CN112686092A - Remote sensing inversion model and method for starch accumulation of overground part of rice based on histogram gradient enhanced regression tree algorithm - Google Patents
Remote sensing inversion model and method for starch accumulation of overground part of rice based on histogram gradient enhanced regression tree algorithm Download PDFInfo
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Abstract
The invention provides a remote sensing inversion model of starch accumulation on the overground part of rice based on a histogram gradient enhanced regression tree algorithm, which is a histogram gradient enhanced regression tree model based on Python language and further provides model parameters of the histogram gradient enhanced regression tree model. The remote sensing inversion method of the starch accumulation of the overground part of the rice based on the gradient enhanced regression tree algorithm of the histogram is also provided. The remote sensing inversion model of the starch accumulation of the overground part of the rice based on the gradient enhancement regression tree algorithm of the histogram can quickly and accurately acquire the information of the starch accumulation of the overground part of the rice, overcomes the difficulty that the characteristic wave band of the starch accumulation of the overground part of the rice is difficult to determine due to the spectrum superposition effect caused by complex rice components, and greatly improves the precision of the inversion model of the starch accumulation of the overground part of the rice.
Description
Technical Field
The invention relates to the technical field of agricultural remote sensing, in particular to the technical field of measurement of starch accumulation of rice overground parts, and specifically relates to a remote sensing inversion model and method of the starch accumulation of the rice overground parts based on a gradient enhanced regression tree algorithm of a histogram.
Background
The starch accumulation amount of the overground part of the rice is an important parameter for quantifying the photosynthesis of the rice to fix carbon dioxide and synthesize carbohydrates, is influenced by factors such as the photosynthetic capacity of the rice, the environmental temperature, the rich water and the like, and reflects the influence of physiological conditions, growth activity of the rice and external management measures for the rich water operation and raising of the rice on the growth condition of the rice.
The method has the advantages that the starch accumulation amount of the overground part of the rice is monitored, physiological conditions and growth conditions of synthesis, transportation, storage, accumulation and the like of rice photosynthetic products are mastered, the yield and quality of rice production can be guaranteed, meanwhile, the water and fertilizer application of the rice can be dynamically managed, and the use amount of the water and fertilizer in the rice production is reduced, so that remarkable economic and social benefits are generated (Wangzhen, Huangjing, plum, and the like; correlation analysis of biochemical parameters and hyperspectral remote sensing characteristic parameters of the rice [ J ]. agricultural engineering report, 2003,19(002): 144-supplement 148). The traditional method for monitoring the starch accumulation amount on the overground part of the rice mainly adopts a destructive sampling method, the detection needs to be carried out indoors, the detection process is complex, time and labor are wasted, the timeliness is poor, the starch accumulation amount on the overground part of the rice cannot be timely obtained, and the popularization and the application are not facilitated.
In the physiological and biochemical processes of rice, the change of certain specific substances and cell structures in rice plants results in the change of rice reflectance spectra. Therefore, the rice growth information such as the starch accumulation amount of the overground part of the rice can be obtained by using the change of the spectrum. Currently, hyperspectrum is used for monitoring the growth state of rice in crop growth monitoring. With the development and popularization of the spectrum technology, the spectrum is used to quickly and rapidly acquire the starch accumulation information of the overground part of rice, which becomes a consensus of more and more rice production practitioners and researchers (Zhoudouqin. monitoring of rice nitrogen nutrition and grain quality based on canopy reflection spectrum [ D ]. Nanjing agriculture university, 2007). 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 starch accumulation amount of the overground part of the rice to construct an inversion model. In the process of constructing the rice overground part starch accumulation inversion model, the spectral range measured by the full-waveband spectrometer covers 350-2500 nm, but because the components of the rice are complex, the characteristic wave bands of the component spectra are partially overlapped, the determination of the characteristic spectrum of the rice overground part starch accumulation is difficult, and meanwhile, the rapid processing of hyperspectral data becomes an urgent technical problem to be solved for estimating the rice overground part starch accumulation based on the hyperspectral data.
Therefore, it is desirable to provide a remote sensing inversion model of the starch accumulation of the overground part of rice, which can quickly and accurately acquire the starch accumulation information of the overground part of rice, overcome the difficulty that the characteristic waveband of the starch accumulation of the overground part of rice is difficult to determine due to the spectrum superposition effect caused by complex rice components, and greatly improve the precision of the inversion model of the starch accumulation of the overground part of rice.
Disclosure of Invention
In order to overcome the defects in the prior art, one object of the present invention is to provide a remote sensing inversion model of starch accumulation in the upper part of a rice field based on a gradient enhanced regression tree algorithm of a histogram, which can quickly and accurately obtain the information of the starch accumulation in the upper part of the rice field, overcome the difficulty that the characteristic waveband of the starch accumulation in the upper part of the rice field is difficult to determine due to the spectrum superposition effect caused by complex rice components, greatly improve the accuracy of the inversion model of the starch accumulation in the upper part of the rice field, and is suitable for large-scale popularization and application.
The invention also aims to provide a remote sensing inversion model of the starch accumulation of the overground part of the rice based on the gradient enhanced regression tree algorithm of the histogram, 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 starch accumulation of the overground part of the rice based on the gradient enhanced regression tree algorithm of the histogram, which can quickly and accurately acquire the starch accumulation information of the overground part of the rice, overcome the difficulty that the characteristic wave band of the starch accumulation of the overground part of the rice is difficult to determine due to the spectrum superposition effect caused by complex rice components, greatly improve the inversion precision of the starch accumulation of the overground part of the rice, and is suitable for large-scale popularization and application.
The invention also aims to provide a remote sensing inversion method of the starch accumulation of the overground part of the rice based on the gradient enhanced regression tree algorithm of the histogram, which has the advantages of ingenious design, simple and convenient operation and low cost, and is suitable for large-scale popularization and application.
In order to achieve the above object, in a first aspect of the present invention, there is provided a remote sensing inversion model of starch accumulation in the upper part of a rice field based on a histogram gradient enhanced regression tree algorithm, which is characterized in that the remote sensing inversion model of starch accumulation in the upper part of a rice field based on the histogram gradient enhanced regression tree algorithm is a gradient enhanced regression tree model based on a histogram Python language, and model parameters of the gradient enhanced regression tree model based on the histogram are as follows: ' loss _ squares ', ' l2_ regulation ', ' 0.00689 ', ' leaving _ rate ', ' 0.0471 ', ' max _ bins ', ' 255, ' max _ depth ', ' None, ' max _ iter ':297, ' max _ leaf _ nodes ':31, ' min _ samples _ leaf ':34, ' tol ':0.0569754, ' evaluation _ action ':0.298873, ' early _ storing ', ' auto ', ' monotonic _ cst ': None, ' n _ iter _ change ':19, ' and ' None '.
Preferably, the histogram-based gradient enhanced regression tree model is trained by using a rice data set, the data set includes canopy reflectances of m sampling points of the rice and an overground starch accumulation amount, the m sampling points are uniformly distributed in a rice planting area, and the canopy reflectivity is a canopy reflectivity of n characteristic bands.
More preferably, m is 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 starch accumulation amount on the overground part of rice based on a histogram gradient enhanced regression tree algorithm, which is characterized by comprising the following steps:
(1) measuring the canopy reflectance of the rice;
(2) measuring the starch accumulation amount of the overground part of the rice;
(3) calculating by taking the canopy reflectivity as input data and adopting a gradient enhanced regression tree model based on a histogram of Python language to obtain an inversion value, and calculating a decision coefficient R according to the inversion value and the starch accumulation amount of the overground part2Changing the value of the model parameter, R, of the histogram-based gradient enhanced regression tree model2The larger the change of the model parameter is, the greater the importance of the model parameter is, the model parameter is arranged from large to small according to the importance to construct a model parameter tuning rank matrix;
(4) training the gradient enhanced regression tree model based on the histogram by taking the canopy reflectivity as the input data and the overground part starch accumulation amount as an output result, and sequentially tuning the model parameters according to the model parameter tuning rank matrix to obtain tuning values of the model parameters;
(5) using the canopy reflectivity as the input data, using the overground part starch accumulation amount as the output result, using the adjusted values of the model parameters to train the histogram-based gradient enhancement regression tree model, after the histogram-based gradient enhancement regression tree model is trained, obtaining a remote sensing inversion model of the starch accumulation of the overground part of the rice based on a gradient enhanced regression tree algorithm of a histogram, saving the remote sensing inversion model of the starch accumulation of the overground part of the rice based on the gradient enhanced regression tree algorithm of the histogram by using a save method, if the remote sensing inversion model of the starch accumulation of the overground part of the rice based on the gradient enhanced regression tree algorithm of the histogram needs to be used, the remote sensing inversion model of the starch accumulation of the overground part of the rice based on the gradient enhanced regression tree algorithm of the histogram 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 the starch accumulation amount of the overground part of the rice specifically comprises:
collecting the overground part plants of the rice, deactivating enzyme, drying to constant weight to obtain dry plants, and measuring the weight of the dry plants to obtain the dry weight of the overground part;
and crushing the dry plants, measuring the starch content of the overground part, and multiplying the weight of the overground part dry matter by the starch content of the overground part to obtain the starch accumulation amount of the overground part.
More preferably, in the step (2), the water-removing temperature is 105 ℃, the water-removing time is 20-30 minutes, the drying temperature is 80-90 ℃, and the determination of the starch content in the overground part adopts a spinning colorimetric method.
Preferably, in the step (3), the model parameter tuning rank matrix is:
Params＝{'loss','l2_regularization','learning_rate','max_bins','max_depth','max_iter','max_leaf_nodes','min_samples_leaf','tol','validation_fraction','early_stopping','monotonic_cst','n_iter_no_change','scoring'}。
preferably, in the step (4), the optimized values of the model parameters are:
'loss':'least_squares','l2_regularization':0.00689,'learning_rate':0.0471,'max_bins':255,'max_depth':None,'max_iter':297,'max_leaf_nodes':31,'min_samples_leaf':34,'tol':0.0569754,'validation_fraction':0.298873,'early_stopping':'auto','monotonic_cst':None,'n_iter_no_change':19,'scoring':'None'。
preferably, in the step (1), the step of measuring the canopy reflectance of the rice is specifically to measure the canopy reflectance of m sampling points of a rice planting area, the m sampling points are uniformly distributed in the rice planting area, and the canopy reflectance is the canopy reflectance of n characteristic bands; in the step (2), the step of measuring the starch accumulation amount on the above-ground part of the rice is specifically to measure the starch accumulation amount on the above-ground part of the m spots.
More preferably, in the step (1), the m is 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 starch accumulation amount on the overground part of the rice based on the histogram gradient enhanced regression tree algorithm is a gradient enhanced regression tree model based on a histogram of Python language, and the model parameters of the gradient enhanced regression tree model based on the histogram are as follows: 'loss _ squares', 'l2_ regulation': 0.00689, 'leaving _ rate':0.0471, 'max _ bins':255, 'max _ depth': None, 'max _ iter':297, 'max _ leaf _ nodes':31, 'min _ samples _ leaf':34, 'tol':0.0569754, 'evaluation _ action':0.298873, 'early _ storing': auto ',' monotonic _ cst ': None,' n _ iter _ change ':19,' scanning ': None', and the model is tested, R2Above 0.85, therefore, the method can quickly and accurately acquire the information of the starch accumulation of the overground part of the rice, overcomes the difficulty that the characteristic wave band of the starch accumulation of the overground part of the rice is difficult to determine due to the spectrum superposition effect caused by complex rice components, greatly improves the precision of a rice overground part starch accumulation inversion model, and is suitable for large-scale popularization and application.
2. The remote sensing inversion model of the starch accumulation amount on the overground part of the rice based on the histogram gradient enhanced regression tree algorithm is a gradient enhanced regression tree model based on a histogram of Python language, and the model parameters of the gradient enhanced regression tree model based on the histogram are as follows: 'loss _ squares', 'l2_ regulation': 0.00689, 'leaving _ rate':0.0471, 'max _ bins':255, 'max _ depth': None, 'max _ iter':297, 'max _ leaf _ nodes':31, 'min _ samples _ leaf':34, 'tol':0.0569754, 'evaluation _ action':0.298873, 'early _ storing': auto ',' monotonic _ cst ': None,' n _ iter _ change ':19,' scanning ': None', and the model is tested, R2Above 0.85, therefore, the method has the advantages of ingenious design, simple and convenient calculation, easy realization and low cost, and is suitable for large-scale popularization and application.
3. The invention discloses a remote sensing inversion method of starch accumulation amount on the overground part of rice based on a histogram gradient enhanced regression tree algorithm, which comprises the following steps: measuring the canopy reflectance of the rice; measuring the starch accumulation amount of the overground part of the rice: calculating by taking the reflectivity of the canopy as input data and adopting a gradient enhanced regression tree model based on a histogram of Python language to determine a coefficient R2Constructing a model parameter tuning order matrix; training a gradient enhanced regression tree model based on a histogram by taking the reflectivity of the canopy as input data and the starch accumulation amount of the overground part as an output result, and sequentially tuning model parameters according to a model parameter tuning order matrix to obtain tuning values of the model parameters; training a histogram-based gradient enhancement regression tree model by using the canopy reflectivity as input data and the overground part starch accumulation as output results and adopting the adjusted values of model parameters to obtain a remote sensing inversion model of the overground part starch accumulation of the rice based on the histogram-based gradient enhancement regression tree algorithm, inspecting the model, and performing R2Above 0.85, therefore, the method can quickly and accurately acquire the information of the starch accumulation amount of the overground part of the rice, overcomes the difficulty that the characteristic wave band of the starch accumulation amount of the overground part of the rice is difficult to determine due to the spectrum superposition effect caused by complex rice components, greatly improves the inversion precision of the starch accumulation amount of the overground part of the rice, and is suitable for large-scale popularization and application.
4. Histogram-based method of the inventionThe remote sensing inversion method of the starch accumulation amount of the overground part of the rice by the gradient enhanced regression tree algorithm comprises the following steps: measuring the canopy reflectance of the rice; measuring the starch accumulation amount of the overground part of the rice: calculating by taking the reflectivity of the canopy as input data and adopting a gradient enhanced regression tree model based on a histogram of Python language to determine a coefficient R2Constructing a model parameter tuning order matrix; training a gradient enhanced regression tree model based on a histogram by taking the reflectivity of the canopy as input data and the starch accumulation amount of the overground part as an output result, and sequentially tuning model parameters according to a model parameter tuning order matrix to obtain tuning values of the model parameters; training a histogram-based gradient enhancement regression tree model by using the canopy reflectivity as input data and the overground part starch accumulation as output results and adopting the adjusted values of model parameters to obtain a remote sensing inversion model of the overground part starch accumulation of the rice based on the histogram-based gradient enhancement regression tree 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 remote sensing inversion method of starch accumulation in the overground part of rice based on a histogram gradient enhanced regression tree algorithm.
FIG. 2 is a schematic diagram of a model building process of the embodiment shown in FIG. 1.
FIG. 3 is a diagram showing the results of model tests in the embodiment shown in FIG. 1, wherein the units of the measured value and the predicted value are g/cm2。
Detailed Description
The invention provides a remote sensing inversion model of the starch accumulation amount on the upper part of the rice based on a histogram gradient enhanced regression tree algorithm, aiming at the requirements of estimating the starch accumulation amount on the upper part of the rice based on hyperspectrum, overcoming the difficulties that the characteristic wave band of the starch accumulation amount on the upper part of the rice 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 remote sensing inversion model of the starch accumulation amount on the upper part of the rice based on the histogram gradient enhanced regression tree algorithm is a Python language histogram based gradient enhanced regression tree model, and the model parameters of the histogram based gradient enhanced regression tree model are as follows: ' loss _ squares ', ' l2_ regulation ', ' 0.00689 ', ' leaving _ rate ', ' 0.0471 ', ' max _ bins ', ' 255, ' max _ depth ', ' None, ' max _ iter ':297, ' max _ leaf _ nodes ':31, ' min _ samples _ leaf ':34, ' tol ':0.0569754, ' evaluation _ action ':0.298873, ' early _ storing ', ' auto ', ' monotonic _ cst ': None, ' n _ iter _ change ':19, ' and ' None '.
The histogram-based gradient enhanced regression tree model may be trained by using any suitable data set, and preferably, the histogram-based gradient enhanced regression tree model is trained by using a data set of rice, the data set includes canopy reflectances and overground starch accumulation amounts of m sampling points of the rice, the m sampling points are uniformly distributed in a rice planting area, and the canopy 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 starch accumulation of the overground part of the rice based on the histogram gradient enhanced regression tree algorithm, which comprises the following steps:
(1) measuring the canopy reflectance of the rice;
(2) measuring the starch accumulation amount of the overground part of the rice;
(3) calculating by taking the canopy reflectivity as input data and adopting a gradient enhanced regression tree model based on a histogram of Python language to obtain an inversion value, and accumulating starch on the overground part according to the inversion valueQuantity calculation determination coefficient R2Changing the value of the model parameter, R, of the histogram-based gradient enhanced regression tree model2The larger the change of the model parameter is, the greater the importance of the model parameter is, the model parameter is arranged from large to small according to the importance to construct a model parameter tuning rank matrix;
(4) training the gradient enhanced regression tree model based on the histogram by taking the canopy reflectivity as the input data and the overground part starch accumulation amount as an output result, and sequentially tuning the model parameters according to the model parameter tuning rank matrix to obtain tuning values of the model parameters;
(5) using the canopy reflectivity as the input data, using the overground part starch accumulation amount as the output result, using the adjusted values of the model parameters to train the histogram-based gradient enhancement regression tree model, after the histogram-based gradient enhancement regression tree model is trained, obtaining a remote sensing inversion model of the starch accumulation of the overground part of the rice based on a gradient enhanced regression tree algorithm of a histogram, saving the remote sensing inversion model of the starch accumulation of the overground part of the rice based on the gradient enhanced regression tree algorithm of the histogram by using a save method, if the remote sensing inversion model of the starch accumulation of the overground part of the rice based on the gradient enhanced regression tree algorithm of the histogram needs to be used, the remote sensing inversion model of the starch accumulation of the overground part of the rice based on the gradient enhanced regression tree algorithm of the histogram 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 starch accumulation amount of the overground part of the rice may specifically include any suitable method, and preferably, in the step (2), the step of measuring the starch accumulation amount of the overground part of the rice specifically includes:
collecting the overground part plants of the rice, deactivating enzyme, drying to constant weight to obtain dry plants, and measuring the weight of the dry plants to obtain the dry weight of the overground part;
and crushing the dry plants, measuring the starch content of the overground part, and multiplying the weight of the overground part dry matter by the starch content of the overground part to obtain the starch accumulation amount of the overground part.
In the step (2), the water-removing and the drying can adopt any suitable conditions, and the above-ground starch content can be measured by any suitable method, and more preferably, in the step (2), the water-removing temperature is 105 ℃, the water-removing time is 20-30 minutes, the drying temperature is 80-90 ℃, and the above-ground starch content is measured by an optical rotation colorimetry.
In the step (3), the model parameter tuning rank matrix is based on a decision coefficient R2Determining, preferably, in the step (3), that the model parameter tuning rank matrix is:
Params＝{'loss','l2_regularization','learning_rate','max_bins','max_depth','max_iter','max_leaf_nodes','min_samples_leaf','tol','validation_fraction','early_stopping','monotonic_cst','n_iter_no_change','scoring'}。
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':'least_squares','l2_regularization':0.00689,'learning_rate':0.0471,'max_bins':255,'max_depth':None,'max_iter':297,'max_leaf_nodes':31,'min_samples_leaf':34,'tol':0.0569754,'validation_fraction':0.298873,'early_stopping':'auto','monotonic_cst':None,'n_iter_no_change':19,'scoring':'None'。
in order to make the precision of the remote sensing inversion model of the starch accumulation amount on the overground part of the rice based on the histogram gradient enhanced regression tree algorithm 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 plurality of sampling points and the overground part starch accumulation amount 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 starch accumulation amount on the above-ground part of the rice is specifically to measure the starch accumulation amount on the above-ground part of the m spots.
In the step (1), m and n are positive integers, which can be determined as required, and more preferably, in the step (1), m is 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 starch accumulation amount on the overground part of the rice based on the histogram gradient enhanced regression tree algorithm is based on actually measured hyperspectral data, and adopts rice canopy reflectance spectrum data and rice starch accumulation amount data on the overground part of the rice, which are collected by a rice planting area (a rice and wheat planting base in Huaian area of agricultural science research institute of Huaian city, Jiangsu province, the rice variety is No. 5, and the sampling period is a rice jointing stage), wherein the total number of the sampling points is 48, and the sampling points are uniformly distributed and completely cover the whole area of the rice planting area. The data of 48 sampling points are divided into two parts by a random method, wherein the data of 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 starch accumulation of the overground part of the rice based on the gradient enhanced regression tree algorithm of the histogram 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 starch accumulation amount in overground part of rice
Collecting 6 rice overground part plants uniformly distributed in a spectral measurement view field of each sampling point, wrapping the plants by using absorbent paper, bringing the wrapped plants back to a laboratory, deactivating enzymes at 105 ℃ for 25 minutes, drying the plants at 85 ℃ to obtain dry plants, weighing the dry plants by using a thousandth electronic balance to obtain the overground part dry Matter Weight (MW), crushing the dry plants, and measuring the starch content in the dry plants by using a spinning colorimetric method (Shanghai plant physiology society, modern plant physiology experimental guideline, Beijing: scientific technology publisher, 1999,12: 131-. The overground part Starch Accumulation (SA) is obtained according to the following formula:
SA＝MW×SC。
3. model construction
The model construction is implemented by using a gradient enhanced regression tree model based on a histogram in Python language, please refer to fig. 2, and the model construction mainly comprises the following steps:
3.1 data verification
And checking the acquired rice canopy reflectivity data, and rejecting abnormal whole spectral curve data. The abnormal spectrum in the invention means that adjacent spectrum changes by more than 100%, and spectrum values including null values and negative values are included.
3.2 preprocessing of data
And preprocessing the verified rice canopy reflectivity data and the rice overground part starch accumulation data, wherein the preprocessing comprises removing paired rice canopy reflectivity data and rice overground part starch accumulation data containing deletion values and null values.
3.3 partitioning of data sets
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 a training data set is divided into 5 parts to train the model when the model is trained and iterated every time.
3.5 construction of model parameter tuning rank matrix
In the invention, the tuning of the model parameters in the model training process is very important, and in order to ensure that the best model tuning is obtained as much as possible, a trial-and-error method is used for tuning the model parameters. The present invention uses the coefficient of determination R2(R2The closer to 1, the better) as the test parameter, a parameter rank matrix for evaluating the weight of the model parameter is constructed. According to a training data set, firstly, the default value of the model parameter is used for calculation to obtain an inversion value, and a decision coefficient R is calculated according to the inversion value and the starch accumulation amount on the overground part2Then changing the value of the model parameter, R2Variations of (2)The larger the importance of the model parameters is, the more the importance of the model parameters is, the model parameters are arranged from large to small to construct a model parameter tuning rank matrix for subsequent calculation.
According to the crown layer reflectivity data in the training data set and the corresponding overground part starch accumulation data, the model parameter tuning order matrix obtained by calculation is as follows:
Params＝{'loss','l2_regularization','learning_rate','max_bins','max_depth','max_iter','max_leaf_nodes','min_samples_leaf','tol','validation_fraction','early_stopping','monotonic_cst','n_iter_no_change','scoring'}。
wherein, the change of the model parameters 'early _ stopping', 'monotonic _ cst', 'n _ iter _ no _ change', 'tuning' of the histogram-based gradient enhanced regression tree model does not cause the accuracy change of the histogram-based gradient enhanced regression tree model.
3.6 model construction
Adjusting an optimal order matrix according to the obtained model parameters, training a gradient enhanced regression tree model based on a histogram according to data used for modeling, including actually-measured crown layer reflectivity data and corresponding actually-measured overground part starch accumulation data, taking the actually-measured crown layer reflectivity data as input data and the actually-measured overground part starch accumulation data as output results, and sequentially adjusting the model parameters according to the model parameter optimal order matrix to obtain complete parameters and values of the model, wherein the data comprises the actually-measured crown layer reflectivity data and the corresponding actually-measured overground part starch accumulation data, and the model comprises the following steps:
'loss':'least_squares','l2_regularization':0.00689,'learning_rate':0.0471,'max_bins':255,'max_depth':None,'max_iter':297,'max_leaf_nodes':31,'min_samples_leaf':34,'tol':0.0569754,'validation_fraction':0.298873,'early_stopping':'auto','monotonic_cst':None,'n_iter_no_change':19,'scoring':'None'。
after the model training is finished, the save method is used for saving the model, and if the model is required to be used, the load method is operated for loading and using.
3.7 model test
Using 12 sampling points out of the constructed model to perform hyperspectral data input model and using the optimized model parametersCalculating to obtain predicted value, analyzing the relationship between the predicted value and measured value (starch accumulation amount in aerial parts), and obtaining the result shown in FIG. 32For 0.9324, model R with default parameters is used2Is 0.
In the embodiment, Matlab software (version: R2020a 9.8.0.1380330) and Python (version:3.7.0) developed by MathWorks corporation in America are used for random division of training data and testing data and construction, training and testing of models, and the gradient enhanced regression tree model based on the histogram of the Python is called through the Matlab software.
Therefore, the invention provides a new remote sensing inversion model of the starch accumulation amount of the overground part of the rice based on a histogram gradient enhanced regression tree algorithm based on the actually measured hyperspectral remote sensing data, the information of the starch accumulation amount of the overground part of the rice can be quickly and accurately obtained based on the actually measured reflectance data of the rice canopy and the data of the starch accumulation amount of the overground part of the rice collected on the spot, the difficulty that the characteristic wave band of the starch accumulation amount of the overground part of the rice is difficult to determine caused by the spectrum superposition effect caused by the complex rice components is overcome, the model parameter is optimized by constructing a model parameter optimization order matrix, the model parameter is optimized by using a trial-and-error method, the phenomenon of overfitting of a linear model is effectively reduced, the inversion accuracy of the starch accumulation amount of the overground part of the rice is greatly improved, and the remote sensing inversion model is suitable for the quantitative inversion of the starch accumulation amount of the overgro, therefore, the physiological state of the rice and the state of water and fertilizer supply are obtained, the growth information acquisition efficiency in the rice cultivation and planting process is improved, and basic scientific data are provided for the operation and research of water and fertilizer in rice production.
Compared with the prior art, the invention has the following advantages:
(1) the gradient enhanced regression tree model (HistGradientBoost model) based on the histogram is suitable for the inversion of the starch accumulation 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 body are considered, particularly the influence and the superposition effect of complex components on the characteristic wave band of the starch accumulation of the overground part of the rice are considered, and the rice overground part starch accumulation information contained in different wave bands in remote sensing data is fully utilized to invert the starch accumulation of the overground part of the rice.
(2) The machine learning algorithm is used for constructing a model of the reflectivity of 350-2500 nm and the starch accumulation amount of the overground part of the rice based on a gradient enhanced regression tree of a histogram, so that the overfitting phenomenon caused by using models such as linear regression can be effectively reduced; in addition, a large amount of training data are segmented by using the histogram, so that the training time of the model is greatly shortened, and the speed and efficiency of the rice overground part starch accumulation inversion based on the hyperspectral information are effectively improved.
(3) The independence of model training and model inspection is fully considered, the training data set and the inspection data set are divided by a random segmentation method, the training data set is only used for model training, and the inspection data set is only used for model inspection, so that the reasonability of model effect inspection is ensured.
(4) Since the parameter tuning of the model is very important to the calculation accuracy of the model, the invention constructs a model parameter rank matrix to determine the coefficient R2In order to evaluate the parameters, a trial and error method is used for model parameter tuning, and on the basis of ensuring the parameter tuning effect, the speed of model training and parameter tuning is greatly improved.
(5) The inversion method for the starch accumulation amount of the overground part of the rice is simple and convenient to calculate, is suitable for remote sensing quantitative inversion of the starch accumulation amount of the overground part of the rice in different ecological regions, different varieties and different growth periods, can accurately invert the starch accumulation amount of the overground part of the rice, can quickly acquire information such as physiological conditions and growth vigor of the rice and the like, and provides scientific data for water and fertilizer operational management of rice planting and cultivation.
In conclusion, the remote sensing inversion model of the starch accumulation of the overground part of the rice based on the gradient enhanced regression tree algorithm of the histogram can quickly and accurately acquire the starch accumulation information of the overground part of the rice, overcomes the difficulty that the characteristic wave band of the starch accumulation of the overground part of the rice is difficult to determine due to the spectrum superposition effect caused by complex rice components, greatly improves the precision of the inversion model of the starch accumulation of the overground part of the rice, and is ingenious in design, simple and convenient to calculate, easy to implement, low in cost and suitable for large-scale popularization and application.
It will thus be seen that the objects of the invention have been fully and effectively accomplished. The functional and structural principles of the present invention have been shown and described in the embodiments, and the embodiments may be modified without departing from the principles. Therefore, this invention includes all modifications encompassed within the spirit and scope of the claims.
Claims (11)
1. The remote sensing inversion model of the starch accumulation of the overground part of the rice based on the histogram gradient enhanced regression tree algorithm is characterized in that the remote sensing inversion model of the starch accumulation of the overground part of the rice based on the histogram gradient enhanced regression tree algorithm is a gradient enhanced regression tree model based on a histogram of Python language, and model parameters of the gradient enhanced regression tree model based on the histogram are as follows: ' loss _ squares ', ' l2_ regulation ', ' 0.00689 ', ' leaving _ rate ', ' 0.0471 ', ' max _ bins ', ' 255, ' max _ depth ', ' None, ' max _ iter ':297, ' max _ leaf _ nodes ':31, ' min _ samples _ leaf ':34, ' tol ':0.0569754, ' evaluation _ action ':0.298873, ' early _ storing ', ' auto ', ' monotonic _ cst ': None, ' n _ iter _ change ':19, ' and ' None '.
2. The remote sensing inversion model of starch accumulation of rice above ground based on histogram gradient enhanced regression tree algorithm as claimed in claim 1, wherein said histogram gradient enhanced regression tree model is trained with a rice data set, said data set includes canopy reflectance and starch accumulation of m sampling points of said rice, said m sampling points are uniformly distributed in rice planting area, said canopy reflectance is canopy reflectance of n characteristic bands.
3. The remote sensing inversion model of starch accumulation in the overground part of rice based on the histogram gradient enhanced regression tree algorithm as claimed in claim 2, wherein m is 36, the n characteristic bands are 2151 characteristic bands, and the 2151 characteristic bands are from 350nm to 2500 nm.
4. A remote sensing inversion method of starch accumulation amount of overground part of rice based on histogram gradient enhanced regression tree algorithm is characterized by comprising the following steps:
(1) measuring the canopy reflectance of the rice;
(2) measuring the starch accumulation amount of the overground part of the rice;
(3) calculating by taking the canopy reflectivity as input data and adopting a gradient enhanced regression tree model based on a histogram of Python language to obtain an inversion value, and calculating a decision coefficient R according to the inversion value and the starch accumulation amount of the overground part2Changing the value of the model parameter, R, of the histogram-based gradient enhanced regression tree model2The larger the change of the model parameter is, the greater the importance of the model parameter is, the model parameter is arranged from large to small according to the importance to construct a model parameter tuning rank matrix;
(4) training the gradient enhanced regression tree model based on the histogram by taking the canopy reflectivity as the input data and the overground part starch accumulation amount as an output result, and sequentially tuning the model parameters according to the model parameter tuning rank matrix to obtain tuning values of the model parameters;
(5) using the canopy reflectivity as the input data, using the overground part starch accumulation amount as the output result, using the adjusted values of the model parameters to train the histogram-based gradient enhancement regression tree model, after the histogram-based gradient enhancement regression tree model is trained, obtaining a remote sensing inversion model of the starch accumulation of the overground part of the rice based on a gradient enhanced regression tree algorithm of a histogram, saving the remote sensing inversion model of the starch accumulation of the overground part of the rice based on the gradient enhanced regression tree algorithm of the histogram by using a save method, if the remote sensing inversion model of the starch accumulation of the overground part of the rice based on the gradient enhanced regression tree algorithm of the histogram needs to be used, the remote sensing inversion model of the starch accumulation of the overground part of the rice based on the gradient enhanced regression tree algorithm of the histogram is loaded by using a load method for use.
5. The remote sensing inversion method of starch accumulation amount on the overground part of rice based on histogram gradient enhanced regression tree algorithm as claimed in claim 4, characterized in that in the step (1), the measurement is performed by using a hyperspectral radiometer, the time of the measurement is 10: 00-14: 00, the hyperspectral radiometer uses a lens with a field angle of 25 degrees, a sensor probe of the portable field hyperspectral radiometer is vertically directed to the canopy of the rice and has a vertical height of 1 meter from the top layer of the canopy, the diameter of the ground field range of the sensor probe is 0.44 meter, the sensor probe faces the 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 starch accumulation of rice above ground based on histogram gradient enhanced regression tree algorithm as claimed in claim 4, wherein in said step (2), said step of measuring starch accumulation of rice above ground specifically comprises:
collecting the overground part plants of the rice, deactivating enzyme, drying to constant weight to obtain dry plants, and measuring the weight of the dry plants to obtain the dry weight of the overground part;
and crushing the dry plants, measuring the starch content of the overground part, and multiplying the weight of the overground part dry matter by the starch content of the overground part to obtain the starch accumulation amount of the overground part.
7. The remote sensing inversion method of starch accumulation amount in the overground part of rice based on histogram gradient enhanced regression tree algorithm as claimed in claim 6, wherein in the step (2), the temperature of de-enzyming is 105 ℃, the time of de-enzyming is 20 minutes to 30 minutes, the temperature of drying is 80 ℃ to 90 ℃, and the method of measuring the starch content in the overground part adopts a spinning colorimetry.
8. The remote sensing inversion method for starch accumulation of overground part of rice based on histogram gradient enhanced regression tree algorithm as claimed in claim 4, wherein in said step (3), said model parameter tuning rank matrix is:
Params＝{'loss','l2_regularization','learning_rate','max_bins','max_depth','max_iter','max_leaf_nodes','min_samples_leaf','tol','validation_fraction','early_stopping','monotonic_cst','n_iter_no_change','scoring'}。
9. the remote sensing inversion method of starch accumulation amount in the overground part of rice based on histogram gradient enhanced regression tree algorithm as claimed in claim 8, wherein in said step (4), said model parameters are adjusted to values of:
'loss':'least_squares','l2_regularization':0.00689,'learning_rate':0.0471,'max_bins':255,'max_depth':None,'max_iter':297,'max_leaf_nodes':31,'min_samples_leaf':34,'tol':0.0569754,'validation_fraction':0.298873,'early_stopping':'auto','monotonic_cst':None,'n_iter_no_change':19,'scoring':'None'。
10. the remote sensing inversion method of starch accumulation amount on the overground part of rice based on histogram-based gradient enhanced regression tree algorithm as claimed in claim 4, wherein in said step (1), said step of measuring the canopy reflectance of rice is specifically to measure said canopy reflectance of m sampling points of a rice planting area, m said sampling points are uniformly distributed in said rice planting area, said canopy reflectance is the canopy reflectance of n characteristic bands; in the step (2), the step of measuring the starch accumulation amount on the above-ground part of the rice is specifically to measure the starch accumulation amount on the above-ground part of the m spots.
11. The remote sensing inversion method of starch accumulation amount in the overground part of rice based on histogram gradient enhanced regression tree algorithm as claimed in claim 10, wherein in said step (1), said m is 36, said n characteristic bands are 2151 characteristic bands, and said 2151 characteristic bands are from 350nm band to 2500nm band.
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