CN114118157A - Illumination information diagnosis method and system based on plant electric signals - Google Patents

Illumination information diagnosis method and system based on plant electric signals Download PDF

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CN114118157A
CN114118157A CN202111441798.XA CN202111441798A CN114118157A CN 114118157 A CN114118157 A CN 114118157A CN 202111441798 A CN202111441798 A CN 202111441798A CN 114118157 A CN114118157 A CN 114118157A
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田立国
李猛
王岳松
刘金奇
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Tianjin University of Technology and Education China Vocational Training Instructor Training Center
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Abstract

The invention discloses a plant electric signal-based illumination information diagnosis method and system. The method comprises the following steps: collecting sample plant electric signals under different illumination degrees, and constructing a training set; training the mixed neural network model through the training set to obtain a relation model between plant electric signals and illumination intensity; determining the illumination intensity of the current plant according to the relation model based on the electric signal data of the current plant; judging whether the illumination intensity of the current plant is within the preset range of the optimal illumination intensity; if not, adjusting the illumination intensity of the current plant. The invention obtains the coupling rule between the plant electric signal and the illumination intensity, can immediately obtain the illumination intensity of the plant in the environment through the current plant electric signal information, and enables the plant to be in the optimal growth state through adjusting the illumination intensity of the surrounding environment.

Description

Illumination information diagnosis method and system based on plant electric signals
Technical Field
The invention relates to the technical field of information agriculture, in particular to an illumination information diagnosis method and system based on plant electric signals.
Background
To judge the growth state of the plant, firstly, an evaluation method and an index system of the growth state of the plant are established, and the quality of the growth state of the plant is accurately judged. The traditional morphological index and physiological and biochemical index are complex to measure and have certain hysteresis. In the prior art, a computer vision is also used for collecting physiological shape images of plants and analyzing and judging the growth vigor of the plants. However, this method is limited to pest control and detection, and has low image recognition accuracy and certain hysteresis in information processing.
Disclosure of Invention
The invention aims to provide a method and a system for diagnosing illumination information based on plant electric signals, which adjust the illumination intensity of the current environment according to the coupling relation between the plant electric signals and the illumination intensity in environmental factors so as to enable plants to be in the optimal growth state.
In order to achieve the purpose, the invention provides the following scheme:
a method for diagnosing illumination information based on plant electric signals comprises the following steps:
collecting sample plant electric signals under different illumination degrees, and constructing a training set;
training the mixed neural network model through the training set to obtain a relation model between plant electric signals and illumination intensity;
determining the illumination intensity of the current plant according to the relation model based on the electric signal data of the current plant;
judging whether the illumination intensity of the current plant is within the preset range of the optimal illumination intensity;
if not, adjusting the illumination intensity of the current plant.
Optionally, the acquiring electrical signal data of the sample plant under different illumination degrees, and constructing a training set specifically includes:
dividing the sample plant electrical signals according to the time length, wherein the length of each section of the sample plant electrical signals is L;
carrying out empirical mode decomposition on each divided section of sample plant electric signal to obtain n IMF vectors;
selecting the IMF vectors with high first m relation numbers from the n IMF vectors;
splicing the m IMF vectors into a plurality of signal number sequences, and converting all the signal number sequences into a two-dimensional matrix of a time domain and a frequency domain by using short-time Fourier transform; the two-dimensional matrix constitutes a training set.
Optionally, the method further comprises: and eliminating interference noise in the m IMF vectors by adopting an improved wavelet threshold method.
Optionally, the removing, by using an improved wavelet threshold method, the interference noise in the m IMF vectors specifically includes:
judging whether the wavelet coefficients of the m IMF vectors are larger than a coefficient threshold value or not;
if yes, reserving;
if not, deleting.
Optionally, the hybrid neural network model comprises a convolutional neural network and a long-short term memory cycling neural network.
The invention also provides an illumination information diagnosis system based on the plant electric signals, which comprises:
the training set construction module is used for collecting the plant electrical signals of the samples under different illumination degrees and constructing a training set;
the training module is used for training the mixed neural network model through the training set to obtain a relation model between the plant electric signals and the illumination intensity;
the current plant illumination intensity determining module is used for determining the illumination intensity of the current plant according to the relation model based on the electric signal data of the current plant;
the judging module is used for judging whether the illumination intensity of the current plant is within the preset range of the optimal illumination intensity;
and the adjusting module is used for adjusting the illumination intensity of the current plant when the illumination intensity of the current plant is not within the preset range of the optimal illumination intensity.
Optionally, the training set constructing module specifically includes:
the dividing unit is used for dividing the sample plant electric signals according to time length, and the length of each section of the sample plant electric signals is L;
the empirical mode decomposition unit is used for carrying out empirical mode decomposition on each divided section of the plant electric signal to obtain n IMF vectors;
a selection unit, configured to select the IMF vectors with the first m high correlation numbers from the n IMF vectors;
the training set constructing unit is used for splicing the m IMF vectors into a plurality of signal number sequences and converting all the signal number sequences into a two-dimensional matrix of a time domain and a frequency domain by using short-time Fourier transform; the two-dimensional matrix constitutes a training set.
Optionally, the training set constructing module further includes: and the elimination unit is used for eliminating the interference noise in the m IMF vectors by adopting an improved wavelet threshold method.
Optionally, the eliminating unit specifically includes:
a judging subunit, configured to judge whether the wavelet coefficients of the m IMF vectors are greater than a coefficient threshold;
a retention subunit configured to retain the wavelet coefficients of the m IMF vectors when the wavelet coefficients are greater than a coefficient threshold;
a deletion subunit, configured to delete when the wavelet coefficients of the m IMF vectors are less than or equal to a coefficient threshold.
Optionally, the hybrid neural network model comprises a convolutional neural network and a long-short term memory cycling neural network.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the method collects plant leaf electrical signals under different illumination degrees, utilizes empirical mode decomposition and an improved wavelet threshold value method to perform noise elimination processing on the electrical signals, and converts the electrical signal array of a one-dimensional time domain into a matrix of a two-dimensional time-frequency domain through short-time Fourier transform. Inputting the matrix data into a mixed neural network, carrying out deep learning training on plant electric signals through CNN and LSTM to obtain a relation model of the plant electric signals and the illumination intensity, then determining the illumination intensity of the current plant based on the relation model, and enabling the plant to be in the optimal growth state by adjusting the illumination intensity of the surrounding environment.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a flowchart of a method for diagnosing illumination information based on plant electrical signals according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a method and a system for diagnosing illumination information based on plant electric signals, which adjust the illumination intensity of the current environment according to the coupling relation between the plant electric signals and the illumination intensity in environmental factors so as to enable plants to be in the optimal growth state.
Similar to bioelectric signals of human beings and animals, electric signal transmission also exists in plants, and the transmission speed of the electric signal is faster than that of other general signals. The plant electric signal reflects the physiological growth state information of the plant, and the plant electric signal and the environmental factor have a specific coupling relation, so that when the growth state of the plant or the surrounding environment changes, the electric signal in the plant body changes accordingly. On the basis, according to the information transmitted by the plant electric signals, the invention analyzes and judges the current illumination intensity state of the plant through a trained model, and adjusts the environmental factors in the surrounding growth environment, so that the plant is always in the optimal growth state, thereby improving the crop yield.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1, the method for diagnosing illumination information based on plant electrical signals according to the present invention includes the following steps:
step 101: and collecting the plant electric signals of the samples under different illumination degrees to construct a training set.
Step 102: and training the mixed neural network model through the training set to obtain a relation model between the plant electric signals and the illumination intensity.
Step 103: and determining the illumination intensity of the current plant according to the relation model based on the electrical signal data of the current plant.
Step 104: and judging whether the illumination intensity of the current plant is within the preset range of the optimal illumination intensity.
Step 105: and when the illumination intensity of the current plant is not within the preset range of the optimal illumination intensity, adjusting the illumination intensity of the current plant.
Under the condition that other environmental factors are kept unchanged, the plant electric signal real-time data under different illumination degrees are collected. The plant electric signal acquisition electrode has a positive electrode, a negative electrode and a reference electrode in common, and the positive electrode and the negative electrode are separated by 10cm to collect effective electric signals. Designing a Butterworth filter with a low-pass cut-off frequency of 100Hz and a high-pass cut-off frequency of 0.05Hz and an adaptive power frequency trap with a non-recursive system FIR structure, and simply preprocessing the plant electric signals under different illumination degrees.
Wherein, step 101 specifically includes:
step 1011: dividing the simply preprocessed plant electric signals at equal intervals according to the time length t to obtain a plant electric signal data array with the length of each segment being L;
step 1012: carrying out empirical mode decomposition on each section of plant electric signals, carrying out stabilization treatment on non-stable plant electric signals to obtain n IMF vectors, selecting the IMF vectors with high m phase relation numbers, and eliminating interference noise in the signals by an improved wavelet threshold method;
step 1013: splicing the m IMF vectors into a signal number sequence with the length of L x m, marking as I, and converting all the signal number sequences I into a two-dimensional matrix of a time domain and a frequency domain by using short-time Fourier transform (STFT); the two-dimensional matrix constitutes a training set, a test set and a verification set.
Wherein, step 1012 specifically includes:
finding out all maximum value points in the plant electric signal data sequence with the length of each segment being L, and forming an upper envelope line e of the discrete signal sequence through spline interpolation function cubic fitting+(t); finding out all minimum value points in the plant electric signal data sequence in the same way, and fitting to form a lower envelope line e of the discrete signal sequence-(t)；
Calculating the mean m (t) of the upper envelope and the lower envelope of the discrete signal sequence, and then: m (t) ═ e+(t)-e-(t)；
Subtracting the mean value m (t) of the upper envelope line and the lower envelope line from the original data sequence to obtain a new data sequence h (t);
repeating the steps until h (t) has no extreme value, and stopping;
a plurality of intrinsic mode functions EMD expression as
Figure BDA0003383613320000051
Wherein x (t) is the original signal sequence, Ci(t) is IMF of order i, rn(t) is the residual component after n decompositions.
The process of eliminating the interference noise in the signal by the improved wavelet threshold method specifically comprises the following steps: determining the wavelet base type according to the noise elimination requirement, and selecting the wavelet decomposition layer number, the wavelet step length and the support distance. A threshold lambda is selected and the wavelet coefficients of the signal are compared to the threshold lambda. Regarding the part with the absolute value less than lambda as noise, and setting the wavelet coefficient to zero; the part with absolute value greater than λ is regarded as valid information in the signal, and its new value minus λ is retained.
And performing wavelet inverse transformation on the electric signal subjected to the denoising treatment, and reconstructing the electric signal according to a wavelet scale. The recombined signal data can ensure the effectiveness in the plant electric signals after the wavelet threshold function is improved.
The step 101 further includes splicing the m IMF vectors into a signal number sequence I to ensure the integrity of the electrical signal information, converting the signal number sequence into a matrix by using short-time Fourier transform, converting the one-dimensional plant electrical signal data into a two-dimensional energy spectrum, and inputting the two-dimensional energy spectrum into the hybrid neural network.
The process of short-time fourier transformation is to divide a longer time signal into shorter segments of the same length, and to compute the fourier transform, i.e. the fourier spectrum, on each shorter segment. Selecting a window function which can be kept stable (is stable) in a short time, and continuously translating the window function to enable f (t), g (t) to be stable signals in each limited time range, so as to obtain energy spectrograms of the data at different moments.
The formula for the short-time fourier transform is as follows:
Figure BDA0003383613320000061
wherein Z (t) is the source signal of plant electricity, and g (t) is a window function.
The hybrid neural network model in step 102 includes a convolutional neural network and a long-short term memory cycle neural network. The input of the hybrid neural network model is an energy spectrogram of plant electric signal data under different illumination degrees, and the plant electric signal data set data is divided, wherein the training set is 70%, the testing set is 15%, and the verifying set is 15%.
The hierarchical structure of the Convolutional Neural Network (CNN) includes a data input layer, a convolutional calculation layer, a ReLU excitation layer, a pooling layer, and a full connection layer. The CNN processes input data by adopting local connection and weight sharing on the basis of a multilayer perceptron (MLP), and reduces the overfitting risk on the basis of reducing the complexity of a model.
The convolutional neural network introduces a residual error network structure, residual error learning is carried out by using the convolutional layer, the model structure can be deeper through the residual error structure, and the precision of model training is gradually improved.
The formula for residual learning is:
F(x)＝H(x)-x
where F (x) is the pre-summation network map and H (x) is the input-to-summation network map.
The convolutional neural network model uses a 3-time convolution process, and each module comprises convolution, batch normalization, a max pooling layer and a RELU layer.
And the convolutional neural network is used for extracting the characteristics of the energy graph, obtaining the parameters of a characteristic extraction layer through autonomous training and continuously updating the weight parameters.
Training errors and generalization errors occur in the training process of the model. The training error is the error expressed in the training set, and the generalization error is the error expressed in the testing set. The learned model parameters of the training data set may cause the model to perform better in the training set than in the test set.
The formula of the adjusting model is as follows:
Figure BDA0003383613320000071
in the above formula, ωkIs the weight parameter of the model, and b is the bias.
Training the convolution neural network electric signal data, and obtaining a one-dimensional signal through feature extraction.
And the one-dimensional signal is used as the input of the long-term and short-term memory neural network, the one-dimensional signal is subjected to learning optimization, and a relation model between the plant electric signal and the illumination intensity is obtained through classification training.
The specific embodiment is as follows:
the illumination intensity is set to five grades from 5000lx to 8000lx, and ten aloe leaf electrical signals with identical growth states are collected under each illumination degree, and are grouped into one group in ten minutes. 500 sets of samples were collected for each illumination level, for a total of 2500 sets of samples.
The aloe collecting electrode comprises a positive electrode, a negative electrode and a reference electrode, the reference electrode is grounded, the positive electrode and the negative electrode are inserted into the neck of the aloe, and the distance between the positive electrode and the negative electrode is 10 cm.
The cut-off frequency of the low-pass filter is 100Hz, the cut-off frequency of the high-pass filter is 0.05Hz, and the power frequency wave trap adopts a non-recursive system FIR structure to simply preprocess the aloe electric signal.
The method comprises the steps of decomposing an aloe electric signal into 8 stable IMF components according to an EMD principle, selecting 4 IMF components according to a correlation principle in mathematics, and performing improved wavelet threshold function denoising on the aloe electric signal.
The modified wavelet threshold function model was chosen as db4 wavelet with a wavelet scale of 3. Obtaining a threshold matrix of each IMF component through MATLAB software, completely zeroing the coefficients smaller than the improved wavelet threshold in the IMF components, and completely reserving the coefficients larger than the improved wavelet threshold.
Splicing the 4 IMF vectors into a signal number sequence with the length of L x 4, marking as I, converting all the signal number sequences I into a two-dimensional matrix of a time domain and a frequency domain by using short-time Fourier transform (STFT), and using the two-dimensional matrix as the input of the convolutional neural network.
Let the label category of five samples be 1, 2, 3, 4, 5, and convert into a matrix in the form of 5 rows and 5 columns of vectors, with the index of label position in each row being 1 and the other positions being 0. The class probability is 1 and the other class probabilities are 0. The plant electric signal data set data is divided, wherein the training set is 70%, the testing set is 15%, and the verifying set is 15%.
The hierarchical structure design of the convolutional neural network comprises a data input layer, a convolutional calculation layer, a ReLU excitation layer, a pooling layer and a full connection layer. The CNN adopts a local connection and weight sharing mode on the basis of a multilayer perceptron, introduces a residual error network structure, uses a convolution layer for residual error learning, uses a convolution process for 3 times, and comprises a convolution layer, a batch normalization layer, a maximum pooling layer and a RELU layer.
The convolutional neural network is set to a convolution kernel size of 5x5, a move step size of 2, a pad of 0, and a sigmod activation function is used. The pooling layers are all maximally pooled, the size of the pooled convolution kernel is 2 x 2, and the step length is 2. The fully-connected layer uses flatten to transform each sample into a two-dimensional vector representation, the vector length being the product of channel, height and width. The fully connected layer has three layers, and the output of each layer of network is 120, 84 and 10 respectively.
The parameters of the long-short term memory neural network model are set as follows: a forgetting gate 64, an updating gate 80 and an output gate 2, and the output result of the complete connection layer of the convolutional neural network model is input into the LSTM network model.
The testing accuracy of the CNN-LSTM hybrid network model according to the classification result is 0.9683, and the testing false alarm rate is as low as 0.0357.
According to the experimental results, the CNN-LSTM combined neural network model is adopted to deeply learn the coupling relation between the plant electric signals and the illumination intensity, and finally the illumination intensity of the surrounding environment can be reflected through the electric signal intensity, so that the illumination intensity can be adjusted to enable the plant to be in the optimal illumination environment.
By the method, the plant leaf electric signals under different illumination degrees are collected, the electric signals are subjected to noise elimination processing by using empirical mode decomposition and an improved wavelet threshold method, and the electric signal array of a one-dimensional time domain is converted into a matrix of a two-dimensional time-frequency domain by short-time Fourier transform. Inputting the matrix data into a neural network, carrying out deep learning training on the plant electric signals through CNN and LSTM to obtain a relation model of the plant electric signals and the illumination intensity, and continuously adjusting the illumination intensity of the surrounding environment through the plant electric signals. The method solves the problems of delayed acquisition of the existing plant growth state evaluation information, long period and the like, and has simple culture and measurement methods and reliable evaluation effect.
The invention also provides an illumination information diagnosis system based on the plant electric signals, which comprises:
the training set construction module is used for collecting the plant electrical signals of the samples under different illumination degrees and constructing a training set;
the training module is used for training the mixed neural network model through the training set to obtain a relation model between the plant electric signals and the illumination intensity;
the current plant illumination intensity determining module is used for determining the illumination intensity of the current plant according to the relation model based on the electric signal data of the current plant;
the judging module is used for judging whether the illumination intensity of the current plant is within the preset range of the optimal illumination intensity;
and the adjusting module is used for adjusting the illumination intensity of the current plant when the illumination intensity of the current plant is not within the preset range of the optimal illumination intensity.
The training set construction module specifically comprises:
the dividing unit is used for dividing the sample plant electric signals according to time length, and the length of each section of the sample plant electric signals is L;
the empirical mode decomposition unit is used for carrying out empirical mode decomposition on each divided section of the plant electric signal to obtain n IMF vectors;
a selection unit, configured to select the IMF vectors with the first m high correlation numbers from the n IMF vectors;
the training set constructing unit is used for splicing the m IMF vectors into a plurality of signal number sequences and converting all the signal number sequences into a two-dimensional matrix of a time domain and a frequency domain by using short-time Fourier transform; the two-dimensional matrix constitutes a training set.
Wherein the training set constructing module further comprises: and the elimination unit is used for eliminating the interference noise in the m IMF vectors by adopting an improved wavelet threshold method.
Optionally, the eliminating unit specifically includes:
a judging subunit, configured to judge whether the wavelet coefficients of the m IMF vectors are greater than a coefficient threshold;
a retention subunit configured to retain the wavelet coefficients of the m IMF vectors when the wavelet coefficients are greater than a coefficient threshold;
a deletion subunit, configured to delete when the wavelet coefficients of the m IMF vectors are less than or equal to a coefficient threshold.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (10)

1. A plant electric signal-based illumination information diagnosis method is characterized by comprising the following steps:
collecting sample plant electric signals under different illumination degrees, and constructing a training set;
training the mixed neural network model through the training set to obtain a relation model between plant electric signals and illumination intensity;
determining the illumination intensity of the current plant according to the relation model based on the electric signal data of the current plant;
judging whether the illumination intensity of the current plant is within the preset range of the optimal illumination intensity;
if not, adjusting the illumination intensity of the current plant.
2. The method for diagnosing the illumination information based on the plant electrical signals according to claim 1, wherein the collecting of the electrical signal data of the sample plants under different illumination degrees to construct a training set specifically comprises:
dividing the sample plant electrical signals according to the time length, wherein the length of each section of the sample plant electrical signals is L;
carrying out empirical mode decomposition on each divided section of sample plant electric signal to obtain n IMF vectors;
selecting the IMF vectors with high first m relation numbers from the n IMF vectors;
splicing the m IMF vectors into a plurality of signal number sequences, and converting all the signal number sequences into a two-dimensional matrix of a time domain and a frequency domain by using short-time Fourier transform; the two-dimensional matrix constitutes a training set.
3. The method for diagnosing the illumination information based on the electrical signal of the plant according to claim 2, further comprising: and eliminating interference noise in the m IMF vectors by adopting an improved wavelet threshold method.
4. The plant electric signal-based illumination information diagnosis method according to claim 3, wherein the elimination of the interference noise in the m IMF vectors by using the improved wavelet threshold method specifically comprises:
judging whether the wavelet coefficients of the m IMF vectors are larger than a coefficient threshold value or not;
if yes, reserving;
if not, deleting.
5. The method as claimed in claim 1, wherein the hybrid neural network model includes a convolutional neural network and a long-short term memory cycle neural network.
6. A lighting information diagnosis system based on plant electric signals, comprising:
the training set construction module is used for collecting the plant electrical signals of the samples under different illumination degrees and constructing a training set;
the training module is used for training the mixed neural network model through the training set to obtain a relation model between the plant electric signals and the illumination intensity;
the current plant illumination intensity determining module is used for determining the illumination intensity of the current plant according to the relation model based on the electric signal data of the current plant;
the judging module is used for judging whether the illumination intensity of the current plant is within the preset range of the optimal illumination intensity;
and the adjusting module is used for adjusting the illumination intensity of the current plant when the illumination intensity of the current plant is not within the preset range of the optimal illumination intensity.
7. The plant electrical signal-based illumination information diagnosis system according to claim 6, wherein the training set construction module specifically comprises:
the dividing unit is used for dividing the sample plant electric signals according to time length, and the length of each section of the sample plant electric signals is L;
the empirical mode decomposition unit is used for carrying out empirical mode decomposition on each divided section of the plant electric signal to obtain n IMF vectors;
a selection unit, configured to select the IMF vectors with the first m high correlation numbers from the n IMF vectors;
the training set constructing unit is used for splicing the m IMF vectors into a plurality of signal number sequences and converting all the signal number sequences into a two-dimensional matrix of a time domain and a frequency domain by using short-time Fourier transform; the two-dimensional matrix constitutes a training set.
8. The plant electrical signal-based illumination information diagnosis method according to claim 1, wherein the training set construction module further comprises: and the elimination unit is used for eliminating the interference noise in the m IMF vectors by adopting an improved wavelet threshold method.
9. The method for diagnosing the illumination information based on the electrical signal of the plant according to claim 8, wherein the eliminating unit specifically comprises:
a judging subunit, configured to judge whether the wavelet coefficients of the m IMF vectors are greater than a coefficient threshold;
a retention subunit configured to retain the wavelet coefficients of the m IMF vectors when the wavelet coefficients are greater than a coefficient threshold;
a deletion subunit, configured to delete when the wavelet coefficients of the m IMF vectors are less than or equal to a coefficient threshold.
10. The plant electric signal-based illumination information diagnosis method according to claim 6, wherein the hybrid neural network model includes a convolutional neural network and a long-short term memory cycle neural network.
CN202111441798.XA 2021-11-30 2021-11-30 Illumination information diagnosis method and system based on plant electric signals Pending CN114118157A (en)

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TWI830545B (en) * 2022-12-19 2024-01-21 財團法人工業技術研究院 Method and systemfor intelligent control light supplement for plants

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