CN112362809A - Passive attack regression algorithm-based remote sensing inversion model and method for nitrogen content of overground part of rice - Google Patents
Passive attack regression algorithm-based remote sensing inversion model and method for nitrogen content of overground part of rice Download PDFInfo
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Abstract
The invention provides a rice overground part nitrogen content remote sensing inversion model based on a passive attack regression algorithm, which is a passive attack regression model of Python language and further provides model parameters of the passive attack regression model. The remote sensing inversion method of the nitrogen content of the overground part of the rice based on the passive attack regression algorithm is also provided. The remote sensing inversion model of the nitrogen content of the overground part of the rice based on the passive attack regression algorithm can quickly and accurately acquire the nitrogen content information of the overground part of the rice, overcomes the difficulty that the characteristic wave band of the nitrogen content of the overground part of the rice is difficult to determine due to the spectrum superposition effect caused by complex rice components, improves the precision of the inversion model of the nitrogen content of the overground part of the rice, can be continuously trained on the basis of the original model to update the model parameters if sample data is newly added, has the advantages of ingenious design, simple and convenient calculation, easy realization and low cost, and is 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 nitrogen content of rice overground parts, and specifically relates to a remote sensing inversion model and method of the nitrogen content of the rice overground parts based on a passive attack regression algorithm.
Background
The nitrogen content of the overground part of the rice is the content of nitrogen elements in the overground part of the rice, 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-rich condition of the rice (Shore Yonni. research on a rapid and nondestructive acquisition technology of physiological characteristic information of the rice growth [ D ]. Zhejiang university, 2010).
The nitrogen content of the overground part of the rice 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 (aging, Tianyongtao, yaowa, and the like) are generated. The traditional method for monitoring the nitrogen content of the overground part of the rice mainly adopts a destructive sampling method, needs to be measured indoors, wastes time and labor, is poor in timeliness, cannot acquire the nitrogen content of the overground part of the rice 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 the rice growth information such as the nitrogen content of the overground part of the rice (Zhoudouqin. monitoring of the nitrogen nutrition of the rice and the quality of grains 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 overground part of the rice, 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 nitrogen content of the overground part to construct an inversion model. In the process of constructing the rice overground part nitrogen content inversion model, the spectral range determined by the full-waveband spectrometer covers 350-2500 nm, but because the components of the rice are complex, the component spectral characteristic wave bands are partially overlapped, the determination of the rice overground part nitrogen content characteristic spectrum is difficult, and meanwhile, the rapid processing of hyperspectral data becomes an urgent technical problem to be solved for estimating the rice overground part nitrogen content based on the hyperspectral data. In addition, if the sample data is newly added, the parameters of the model need to be recalculated and determined, so that long time is consumed, and inconvenience is brought to popularization and application.
Therefore, it is desirable to provide a remote sensing inversion model of nitrogen content in the paddy rice overground part, which can quickly and accurately acquire the nitrogen content information in the paddy rice overground part, overcome the difficulty that the characteristic wave band of nitrogen content in the paddy rice overground part is difficult to determine due to the spectrum superposition effect caused by complex paddy rice components, improve the precision of the inversion model of nitrogen content in the paddy rice overground part, and if sample data is added, continue training on the basis of the original model so as to update the model parameters.
Disclosure of Invention
In order to overcome the defects in the prior art, one object of the present invention is to provide a remote sensing inversion model of nitrogen content in the paddy rice overground part based on a passive attack regression algorithm, which can quickly and accurately obtain the nitrogen content information in the paddy rice overground part, overcome the difficulty that the characteristic wave band of nitrogen content in the paddy rice overground part is difficult to determine due to the spectrum superposition effect caused by the complex composition of the paddy rice, improve the precision of the inversion model of nitrogen content in the paddy rice overground part, and if sample data is newly added, the model parameters can be continuously trained on the basis of the original model so as to update, so that the present invention is suitable for large-scale popularization.
The invention also aims to provide a remote sensing inversion model of the nitrogen content of the overground part of the rice based on the passive attack 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 remote sensing inversion method of the nitrogen content of the overground part of the rice based on the passive attack regression algorithm, which can quickly and accurately acquire the nitrogen content information of the overground part of the rice, overcome the difficulty that the characteristic wave band of the nitrogen content of the overground part of the rice is difficult to determine due to the spectrum superposition effect caused by complex rice components, improve the inversion precision of the nitrogen content of the overground part of the rice, and if sample data are newly added, the method can be continuously trained on the basis of the original model so as to update model parameters, and is suitable for large-scale popularization and application.
The invention also aims to provide a remote sensing inversion method of the nitrogen content of the overground part of the rice based on the passive attack 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, there is provided a remote sensing inversion model of nitrogen content in the overground part of rice based on a passive attack regression algorithm, which is characterized in that the remote sensing inversion model of nitrogen content in the overground part of rice based on the passive attack regression algorithm is a passive attack regression model in Python language, and the model parameters of the passive attack regression model are as follows: ' loss ' epsilon _ induction ', n _ iter _ no _ change ' 364 ', C ' 9.53859412732987 ', max _ iter ' 36204 ', valid _ fraction ' 0.030410487333966476 ', tol ' 1.9812983181483994, epsilon ' 0.049443180615865376.
Preferably, the passive attack regression model is trained by using a rice data set, the data set includes canopy reflectances and overground 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 a canopy reflectivity of n characteristic bands.
More preferably, m is 36, 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 the nitrogen content of the overground part of rice based on a passive attack regression algorithm, which is characterized by comprising the following steps:
(1) measuring the canopy reflectance of the rice;
(2) measuring the nitrogen content of the overground part of the rice;
(3) calculating by taking the canopy reflectivity as input data and adopting a passive attack regression model of Python language to obtain an inversion value, and calculating a decision coefficient R according to the inversion value and the overground part nitrogen content2Changing the value of the model parameter, R, of the passive attack regression 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 passive attack regression model by taking the canopy reflectivity as the input data and the nitrogen content of the overground part 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) the method comprises the steps of taking the canopy reflectivity as input data, taking the aboveground part nitrogen content as an output result, adopting the adjusted value of the model parameter to train a passive attack regression model, obtaining a remote sensing inversion model of the rice aboveground part nitrogen content based on a passive attack regression algorithm after the passive attack regression model is trained, storing the remote sensing inversion model of the rice aboveground part nitrogen content based on the passive attack regression algorithm by using a save method, and loading the remote sensing inversion model of the rice aboveground part nitrogen content based on the passive attack regression algorithm for use by using a load method if the remote sensing inversion model of the rice aboveground part nitrogen content based on the passive attack regression algorithm 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 nitrogen content in the overground part of the rice specifically comprises:
collecting the overground part plants of the rice, deactivating enzyme, drying to constant weight to obtain dry overground parts, crushing the dry overground parts, and measuring the total nitrogen content of the overground parts to obtain the nitrogen content of the overground parts.
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 half-micro Kjeldahl method is adopted for measuring the total nitrogen content of the overground part.
Preferably, in the step (3), the model parameter tuning rank matrix is:
Params＝{'loss','n_iter_no_change','C','max_iter','validation_fraction','tol','epsilon'}。
preferably, in the step (4), the optimized values of the model parameters are:
'loss'＝'epsilon_insensitive','n_iter_no_change'＝364,'C'＝9.53859412732987,'max_iter'＝36204,'validation_fraction'＝0.030410487333966476,'tol'＝1.9812983181483994,'epsilon'＝0.049443180615865376。
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 nitrogen content of the overground part of the rice is to measure the nitrogen content of the overground part of the m sampling points.
More preferably, in the step (1), the m is 36, 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 nitrogen content of the overground part of the rice based on the passive attack regression algorithm is a passive attack regression model of Python language, and the model parameters of the passive attack regression model are as follows: the model was examined, R 'loss _ induction', 'n _ iter _ no _ change' 364, 'C' 9.53859412732987, 'max _ iter' 36204, 'differentiation _ fraction' 0.030410487333966476, 'tol' 1.9812983181483994, 'epsilon' 0.0494431806158653762Above 0.85, therefore, the method can quickly and accurately acquire the nitrogen content information of the overground part of the rice, overcome the difficulty that the characteristic wave band of the nitrogen content of the overground part of the rice is difficult to determine due to the spectrum superposition effect caused by complex rice components, improve the precision of a model for inverting the nitrogen content of the overground part of the rice, and if sample data is added, continue training on the basis of the original model so as to update model parameters, and is suitable for large-scale popularization and application.
2. The remote sensing inversion model of the nitrogen content of the overground part of the rice based on the passive attack regression algorithm is a passive attack regression model of Python language, and the model parameters of the passive attack regression model are as follows: the model was examined, R 'loss _ induction', 'n _ iter _ no _ change' 364, 'C' 9.53859412732987, 'max _ iter' 36204, 'differentiation _ fraction' 0.030410487333966476, 'tol' 1.9812983181483994, 'epsilon' 0.0494431806158653762Above 0.85, therefore, the method has the advantages of ingenious design, simple and convenient calculation, easy realization and low cost, and is suitable for large-scale popularization and application.
3. The invention discloses a remote sensing inversion method of nitrogen content of an overground part of rice based on a passive attack regression algorithm, which comprises the following steps: measuring the canopy reflectance of the rice; measuring the nitrogen content of the overground part of the rice: taking the reflectivity of the canopy as input data, calculating by adopting a passive attack regression model of Python language to determine a coefficient R2Constructing a model parameter tuning order matrix; training a passive attack regression model by taking the reflectivity of the canopy as input data and the nitrogen content of the overground part as output results, and sequentially tuning model parameters according to a model parameter tuning order matrix to obtain tuning values of the model parameters; training a passive attack regression model by taking the reflectivity of the canopy as input data and the nitrogen content of the overground part as output results and adopting the adjusted values of model parameters to obtain a remote sensing inversion model of the nitrogen content of the overground part of the rice based on a passive attack regression algorithm, inspecting the model, and performing R2Above 0.85, therefore, the method can quickly and accurately acquire the nitrogen content information of the overground part of the rice, overcome the difficulty that the characteristic wave band of the nitrogen content of the overground part of the rice is difficult to determine due to the spectrum superposition effect caused by complex rice components, improve the inversion precision of the nitrogen content of the overground part of the rice, and continue training on the basis of the original model to update the model parameters if sample data are newly added, and is suitable for large-scale popularization and application.
4. The invention discloses a remote sensing inversion method of nitrogen content of an overground part of rice based on a passive attack regression algorithm, which comprises the following steps: measuring the canopy reflectance of the rice; measuring the nitrogen content of the overground part of the rice: taking the reflectivity of the canopy as input data, calculating by adopting a passive attack regression model of Python language to determine a coefficient R2Constructing a model parameter tuning order matrix; training a passive attack regression model by taking the reflectivity of the canopy as input data and the nitrogen content of the overground part as output results, and sequentially tuning model parameters according to a model parameter tuning order matrix to obtain tuning values of the model parameters; training a passive attack regression model by using the canopy reflectivity as input data and the nitrogen content of the overground part as output results and adopting the adjusted values of model parameters to obtain a remote sensing inversion model of the nitrogen content of the overground part of the rice based on a passive attack regression algorithm, and carrying out the inversion on the modelInspection, R2Above 0.85, therefore, the design is ingenious, the operation is simple and convenient, the cost is low, and the method is suitable for large-scale popularization and application.
These and other objects, features and advantages of the present invention will become more fully apparent from the following detailed description, the accompanying drawings and the claims, and may be realized by means of the instrumentalities, devices and combinations particularly pointed out in the appended claims.
Drawings
FIG. 1 is a schematic flow chart of a specific embodiment of the remote sensing inversion method of nitrogen content in the overground part of rice based on the passive attack 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 the model test of the embodiment shown in FIG. 1, wherein the measured values and the predicted values are in% by weight.
Detailed Description
The invention provides a remote sensing inversion model of the nitrogen content of the overground part of the rice based on a passive attack regression algorithm aiming at the requirement of estimating the nitrogen content of the overground part of the rice based on hyperspectrum, overcoming the difficulties that the characteristic wave band of the nitrogen content of the overground part of the rice is difficult to determine and the characteristic wave band of hyperspectral data is time-consuming and labor-consuming in screening because the components of the rice are complex, wherein the remote sensing inversion model of the nitrogen content of the overground part of the rice based on the passive attack regression algorithm is a passive attack regression model of Python language, and the model parameters of the passive attack regression model are as follows: ' loss ' epsilon _ induction ', n _ iter _ no _ change ' 364 ', C ' 9.53859412732987 ', max _ iter ' 36204 ', valid _ fraction ' 0.030410487333966476 ', tol ' 1.9812983181483994, epsilon ' 0.049443180615865376.
The passive attack regression model may be trained by using any suitable data set, and preferably, the passive attack regression model is trained by using a data set of rice, the data set includes canopy reflectances and overground 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 a 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 36, 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 nitrogen content of the overground part of the rice based on the passive attack regression algorithm, which comprises the following steps:
(1) measuring the canopy reflectance of the rice;
(2) measuring the nitrogen content of the overground part of the rice;
(3) calculating by taking the canopy reflectivity as input data and adopting a passive attack regression model of Python language to obtain an inversion value, and calculating a decision coefficient R according to the inversion value and the overground part nitrogen content2Changing the value of the model parameter, R, of the passive attack regression 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 passive attack regression model by taking the canopy reflectivity as the input data and the nitrogen content of the overground part 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) the method comprises the steps of taking the canopy reflectivity as input data, taking the aboveground part nitrogen content as an output result, adopting the adjusted value of the model parameter to train a passive attack regression model, obtaining a remote sensing inversion model of the rice aboveground part nitrogen content based on a passive attack regression algorithm after the passive attack regression model is trained, storing the remote sensing inversion model of the rice aboveground part nitrogen content based on the passive attack regression algorithm by using a save method, and loading the remote sensing inversion model of the rice aboveground part nitrogen content based on the passive attack regression algorithm for use by using a load method if the remote sensing inversion model of the rice aboveground part nitrogen content based on the passive attack regression algorithm is required to be used.
If the training is needed to continue, the training is continued by using new data on the basis of the saved model directly without the need of previous training data.
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 nitrogen content of the overground part of the rice may specifically include any suitable method, and preferably in the step (2), the step of measuring the nitrogen content of the overground part of the rice specifically includes:
collecting the overground part plants of the rice, deactivating enzyme, drying to constant weight to obtain dry overground parts, crushing the dry overground parts, and measuring the total nitrogen content of the overground parts to obtain the nitrogen content of the overground parts.
In the step (2), the water-removing and the drying can adopt any suitable conditions, and the determination of the total nitrogen content of the overground part can adopt 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 determination of the total nitrogen content of the overground part adopts 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＝{'loss','n_iter_no_change','C','max_iter','validation_fraction','tol','epsilon'}。
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:
'loss'＝'epsilon_insensitive','n_iter_no_change'＝364,'C'＝9.53859412732987,'max_iter'＝36204,'validation_fraction'＝0.030410487333966476,'tol'＝1.9812983181483994,'epsilon'＝0.049443180615865376。
in order to improve the precision of the remote sensing inversion model of the nitrogen content of the overground part of the rice based on the passive attack regression algorithm, 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 plurality of sampling points and the nitrogen content of the overground part of the plurality of sampling points are measured, preferably, in the step (1), the step of measuring the canopy reflectivity of the rice is specifically to measure the canopy 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 nitrogen content of the overground part of the rice is to measure the nitrogen content of the overground part 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 36, 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 content of the overground part of the rice based on the passive attack regression algorithm is based on actually measured hyperspectral data, and adopts rice canopy reflectivity spectrum data and rice overground part nitrogen content 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 48 sampling points are totally distributed uniformly 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 36 sampling points is used for model construction, and the data of 12 sampling points is used for model inspection. The flow of the remote sensing inversion method of the nitrogen content of the overground part of the rice based on the passive attack regression algorithm is shown in figure 1, and the method 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 nitrogen content in paddy rice overground part
Collecting 6 plants of 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 the overground part, deactivating enzymes at 105 ℃ for 20 minutes, drying the plants at 85 ℃ until the weight is constant to obtain the dry overground part, crushing the dry overground part, and measuring the total Nitrogen Content (NC) of the overground part by adopting a semi-micro Kjeldahl method, wherein the unit is (% by weight).
3. Model construction
The model construction adopts a Python language passive attack regression model for construction, 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 overground part nitrogen content data, wherein the preprocessing comprises removing paired rice canopy reflectivity data and rice overground part nitrogen content data containing a missing value and a null value.
3.3 partitioning of data sets
To ensure a reasonable evaluation of the model training and inversion results, a stochastic approach was used to divide the entire data set (48 groups) into two parts, with 75% (36 groups) of data used for model training and 25% (12 groups) for post-training effectiveness evaluation.
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, 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 the nitrogen content of the overground part2Then changeValue of model parameter, R2The larger the change of the model parameter is, the greater the importance of the model parameter is, the model parameter is arranged from large to small according to the importance to construct a model parameter tuning rank matrix for subsequent calculation.
According to the crown layer reflectivity data in the training data set and the corresponding overground part nitrogen content data, the model parameter tuning order matrix obtained by calculation is as follows:
Params＝{'loss','n_iter_no_change','C','max_iter','validation_fraction','tol','epsilon'}。
3.6 model construction
Adjusting the order matrix according to the obtained model parameters, training a passive attack regression model by using data used for modeling, including actually-measured crown layer reflectivity data and corresponding actually-measured overground part nitrogen content data, taking the actually-measured crown layer reflectivity data as input data and the actually-measured overground part nitrogen content data as output results, and 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 following data:
'loss'＝'epsilon_insensitive','n_iter_no_change'＝364,'C'＝9.53859412732987,'max_iter'＝36204,'validation_fraction'＝0.030410487333966476,'tol'＝1.9812983181483994,'epsilon'＝0.049443180615865376。
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 12 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 (the nitrogen content of the overground part), and obtaining a result shown in figure 3, wherein R of the model is2Is 0.8940. Model R using default parameters2Is 0.7091.
If new data is required to be added for training in the later use of the trained model, original training data is not required, and the trained model is continuously trained by directly using the newly added data, so that a new model can be obtained.
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 inspection data and construction, training and inspection of models, and the passive attack regression model of Python is called through the Matlab software.
Therefore, the invention provides a new rice aboveground part nitrogen content remote sensing inversion model based on a passive attack regression algorithm and based on actual measurement hyperspectral remote sensing data, the rice aboveground part nitrogen content information can be rapidly and accurately obtained based on the actual measurement rice canopy reflectivity data and the rice aboveground part nitrogen content data collected on the spot, the difficulty that the characteristic wave band of the rice aboveground part nitrogen content is difficult to determine due to the spectrum superposition effect caused by complex rice components is overcome, the model parameters are optimized by constructing a model parameter optimization rank matrix and using a trial-and-error method, and the accuracy of the rice aboveground part nitrogen content inversion is effectively improved. In the popularization and application of the model, original training data is not needed, new data are directly used for continuing training the existing model, and a quantitative inversion model suitable for the nitrogen content of the overground part of the rice in different ecological regions, different varieties and main growth periods can be obtained, so that the nitrogen nutrition, physiological state and water and fertilizer supply state of the rice are obtained, the growth information acquisition efficiency in the rice cultivation and planting process is improved, and basic scientific data are provided for water and fertilizer operation in rice production.
Compared with the prior art, the invention has the following advantages:
(1) a Passive Attack Regression Model (PARM) used by the method is suitable for the inversion of the nitrogen content of the overground part of the rice based on the hyperspectrum, on the basis of comprehensively considering the information of the wave band range of 350-2500 nm of the hyperspectrum, the optical characteristics of various substance compositions and cell structures in the rice are considered, particularly the influence and superposition effect of complex components on the characteristic wave band of the nitrogen content of the overground part of the rice are considered, and the nitrogen content information of the overground part of the rice contained in different wave bands in remote sensing data is fully utilized to invert the nitrogen content of the overground part of the rice;
(2) a passive attack regression machine learning algorithm is used for constructing a model of a logarithm value of the reflectivity of 350-2500 nm and the nitrogen content of the overground part of the rice, the data training model can be used in batches, the problem that the model training time is long due to large data volume is effectively solved, and the speed and the efficiency of the nitrogen content inversion of the overground part of the rice based on hyperspectral information are improved;
(3) the independence of model training and model inspection is fully considered, the training data set and the inspection data set are divided by a random segmentation method, the training data set is only used for model training, and the inspection data set is only used for model inspection, so that the reasonability of model effect inspection is ensured.
(4) Since the parameter tuning of the model is very important to the calculation accuracy of the model, the invention constructs a model parameter rank matrix to determine the coefficient R2In order to evaluate the parameters, a trial and error method is used for model parameter tuning, and on the basis of ensuring the parameter tuning effect, the speed of model training and parameter tuning is greatly improved.
(5) The inversion method for the nitrogen content of the overground part of the rice is simple and convenient to calculate, and particularly, the model can be continuously trained by using new data without original training data, so that the inversion method is suitable for determining remote sensing quantitative inversion models of the nitrogen content of the overground part of the rice in different ecological regions, different varieties and different growth periods, the inversion accuracy of the nitrogen content of the overground part of the rice is improved, the information of the nitrogen nutrition, the physiological condition, the growth vigor and the like of the rice is rapidly acquired, meanwhile, scientific data are provided for the water and fertilizer operational management of rice planting and cultivation, and the method is convenient to popularize and apply.
In conclusion, the remote sensing inversion model of the nitrogen content of the overground part of the rice based on the passive attack regression algorithm can quickly and accurately acquire the nitrogen content information of the overground part of the rice, overcomes the difficulty that the characteristic wave band of the nitrogen content of the overground part of the rice is difficult to determine due to the spectrum superposition effect caused by complex rice components, improves the accuracy of the inversion model of the nitrogen content of the overground part of the rice, can be continuously trained on the basis of the original model to update model parameters if sample data is newly added, and 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 remote sensing inversion model of the nitrogen content of the overground part of rice based on a passive attack regression algorithm is characterized in that the remote sensing inversion model of the nitrogen content of the overground part of rice based on the passive attack regression algorithm is a passive attack regression model of Python language, and model parameters of the passive attack regression model are as follows: ' loss ' epsilon _ induction ', n _ iter _ no _ change ' 364 ', C ' 9.53859412732987 ', max _ iter ' 36204 ', valid _ fraction ' 0.030410487333966476 ', tol ' 1.9812983181483994, epsilon ' 0.049443180615865376.
2. The remote sensing inversion model of nitrogen content in the overground part of rice based on the passive attack regression algorithm as claimed in claim 1, wherein the passive attack regression model is trained by adopting a data set of rice, the data set comprises canopy reflectivity and the overground part nitrogen content 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.
3. The remote sensing inversion model of nitrogen content in the overground part of rice based on the passive attack regression algorithm as claimed in claim 2, wherein m is 36, the n characteristic wave bands are 2151 characteristic wave bands, and the 2151 characteristic wave bands are from 350nm wave band to 2500nm wave band.
4. A remote sensing inversion method of nitrogen content of the overground part of rice based on a passive attack regression algorithm is characterized by comprising the following steps:
(1) measuring the canopy reflectance of the rice;
(2) measuring the nitrogen content of the overground part of the rice;
(3) in the reflectivity of the canopyCalculating by adopting a Python language passive attack regression model to obtain an inversion value for inputting data, and calculating a decision coefficient R according to the inversion value and the nitrogen content of the overground part2Changing the value of the model parameter, R, of the passive attack regression 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 passive attack regression model by taking the canopy reflectivity as the input data and the nitrogen content of the overground part 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) the method comprises the steps of taking the canopy reflectivity as input data, taking the aboveground part nitrogen content as an output result, adopting the adjusted value of the model parameter to train a passive attack regression model, obtaining a remote sensing inversion model of the rice aboveground part nitrogen content based on a passive attack regression algorithm after the passive attack regression model is trained, storing the remote sensing inversion model of the rice aboveground part nitrogen content based on the passive attack regression algorithm by using a save method, and loading the remote sensing inversion model of the rice aboveground part nitrogen content based on the passive attack regression algorithm for use by using a load method if the remote sensing inversion model of the rice aboveground part nitrogen content based on the passive attack regression algorithm is required to be used.
5. The remote sensing inversion method of nitrogen content in the overground part of rice based on the passive attack regression algorithm according to claim 4, characterized in that in the step (1), the measurement is carried out by using a hyperspectral radiometer, the measurement time is 10: 00-14: 00, the hyperspectral radiometer uses a lens with a field angle of 25 degrees, a sensor probe of the portable field hyperspectral radiometer 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 meters, 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 nitrogen content in the overground part of rice based on the passive attack regression algorithm as claimed in claim 4, wherein in the step (2), the step of measuring the nitrogen content in the overground part of rice specifically comprises:
collecting the overground part plants of the rice, deactivating enzyme, drying to constant weight to obtain dry overground parts, crushing the dry overground parts, and measuring the total nitrogen content of the overground parts to obtain the nitrogen content of the overground parts.
7. The remote sensing inversion method of nitrogen content in paddy rice overground part based on the passive attack regression algorithm as claimed in claim 6, characterized in that in the step (2), the temperature of de-enzyming is 105 ℃, the time of de-enzyming is 20-30 minutes, the temperature of drying is 80-90 ℃, and the method of nitrogen determination by half micro Kjeldahl method is adopted for measuring the total nitrogen content in the overground part.
8. The remote sensing inversion method of nitrogen content in the overground part of rice based on the passive attack regression algorithm according to claim 4, wherein in the step (3), the model parameter tuning rank matrix is as follows:
Params＝{'loss','n_iter_no_change','C','max_iter','validation_fraction','tol','epsilon'}。
9. the remote sensing inversion method of nitrogen content in the overground part of rice based on the passive attack regression algorithm according to claim 8, wherein in the step (4), the optimized values of the model parameters are as follows:
'loss'＝'epsilon_insensitive','n_iter_no_change'＝364,'C'＝9.53859412732987,'max_iter'＝36204,'validation_fraction'＝0.030410487333966476,'tol'＝1.9812983181483994,'epsilon'＝0.049443180615865376。
10. the remote sensing inversion method of nitrogen content in the overground part of rice based on the passive attack regression algorithm according to claim 4, characterized in that in the step (1), the step of measuring the reflectivity of the canopy of rice is specifically to measure the reflectivity of the canopy of m sampling points of a rice planting area, the m sampling points are uniformly distributed in the rice planting area, and the reflectivity of the canopy is the reflectivity of the canopy of n characteristic wave bands; in the step (2), the step of measuring the nitrogen content of the overground part of the rice is to measure the nitrogen content of the overground part of the m sampling points.
11. The remote sensing inversion method of nitrogen content in the overground part of rice based on the passive attack regression algorithm according to claim 10, wherein in the step (1), the m is 36, 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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