CN108007916B - Confocal micro-Raman measurement model for establishing nitrogen content of rice plant by Hilbert-Huang method - Google Patents
Confocal micro-Raman measurement model for establishing nitrogen content of rice plant by Hilbert-Huang method Download PDFInfo
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Abstract
The invention discloses a confocal micro-Raman measurement model for establishing the nitrogen content of rice plants by a Hilbert-Huang method, belonging to the technical field of measurement of trace elements in crops. According to the requirement of a measurement target, 5 functional modules are constructed: the system comprises a spectrum acquisition module, a spectrum preprocessing module, a spectrum decomposition and training module, a Hilbert-Huang transform module and a mode recognition module. The main function of the method is to collect the rice plant spectrum by using a confocal micro-Raman spectrometer; denoising and baseline correction are carried out on the spectral data by applying a wavelet decomposition algorithm; decomposing the spectral data by using a global average empirical mode method to obtain characteristic modal components, and obtaining nitrogen element associated components through neural network training and recognition; obtaining nitrogen characteristic frequency from the marginal spectrum by using Hilbert-Huang transform; and establishing a least square measurement model of the characteristic frequency and the nitrogen content. The invention uses nitrogen marginal spectrum frequency to participate in modeling, has clear measurement mechanism, and has the characteristics of high measurement precision and short test time.
Description
Technical Field
The invention relates to a confocal micro-Raman spectrum measurement method for establishing the nitrogen content of a rice plant by a Hilbert-Huang method, belonging to the technical field of measurement of trace elements in crops.
Background
The traditional detection method of nitrogen level comprises a soil index method, a biochemical determination method and an empirical method. The soil index method is to supplement fertilizer to rice according to the nitrogen content in soil, namely a soil testing formula. The biochemical determination method is the main experimental method for detecting the nitrogen of the rice at present, and comprises a formaldehyde method, a Kjeldahl nitrogen determination method, a Dumas combustion nitrogen determination method and the like. The technical means or indexes of the empirical detection are mainly divided into three categories: visual inspection, remote spectrum sensing, and machine vision. The visual inspection method uses the abnormal color of the leaves as an index to diagnose the disease condition according to the production experience, and is a widely adopted detection means at present. The spectrum remote sensing method is characterized in that the spectrum of the rice field canopy is used as an analysis object, and the standard spectrum is compared to realize nitrogen deficiency detection. The machine vision method mainly uses a PC to distinguish the slight color difference between diseased rice and healthy rice plants from images, and is a digital extension of the visual method. In the method, only when rice has diseases, the spectrum remote sensing method and the machine vision method can be used for effective detection, namely, early measurement is difficult. Experience shows that after the rice shows the nitrogen deficiency symptom, the dosage of the additional fertilizer is at least doubled and obviously influences the growth of seedlings, so that the early detection of the nitrogen deficiency disease is very important. The soil index method is predictive, but the nitrogen in the rice seedlings is related to the nitrogen content in the soil, is influenced by illumination, temperature and humidity, soil pH value and the like, is a multi-factor constraint variable, and is an indirect measurement means with low measurement precision. The biochemical determination method is accurate and reliable, but has complex operation and low efficiency, can not carry out scale and rapid measurement and is mainly used in a control experiment. In the current rice nitrogen deficiency detection, although the visual detection method has large error, the operation is simple, and the method is the most widely applied detection means. There are two disadvantages to manual visual inspection: (1) the color of the rice plant needs to be observed according to experience by manual visual observation, and the judgment result is greatly influenced by subjective factors. When two or more nutrient elements are simultaneously deficient, the color of pathological changes is affected in a crossed manner, so that the visual inspection method cannot effectively judge the color. (2) Only serious nitrogen deficiency lesions can be observed by manual visual inspection. The change of rice plant expression is not obvious in early stage or light degree of pathological changes, but the change has serious influence on plant photosynthesis physiology and the like, and visual observation is difficult.
Disclosure of Invention
The invention aims to solve the problems in the prior art, and researches a confocal microscopic Raman spectrum measurement model for analyzing the nitrogen content in rice plants based on a Hilbert-Huang method, and the model can accurately, quickly and early measure the nitrogen level in the rice plants.
To achieve the above object, the present invention is achieved by the following steps:
(1) firstly, acquiring a confocal microscopic Raman spectrum of a rice plant;
(2) then preprocessing the confocal micro-Raman spectrum data;
(3) then, carrying out self-adaptive decomposition on the confocal micro-Raman spectrum signal, and carrying out correlation identification on each decomposition component and nitrogen to obtain a correlation signal;
(4) then performing Hilbert-Huang transformation on the correlation signals to obtain nitrogen characteristic frequency;
(5) and establishing a correlation characteristic frequency and nitrogen content chemometric measurement model.
[ detailed description ] embodiments
The following provides a specific implementation mode of the method for measuring the nitrogen content in the rice plant based on the confocal micro-Raman spectrum frequency method.
In a laboratory, under the constant temperature environment of 25 ℃, the rice leaves are pressed for 1 hour by a pressing glass sheet, and 0.5 × 0.5cm is cut from the middle part of the position 1cm away from the leaf apex2Leaf sample, placing the leaf sample between a glass slide and a cover glass, placing the leaf sample on an objective table below an objective lens of a micro-Raman spectrometer, scanning the sample with the horizontal and vertical step lengths of 1.2 mu m to obtain a spatial micro-Raman spectrum image of the sample, and expressing a standard image of the rice leaf sample by using average values of a plurality of images; denoising and baseline drift removal processing are carried out on the confocal micro-Raman spectrum by using a wavelet decomposition method; performing ensemble average empirical mode decomposition on the confocal micro-Raman spectrum signal, and applying neural network training and identification to obtain a nitrogen correlation signal; performing Hilbert-Huang transform on the correlation signals, and obtaining nitrogen characteristic frequency through marginal spectrum; and finally, establishing a least square measurement model of the characteristic frequency and the nitrogen content.
The method has the advantages that the spectrum characteristic modal component obtained by overall average empirical mode decomposition is subjected to neural network identification, irrelevant or weakly relevant components are removed, a nitrogen related characteristic modal component is obtained, further Hilbert-Huang transformation is carried out, the inherent characteristic modal component in the spectrum is obtained, the characteristic modal component is nitrogen covalent bond vibration single-frequency direct expression, and the frequency component obtained by comparing the Hilbert-Huang transformation has practical significance, so that the frequency characteristic index is not influenced by superposition of characteristic peaks of other trace elements, and the method has specificity and high identification precision and is suitable for trace element measurement in plants.
Drawings
FIG. 1 is a functional block diagram of a method.
Detailed Description
The embodiments of the present invention will be described in detail below with reference to the accompanying drawings:
1. functional block diagram of method
The method is designed in a modularized mode so as to be convenient to adjust, reusable, easy to modify and easy to expand. According to the measurement target requirement, namely the rice plant leaf confocal micro-Raman spectrum processing and modeling process, 5 functional modules are constructed: the system comprises a spectrum acquisition module, a spectrum preprocessing module, a spectrum decomposition and training module, a Hilbert-Huang transform module and a mode identification module, as shown in figure 1.
The spectrum acquisition module realizes parameter adjustment, state setting, sample preparation, spectrum acquisition and data storage of the spectrum instrument; the spectrum preprocessing module provides a spectrum preprocessing algorithm, including denoising and baseline correction; the spectrum decomposition and training module is used for decomposing the Raman spectrum, training and identifying to obtain the related components of the nitrogen; the Hilbert-Huang transform module converts the wavelength characteristic modal component into a frequency characteristic index; the mode identification module adopts a least square method to establish a nitrogen content measurement model.
2. Key technology
2.1 wavelet decomposition denoising and de-baseline Drift
There are two types of noise in confocal micro-raman spectroscopy, electronic thermal motion noise from the instrument and external communication system interference. The existence of noise greatly affects the interpretation of the true information of the spectrum, so the signal noise reduction plays a significant role in spectrum analysis.
Signal reconstruction is performed by using sym1, sym2, sym3, sym4, sym5, sym6, sym7, sym8, db1, db2, db3, db4, db5, db6, db7, db8, db9, coif1, coif2, coif3, coif4 and coif5 wavelet basis functions respectively, and different threshold estimation methods Hcursucurc, Sqtwolog, Rigrsucrc, Minimaxi and n (1-10) are used for signal reconstruction, and in the present invention, for the confocal microscopy baseline Raman spectrum of rice leaves, the denoising and denoising parameters are preferably set as: basis functions sym5, sym6, sym7, db6, db 7; threshold estimation methods hcursrc, Sqtwolog; the number of decomposition layers was 5.
2.2 ensemble averaged empirical mode decomposition confocal microscopy Raman Spectroscopy
The empirical mode decomposition method can decompose signals in a self-adaptive mode according to different frequencies, but the problem of mode aliasing occurs in the process of decomposing the confocal micro-Raman spectrum.
And (3) ensemble average empirical mode decomposition:
step 1: adding equal-length unequal-amplitude Gaussian white noise into the confocal micro-Raman spectrum to be processed, carrying out empirical mode decomposition on the composite signal, and repeating the operation k times to obtain characteristic modal components
And remainder
。
Empirical mode decomposition:
step a: obtaining a maximum envelope u (t) and a minimum envelope v (t) of the confocal microscopic Raman spectrum x (t) through a cubic spline function, wherein the mean value is recorded as m (t):
step b: let r (t) = x (t) -m (t), if r (t) does not satisfy the characteristic modal component condition, assign it to x (t). Returning to the step 1, circularly calculating
Up to standard deviation
The screening process may be stopped. Wherein
Step c: obtaining the 1 st characteristic modal component
And the remainder
。
Step d: returning to the step 1 to continue screening
In turn obtain
，
，…，
. Judge the remainder
The screening process stops when there is little or substantially a monotonic trend.
Step 2: the ensemble average of the characteristic modal components is taken,
in the formula
The number of empirical mode decomposition times for noise addition.
Identification of characteristic modal components by neural network training
The degree of association with nitrogen element, and the threshold value is set to screen out the associated characteristic frequency component
. The neural network in the invention adopts a BP type structure, and the parameters are set as follows: the number of input layer neurons is determined by the characteristic modal components, in this example, the input feature vector is
Data; the output layer adopts 1 node, and 0 and 1 respectively represent that the nitrogen characteristic modal component is false and true; the selection of the number of nodes of the hidden layer has great influence on the performance of the network, the number of nodes is too small, the nodes are easy to fall into local minimum values, the number of the hidden nodes is too large, a fitting function is complex, the generalization capability of the network is poor, and test results show that the number of the nodes of the optimal hidden layer ranges from 8 to 12 (according to different values of n and m); selecting a logsig () function as an activation function of a neuron in a hidden layer, and selecting a pureline () function as an activation function of an output layer; the number of iterations is set to 1000, the network is trained 1 time every 10 steps, the target value is 0.01, the learning rate is 0.1, and the function of rainlm () is used as the training network.
2.3 Hilbert-Huang transform and model building
Correlating characteristic modal components with
Performing convolution integral operation on the signal to obtain the Hilbert-Huang instantaneous frequency of the associated characteristic modal component
The marginal spectrum of each hilbert instantaneous frequency is calculated according to equation (6).
In the formula
Is the marginal spectrum of the kth component of the confocal micro-raman spectrum. And (3) establishing a partial least square fitting model by taking each frequency amplitude in the marginal spectrum as an input variable and the nitrogen content as an output variable, so as to realize the measurement of the nitrogen content in the rice.
Therefore, the method disclosed by the embodiment of the invention establishes the nitrogen content measurement model in the rice by using the Hilbert-Huang method, is different from a black box modeling method, directly participates in modeling by using the marginal spectrum frequency of nitrogen element, has a clear model measurement mechanism, and has the characteristics of high measurement precision and short test time. The invention is suitable for early detection of nitrogen deficiency diseases of crops such as rice and the like and can provide scientific basis for precision agriculture.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (5)
1. The Hilbert-Huang method is used for establishing a confocal microscopic Raman measurement model of the nitrogen content of the rice plant, and is characterized by being realized by the following steps:
(1) firstly, acquiring a confocal microscopic Raman spectrum of a rice plant;
(2) then preprocessing the confocal micro-Raman spectrum data;
(3) then, carrying out self-adaptive decomposition on the confocal micro-Raman spectrum signal by using a global average empirical mode method to obtain characteristic modal components, and identifying each decomposition component by using neural network training to obtain a nitrogen correlation signal;
(4) then performing Hilbert-Huang transformation on the correlation signals to obtain nitrogen characteristic frequency;
(5) and establishing a correlation characteristic frequency and nitrogen content chemometric measurement model.
2. The hilbert yellow method of claim 1, wherein the model is a confocal micro-raman measurement model for nitrogen content of rice plants, and is characterized in that:
in a laboratory, under the constant temperature environment of 25 ℃;
pressing the rice leaf for 1 hour by using a press glass sheet, and cutting 0.5 × 0.5.5 cm from the middle part of the position 1cm away from the leaf tip2Leaf samples;
placing the leaf sample between a glass slide and a cover glass, placing the leaf sample on an objective table below an objective lens of a micro-Raman spectrometer, and scanning the sample in transverse and longitudinal steps of 1.2 mu m to obtain a spatial micro-Raman spectrum image of the sample;
the rice leaf-like standard image is represented by the average value of a plurality of images.
3. The hilbert yellow method of claim 1, wherein the model is a confocal micro-raman measurement model for nitrogen content of rice plants, and is characterized in that:
denoising the confocal micro-Raman spectrum by using a wavelet decomposition method;
and removing the base line drift of the confocal micro-Raman spectrum by using a wavelet decomposition method.
4. The hilbert yellow method of claim 1, wherein the model is a confocal micro-raman measurement model for nitrogen content of rice plants, and is characterized in that:
and performing Hilbert-Huang transform on the correlation signals, and obtaining the nitrogen characteristic frequency through marginal spectrum.
5. The hilbert yellow method of claim 1, wherein the model is a confocal micro-raman measurement model for nitrogen content of rice plants, and is characterized in that:
and establishing a characteristic frequency and nitrogen content measurement model by using a least square method.
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