portable field hyperspectral radiometer is vertically directed at the canopy of the rice and has a vertical height of 1 meter from the top layer of the canopy, the ground field range of the sensor probe has a diameter of 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 95-99%.
6. The remote sensing inversion method of rice leaf protein nitrogen content based on TheilSen regression algorithm as claimed in claim 4, wherein in said step (2), said step of measuring said rice leaf protein nitrogen content specifically comprises:
collecting the leaves of the rice, deactivating enzyme, drying to constant weight to obtain dry leaves, crushing the dry leaves, and determining the protein nitrogen content of the leaves.
7. The TheilSen regression algorithm-based remote sensing inversion method for rice leaf protein nitrogen content according to claim 6, wherein in the step (2), the water-removing temperature is 105 ℃, the water-removing time is 20-30 minutes, the drying temperature is 80-90 ℃, and the determination of the leaf protein nitrogen content adopts trichloroacetic acid solution precipitation and a semi-micro Kjeldahl method.
8. The TheilSen regression algorithm-based rice leaf protein nitrogen content remote sensing inversion method of claim 4, wherein in the step (3), the model parameter tuning rank matrix is:
Params＝{'max_iter','max_subpopulation','tol','n_subsamples','n_jobs','fit_intercept','copy_X'}。
9. the remote sensing inversion method for rice leaf protein nitrogen content based on TheilSen regression algorithm as claimed in claim 8, wherein in step (4), the model parameters are adjusted to values:
'max_iter':358,'max_subpopulation':6539,'tol':5.488139551138208,'n_subsamples':None,,'n_jobs':None，'fit_intercept':True,'copy_X':True。
10. the TheilSen regression algorithm-based rice leaf protein nitrogen content remote sensing inversion method according to claim 4, wherein in the step (1), the step of measuring the rice canopy reflectance is specifically to measure the canopy reflectance of m sampling points 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 protein nitrogen content of the rice is specifically to measure the leaf protein nitrogen content of the m sampling points.
11. The remote sensing inversion method of rice leaf protein nitrogen content based on TheilSen regression algorithm as claimed in claim 10, wherein in step (1), said m is 38, said n characteristic bands are 2151 characteristic bands, and said 2151 characteristic bands are from 350nm to 2500 nm.
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