CN108491684B - Plant electric signal analysis method and system - Google Patents
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Abstract
The embodiment of the invention provides a plant electric signal analysis method and a plant electric signal analysis system, wherein the method comprises the following steps: acquiring direct-current coupling data of plant photoelectric rhythm signals according to plant electric signals generated by photoinduced plants; dividing the direct current coupling data into a positive trend data section and a negative trend data section; and respectively performing data fitting on each positive trend data segment and each negative trend data segment based on a plant cell dynamics equation, determining a target parameter value corresponding to each positive trend data segment and a target parameter value corresponding to each negative trend data segment, and analyzing the plant photoelectric rhythm signals according to the change of the target parameter values. According to the embodiment of the invention, the plant electric signals are subjected to parameter fitting by adopting a method of fitting the single trend curve in a segmented manner, the parameters obtained by fitting can reflect the slight change of the plant photoelectric signals, and the fitting process has definite plant cell electrophysiology significance and more accurately reflects the physiological state of the plant.
Description
Technical Field
The embodiment of the invention relates to the technical field of plant photoelectric signal detection, in particular to a plant electric signal analysis method and system.
Background
The electrocardiogram provides abundant data for heart-related diseases in medicine, and the electroencephalogram corresponding to the data also plays an important role in the current brain science research. However, applications in the field of electrophysiology are by no means restricted to animals. In the field of plant electrophysiology, a plant photoelectric rhythm graph which can reflect the response degree of plant leaves or other tissues to light is obtained by a reliable electric signal acquisition mode and a stable signal induction method. However, the plant photoelectric rhythm graph is different from an electrocardiogram, the period of the plant photoelectric rhythm graph is in the minute scale, and the recording mode is direct current coupling recording. And in essence, the plant photoelectric rhythm signal is not an electric signal emitted by a plant cell under the condition of no external condition change, but induces the plant to generate electric activity through illumination of different periods.
In the prior art, the analysis of plant electrical signals still adopts the same analysis mode as electrocardiogram and electroencephalogram, but neglects the plant physiological characteristics of the plants, so that the slight change information of the plant electrical signals cannot be reflected, and the analysis result of the plant electrical signals cannot correctly reflect the physiological state of the plants, so that an effective plant electrical signal analysis method is urgently needed to scientifically analyze the physiological state of the plants.
Disclosure of Invention
Embodiments of the present invention provide a plant electrical signal analysis method and system that overcomes, or at least partially solves, the above-mentioned problems.
In a first aspect, an embodiment of the present invention provides a plant electrical signal analysis method, including:
s1, acquiring direct current coupling data of the plant photoelectric rhythm signal according to the plant electric signal generated by the light-induced plant;
s2, dividing the direct current coupling data into a positive trend data section and a negative trend data section, wherein the derivative of all data points in the positive trend data section is larger than 0, and the derivative of all data points in the negative trend data section is smaller than 0;
and S3, respectively performing data fitting on each positive trend data segment and each negative trend data segment based on a plant cell dynamics equation, determining a target parameter value corresponding to each positive trend data segment and a target parameter value corresponding to each negative trend data segment, and analyzing the plant photoelectric rhythm signal according to the change of the target parameter value.
If the dc-coupled data of the plant photoelectric rhythm signal is a periodic signal, correspondingly, the step S2 specifically includes:
intercepting a target periodic signal from the direct current coupling data;
determining a number of target data points in the target periodic signal having a derivative of 0;
and dividing the direct current coupling data into a positive trend data segment and a negative trend data segment based on the target data points.
Wherein, step S2 further includes:
and if the acquisition time interval of any two target data points is smaller than a preset threshold, aggregating the two target data points into one target data point.
Wherein, step S3 specifically includes:
establishing a target equation based on a plant cell dynamics equation, wherein the target equation comprises a plurality of preset target parameters and represents the vector sum of cell membrane charged ions corresponding to different time points;
and respectively performing data fitting on each positive trend data segment and each negative trend data segment according to the target equation, determining a target parameter value corresponding to each positive trend data segment and a target parameter value corresponding to each negative trend data segment, and analyzing the plant photoelectric rhythm signal according to the change of the target parameter value.
Wherein the target equations comprise a single exponential equation and a multi-exponential equation, the single exponential equation having a higher priority than the multi-exponential equation;
if the fitting of any positive trend data segment and/or negative trend data segment by using the single exponential equation fails, fitting again by using the multi-exponential equation;
the preset target parameter number of the multi-exponential equation is N times of that of the single-exponential equation, and N is the exponential number of the multi-exponential equation.
Wherein, the single exponential equation specifically comprises:
h, tau, n and b are target parameters of the single exponential equation, Y is a potential value array of data, h represents a height parameter of a data curve, tau and n represent radian of the data curve and a change speed parameter of the data curve, b is a baseline parameter, and t is a data time array;
accordingly, the forward trend data segment is employed
Fitting is carried out, and the negative trend data segment is adopted
And (6) fitting.
In a second aspect, an embodiment of the present invention provides a plant electrical signal analysis system, including:
the acquisition module is used for acquiring direct current coupling data of the plant photoelectric rhythm signal according to the plant electric signal generated by the photoinduction plant;
the dividing module is used for dividing the direct current coupling data into a positive trend data section and a negative trend data section, wherein the derivative of all data points in the positive trend data section is greater than 0, and the derivative of all data points in the negative trend data section is less than 0;
and the analysis module is used for respectively performing data fitting on each positive trend data segment and each negative trend data segment based on a plant cell dynamics equation, determining a target parameter value corresponding to each positive trend data segment and a target parameter value corresponding to each negative trend data segment, and analyzing the plant photoelectric rhythm signal according to the change of the target parameter value.
According to a third aspect of the present invention, there is provided a plant electric signal analyzing apparatus comprising:
a processor, a memory, a communication interface, and a bus; the processor, the memory and the communication interface complete mutual communication through the bus;
the communication interface is used for information transmission between the test equipment and the communication equipment of the display device;
the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the plant electric signal analysis method.
A fourth aspect of the invention embodiments provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the above method.
A fifth aspect of the invention provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the above method.
According to the plant electric signal analysis method and system provided by the embodiment of the invention, the plant electric signal is subjected to parameter fitting by adopting a method of fitting a single trend curve in a segmented manner, the parameters obtained by fitting can reflect the slight change of the plant photoelectric signal, and the fitting process has definite plant cell electrophysiology significance and more accurately reflects the physiological state of the plant.
Drawings
FIG. 1 is a flow chart of a plant electrical signal analysis method provided by an embodiment of the invention;
FIG. 2 is a schematic diagram of a plant photoelectric rhythm diagram provided by an embodiment of the present invention;
fig. 3 is a structural diagram of a plant electrical signal analysis system according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments, but not all embodiments, of the present invention. 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.
It can be understood that in the field of plant electrophysiology, a reliable electrical signal acquisition mode and a stable signal induction method can be used for obtaining a plant photoelectric rhythm graph capable of responding to the response degree of plant leaves or other tissues to light, but the plant photoelectric rhythm graph is similar to an electrocardiogram and electroencephalogram type, but the plant per se has characteristics different from those of animals.
However, in the prior art, no special response is made to the characteristics of the plant electric signals, so that the analysis result cannot correctly reflect the physiological state of the plant.
In view of the problems existing in the prior art, fig. 1 is a flowchart of a plant electrical signal analysis method provided by an embodiment of the present invention, and as shown in fig. 1, the method includes:
s1, acquiring direct current coupling data of the plant photoelectric rhythm signal according to the plant electric signal generated by the light-induced plant;
s2, dividing the direct current coupling data into a positive trend data section and a negative trend data section, wherein the derivative of all data points in the positive trend data section is larger than 0, and the derivative of all data points in the negative trend data section is smaller than 0;
and S3, respectively performing data fitting on each positive trend data segment and each negative trend data segment based on a plant cell dynamics equation, determining a target parameter value corresponding to each positive trend data segment and a target parameter value corresponding to each negative trend data segment, and analyzing the plant photoelectric rhythm signal according to the change of the target parameter value.
Specifically, in S1, it is understood that the embodiment of the present invention substantially induces the plant with light, and preferably, the embodiment of the present invention circularly induces the plant with light of different intensities, so as to generate a plant electrical signal with a strong periodicity.
In the examples of the present invention, the local potential of the photoinduced plant is generally used as the main electric signal, but the variable potential can be analyzed as an electric signal similarly to the relatively slow action potential.
Furthermore, the embodiment of the present invention does not limit the manner of light induction, and the periodic repetitive induction is adopted to generate a periodic signal that is easy to observe and compare, but other induction manners may also be applied in the embodiment of the present invention.
A plant photoelectric rhythm graph can be established according to the direct-current coupling data of the plant electric signals, fig. 2 is a schematic diagram of the plant photoelectric rhythm graph provided by the embodiment of the invention, as shown in fig. 2, fig. 2 includes curves at two ends, the upper curve is an original plant photoelectric rhythm graph established according to the direct-current coupling data of the plant electric signals, the lower curve is a low-pass filtered curve of the original plant photoelectric rhythm graph, in fig. 2, the abscissa is electric signal acquisition time, and the ordinate is an acquired potential value.
It can be understood that the dc-coupled data of the plant photoelectric rhythm signal in S1 is two-dimensional data, the abscissa is the electrical signal acquisition time, the ordinate is the acquired potential value, and the acquired potential value is reflected in the photoelectric rhythm diagram, i.e. a segment of data waveform, and then for any point in the waveform, the derivative of the data point can be determined according to the tangent line of the data point position.
In S2, if the derivative of the data point is greater than 0, that is, the slope of the tangent to the data point position is greater than 0, and the derivative of the data point is less than 0, that is, the slope of the tangent to the data point position is less than 0, then the whole dc-coupled data segment can be divided into several segments of positive trend data segments with an upward trend and several segments of negative trend data segments with a downward trend.
It can be understood that since the plant photoelectric rhythm graph is the electrical activity of the plant and is the sum of the time-space superposition vectors of the recorded electrical signals of the population cells, and the physiological change degree of the plant is very slow in a short time, a method with higher resolution is needed to show the slight change of the plant electrical signals, and then the signal is divided into a plurality of single trend curves, so that the change characteristics of the plant electrical signals can be refined, and the analysis is more accurate.
In S3, the cell dynamics is a science to study the source, change, distribution and movement laws of cell populations in biological or artificial systems, and how various conditions affect these processes.
According to the embodiment of the invention, the function of ion extraction on the cell membrane is simulated through the charge-discharge model of the capacitor, so that the fitting process conforms to physiological significance, the fitting result is a plurality of target parameters conforming to curve trend, and the change of the curve can be accurately reflected through the change of the target parameters, so that the physiological state of the plant can be accurately reflected.
According to the plant electric signal analysis method provided by the embodiment of the invention, the plant electric signal is subjected to parameter fitting by adopting a method of fitting a single trend curve in a segmented manner, the parameters obtained by fitting can reflect the slight change of the plant photoelectric signal, and the fitting process has definite plant cell electrophysiology significance and more accurately reflects the physiological state of the plant.
On the basis of the foregoing embodiment, if the dc-coupled data of the plant photoelectric rhythm signal is a periodic signal, correspondingly, the step S2 specifically includes:
intercepting a target periodic signal from the direct current coupling data;
determining a number of target data points in the target periodic signal having a derivative of 0;
and dividing the direct current coupling data into a positive trend data segment and a negative trend data segment based on the target data points.
It is understood that the embodiment of the present invention preferably uses a periodic signal for analysis, as shown in fig. 2, and the illumination induction manner of the above embodiment can generate a periodic signal with a waveform similar to that of the partial curve of fig. 2. Preferably, the dc-coupled data provided by the embodiment of the present invention may have a significant periodicity, the cycle duration may have a large variation range, and each cycle may have several times of electrical activity variation data.
In the embodiment of the present invention, the periodic signal is first divided according to the signal period, and the signal of one period is analyzed as the target periodic signal in the embodiment of the present invention,
further, in the embodiment of the present invention, a signal point where the derivative of the target periodic signal is 0 is determined, the signal can be divided into several segments by determining a point where the derivative of the target periodic signal is 0, and if the photo-electric rhythm periodic signal is divided into n curves, the effective parameters of a set of data are 4 n.
It can be understood that, the dividing of the dc-coupled data into a positive trend data segment and a negative trend data segment based on the plurality of target data points classifies data segments between two adjacent target data points, if a slope of the segment of data is greater than 0, the segment of data is classified as the positive trend data segment, and if the slope of the segment of data is less than 0, the segment of data is classified as the negative trend data segment.
When the periodic data are analyzed, the target parameter values calculated by adopting the curves at the same position corresponding to different periods are compared, so as to determine the physiological state change condition of the plant at the same position in the same period, for example: the parameter of the first negative direction curve in the period 1 needs to be compared with the target parameter value of the first negative direction curve in other periods such as the period 2, and if the first negative direction curve does not exist in other periods such as the period 2, the comparison is not carried out.
On the basis of the above embodiment, step S2 further includes:
and if the acquisition time interval of any two target data points is smaller than a preset threshold, aggregating the two target data points into one target data point.
It will be appreciated that if the acquisition time interval between two target data points is too short, then the data is shown to be more aggregated during this time period, then the partitioning of the positive and negative trend data segments will be perturbed.
In order to overcome the above problem, in the embodiment of the present invention, it is preferable that two target data points, whose collection time intervals of two target data points are smaller than a preset threshold, are aggregated into 1 target data point, so that the time interval between any two adjacent target data points is not smaller than the preset threshold, and each segment of the data curve can be accurately divided.
Preferably, the threshold of the time interval is set to 10 seconds in the embodiment of the present invention, but the embodiment of the present invention is not limited to this specifically.
On the basis of the foregoing embodiment, step S3 specifically includes:
establishing a target equation based on a plant cell dynamics equation, wherein the target equation comprises a plurality of preset target parameters and represents the vector sum of cell membrane charged ions corresponding to different time points;
and respectively performing data fitting on each positive trend data segment and each negative trend data segment according to the target equation, determining a target parameter value corresponding to each positive trend data segment and a target parameter value corresponding to each negative trend data segment, and analyzing the plant photoelectric rhythm signal according to the change of the target parameter value.
It will be appreciated that the data were analyzed using fitted curve parameters in the examples, each trend curve reflecting the common results of multiple ions entering and exiting the cell membrane.
The method has physical significance, and in a cell dynamics model, the effect of ion extraction on cell membranes can be simulated through a charge-discharge model of a capacitor.
It should be noted that the target equation derived through the cellular dynamics equation conforms to physiological significance, so that the physiological state of the plant can be accurately reflected, then the optitoptions function in matlab is used for fitting according to the target equation, a target parameter value corresponding to a curve is output, and then the physiological state of the plant is analyzed according to the change of the parameter value.
On the basis of the above embodiment, the target equations include a single exponential equation and a multi-exponential equation, and the single exponential equation has higher priority than the multi-exponential equation;
if the fitting of any positive trend data segment and/or negative trend data segment by using the single exponential equation fails, fitting again by using the multi-exponential equation;
the preset target parameter number of the multi-exponential equation is N times of that of the single-exponential equation, and N is the exponential number of the multi-exponential equation.
The single exponential equation is specifically as follows:
h, tau, n and b are target parameters of the single exponential equation, Y is a potential value array of data, h represents a height parameter of a data curve, tau and n represent radian of the data curve and a change speed parameter of the data curve, b is a baseline parameter, and t is a data time array;
accordingly, the forward trend data segment is employed
Fitting is carried out, and the negative trend data segment is adopted
And (6) fitting.
The multi-exponential equation is specifically as follows:
Y＝a1ex1+a2ex2……anexn。
the single exponential equation provided by the embodiment of the invention
The vector sum of charged ions entering and exiting the cell membrane can be fitted, wherein b can be used as the basis for analyzing the change of the long-term physiological state of the plant; h, tau, n can be used as the analysis basis of the change of the physiological state in the short term of the plant.
On the premise that the single exponential equation cannot be well fitted, the embodiment of the invention preferably provides a multi-exponential equation for fitting data, and Y is a1ex1+a2ex2……anexnWherein a isiexiAnd representing a single exponential equation, wherein n is the number of exponents, and if the target parameters of the single exponential equation are m, the number of the target parameters of the multi-exponential equation is n m.
Furthermore, the embodiment of the invention simulates partial data before and after salt stress of the No. 961 wheat leaf.
According to the embodiment of the invention, continuous plant electric signals before and after salt stress of a Dekang 961 type wheat leaf are obtained, and target period data of a photoelectric rhythm graph is determined according to the period characteristics;
further, the embodiment of the invention divides the target period data into a positive trend data segment and a negative trend data segment according to a node with a derivative of 0, performs curve fitting on each segment of data by adopting a single exponential equation, performs fitting processing on 4 parameters by using an optitoptions function in matlab, inputs data as time t and potential Y, and obtains four characteristic parameters of fitting parameters h, tau, n and b.
TABLE 1 salt stress front data for model 961 de resistance wheat leaves
TABLE 2 post salt stress data for model 961 de wheat leaves
Table 1 is the extracted front part data of the salt stress of the texas 961 wheat leaves, and table 2 is the extracted rear part data of the salt stress of the texas 961 wheat leaves. The time interval between the two data was 20 minutes.
Through the comparison of the horizontal data in table 1 and table 2, it can be seen that the waveform change details can be fully embodied through the change of the 5 characteristic parameters, and the waveform change details directly reflect the physiological state of the plant at the moment.
According to the simulation result, the method for fitting the parameters of the plant photoelectric rhythm graph by adopting the method of fitting the single trend curve in a segmented manner is adopted, and finally obtained characteristic parameters can reflect the slight change of the plant electric signals.
And the fitting equation has definite plant cell electrophysiology significance, the plant electrical signal is slow, and the method using curve fitting is more suitable for plant electrophysiology.
Fig. 3 is a structural diagram of a plant electrical signal analysis system according to an embodiment of the present invention, and as shown in fig. 3, the system includes: the device comprises an acquisition module 1, a division module 2 and an analysis module 3, wherein:
the acquisition module 1 is used for acquiring direct current coupling data of plant photoelectric rhythm signals according to plant electric signals generated by photoinduction plants;
the dividing module 2 is configured to divide the dc-coupled data into a positive trend data segment and a negative trend data segment, where derivatives of all data points in the positive trend data segment are greater than 0, and derivatives of all data points in the negative trend data segment are less than 0;
the analysis module 3 is configured to perform data fitting on each positive trend data segment and each negative trend data segment respectively based on a plant cell dynamics equation, determine a target parameter value corresponding to each positive trend data segment and a target parameter value corresponding to each negative trend data segment, and analyze the plant photoelectric rhythm signal according to a change in the target parameter value.
For details, how to analyze the plant electrical signal by using the obtaining module 1, the dividing module 2 and the analyzing module 3 can refer to the above embodiments, and the embodiments of the present invention are not described herein again.
According to the plant electric signal analysis system provided by the embodiment of the invention, the plant electric signals are subjected to parameter fitting by adopting a method of fitting a single trend curve in a segmented manner, the parameters obtained by fitting can reflect the slight change of the plant photoelectric signals, and the fitting process has definite plant cell electrophysiology significance and is more specific to the field of plant electric signal analysis.
The embodiment of the invention provides a plant electric signal analysis system, which comprises: at least one processor; and at least one memory communicatively coupled to the processor, wherein:
the memory stores program instructions executable by the processor, the processor calls the program instructions to perform the methods provided by the method embodiments, for example, including: acquiring direct-current coupling data of plant photoelectric rhythm signals according to plant electric signals generated by photoinduced plants; dividing the direct current coupling data into a positive trend data section and a negative trend data section, wherein the derivative of all data points in the positive trend data section is greater than 0, and the derivative of all data points in the negative trend data section is less than 0; and respectively performing data fitting on each positive trend data segment and each negative trend data segment on the basis of a plant cell dynamics equation, determining a target parameter value corresponding to each positive trend data segment and a target parameter value corresponding to each negative trend data segment, and analyzing the plant photoelectric rhythm signal according to the change of the target parameter value.
The present embodiment discloses a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the method provided by the above-mentioned method embodiments, for example, comprising: acquiring direct-current coupling data of plant photoelectric rhythm signals according to plant electric signals generated by photoinduced plants; dividing the direct current coupling data into a positive trend data section and a negative trend data section, wherein the derivative of all data points in the positive trend data section is greater than 0, and the derivative of all data points in the negative trend data section is less than 0; and respectively performing data fitting on each positive trend data segment and each negative trend data segment on the basis of a plant cell dynamics equation, determining a target parameter value corresponding to each positive trend data segment and a target parameter value corresponding to each negative trend data segment, and analyzing the plant photoelectric rhythm signal according to the change of the target parameter value.
The present embodiments provide a non-transitory computer-readable storage medium storing computer instructions that cause the computer to perform the methods provided by the above method embodiments, for example, including: acquiring direct-current coupling data of plant photoelectric rhythm signals according to plant electric signals generated by photoinduced plants; dividing the direct current coupling data into a positive trend data section and a negative trend data section, wherein the derivative of all data points in the positive trend data section is greater than 0, and the derivative of all data points in the negative trend data section is less than 0; and respectively performing data fitting on each positive trend data segment and each negative trend data segment on the basis of a plant cell dynamics equation, determining a target parameter value corresponding to each positive trend data segment and a target parameter value corresponding to each negative trend data segment, and analyzing the plant photoelectric rhythm signal according to the change of the target parameter value.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, the method of the present application is only a preferred embodiment and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A method for analyzing plant electrical signals, comprising:
s1, acquiring direct current coupling data of the plant photoelectric rhythm signal according to the plant electric signal generated by the light-induced plant;
s2, dividing the direct current coupling data into a positive trend data section and a negative trend data section, wherein the derivative of all data points in the positive trend data section is larger than 0, and the derivative of all data points in the negative trend data section is smaller than 0;
and S3, respectively performing data fitting on each positive trend data segment and each negative trend data segment based on a plant cell dynamics equation, determining a target parameter value corresponding to each positive trend data segment and a target parameter value corresponding to each negative trend data segment, and analyzing the plant photoelectric rhythm signal according to the change of the target parameter value.
2. The method according to claim 1, wherein if the dc-coupled data of the plant photo-electric rhythm signal is a periodic signal, the step S2 specifically includes:
intercepting a target periodic signal from the direct current coupling data;
determining a number of target data points in the target periodic signal having a derivative of 0;
and dividing the direct current coupling data into a positive trend data segment and a negative trend data segment based on the target data points.
3. The method according to claim 2, wherein step S2 further comprises:
and if the acquisition time interval of any two target data points is smaller than a preset threshold, aggregating the two target data points into one target data point.
4. The method according to claim 1 or 2, wherein step S3 specifically comprises:
establishing a target equation based on a plant cell dynamics equation, wherein the target equation comprises a plurality of preset target parameters, and the target equation fits the vector sum of cell membrane charged ions corresponding to different time points;
and respectively performing data fitting on each positive trend data segment and each negative trend data segment according to the target equation, determining a target parameter value corresponding to each positive trend data segment and a target parameter value corresponding to each negative trend data segment, and analyzing the plant photoelectric rhythm signal according to the change of the target parameter value.
5. The method of claim 4, wherein the target equations comprise a single exponential equation and a multi-exponential equation, the single exponential equation having a higher priority than the multi-exponential equation;
if the fitting of any positive trend data segment and/or negative trend data segment by using the single exponential equation fails, fitting again by using the multi-exponential equation;
the preset target parameter number of the multi-exponential equation is N times of that of the single-exponential equation, and N is the exponential number of the multi-exponential equation.
6. The method according to claim 5, characterized in that said single exponential equation is in particular:
h, tau, n and b are target parameters of the single exponential equation, Y is a potential value array of data, h represents a height parameter of a data curve, tau and n represent radian of the data curve and a change speed parameter of the data curve, b is a baseline parameter, and t is a data time array;
accordingly, the forward trend data segment is employed
Fitting is carried out, and the negative trend data segment is adopted
And (6) fitting.
7. A plant electrical signal analysis system, comprising:
the acquisition module is used for acquiring direct current coupling data of the plant photoelectric rhythm signal according to the plant electric signal generated by the photoinduction plant;
the dividing module is used for dividing the direct current coupling data into a positive trend data section and a negative trend data section, wherein the derivative of all data points in the positive trend data section is greater than 0, and the derivative of all data points in the negative trend data section is less than 0;
and the analysis module is used for respectively performing data fitting on each positive trend data segment and each negative trend data segment based on a plant cell dynamics equation, determining a target parameter value corresponding to each positive trend data segment and a target parameter value corresponding to each negative trend data segment, and analyzing the plant photoelectric rhythm signal according to the change of the target parameter value.
8. A computer device, comprising a memory and a processor, wherein the processor and the memory communicate with each other via a bus; the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1 to 6.
9. A non-transitory computer-readable storage medium storing computer instructions that cause a computer to perform the method of any one of claims 1 to 6.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101021518A (en) * | 2006-11-01 | 2007-08-22 | 中国农业大学 | Method for detecting plant electric signal in green house and special equipment thereof |
| CN102879367A (en) * | 2012-09-24 | 2013-01-16 | 中国农业大学 | Photoelectric synchronous recording system and photoelectric synchronous recording method for electrical activity of high plant orthotopic living organisms |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP3505566B2 (en) * | 1995-06-23 | 2004-03-08 | 独立行政法人農業・生物系特定産業技術研究機構 | Physiological condition analyzer and method |
| JP3360210B2 (en) * | 1999-09-30 | 2002-12-24 | 静岡県 | Plant selection system based on biological information |
| DE102010027144A1 (en) * | 2010-07-09 | 2012-01-12 | Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. | Training method for an adaptive evaluation algorithm, a hyperspectral measuring device, and a device for applying a resource |
| CN103141320B (en) * | 2013-03-27 | 2015-07-22 | 西北农林科技大学 | Automatic plant root system monitoring system |
| EP3049792B1 (en) * | 2013-09-25 | 2019-09-25 | FOSS Analytical A/S | Method and device for determining a nutritional state of a plant |
| CN104849250B (en) * | 2015-05-05 | 2018-07-31 | 浙江大学 | Portable intelligent plant physiology condition detecting system and method |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101021518A (en) * | 2006-11-01 | 2007-08-22 | 中国农业大学 | Method for detecting plant electric signal in green house and special equipment thereof |
| CN102879367A (en) * | 2012-09-24 | 2013-01-16 | 中国农业大学 | Photoelectric synchronous recording system and photoelectric synchronous recording method for electrical activity of high plant orthotopic living organisms |
Non-Patent Citations (3)
| Title |
|---|
| Computing Methods in Analyzing Plant Electrical Signal;黄岚等;《农业工程学报》;20020930;第18卷(第5期);第39-42页 * |
| 两种植物电信号的自适应AR参数模型分析;陆静霞等;《南京农业大学学报》;20121231;第35卷(第6期);第142-147页 * |
| 长春花微弱电信号的ARIMA模型;王兰州等;《中国计量学院学报》;20070930;第18卷(第3期);第186-190页 * |
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