Sensors and Materials, Vol. 26, No. 7 (2014) 483–493
MYU Tokyo
S & M 1015

Evaluating Abnormal Conditionin Physiological Disorder Using a Fluctuation
Characteristic of Plant Bioelectric Potential
Shin-ichi Shibata*, Fumitake Sato1, Haruhiko Kimura2 and Takashi Oyabu3
School of Informatics, Department of Information System, Daido University,
10-3 Takiharu, Minami, Nagoya, Aichi 457-8530, Japan
1JSS Co., Ltd., 2-115 Shimeno, Kanazawa, Ishikawa 920-0058, Japan
2Graduate School of Natural Science & Technology, Kanazawa University,
Kakuma, Kanazawa, Ishikawa 920-1192, Japan
3Kokusai Business Gakuin College, 6-12 Minami, Kanazawa, Ishikawa 920-0919, Japan(Received January 15, 2014; accepted June 3, 2014)
Key words: plant bioelectric potential, evaluating growth condition, maximum entropy method,fluctuation characteristics, logistic regression analysis

For maintaining a steady food supply, the trend has been toward developing factorylike production methods in Japan. To date, there has been no indicator that can predictthe growth status of crops. We have accordingly focused on the bioelectric potential
inside the plant, which could be an indicator of plant growth. However, a few researchstudies have been conducted on its role in determining the plant growth status. In
this study, we aimed to use plant bioelectric potential as a means of determining
growth conditions. Fluctuation characteristics in plant bioelectric potential frequency
components should provide indicators of conditions between normal and inhibited
growth processes. We discuss that different growth condition factors were identified
using logistic regression analysis. This experiment yields a discrimination rate of 86.6%
obtained using the calculated logistic regression model between these conditions.

1.

Introduction

Recently, both the supply and safety of food have become increasing concerns, owing
to environmental issues.(1) At the same time, the decline in the agricultural populationand the low food self-sufficiency ratio are also becoming an issue in Japan. According toestimates by the Ministry of Agriculture and Fisheries in 2010, Japan’s self-sufficiency
rate was 39% by its calories and the elderly make up 60% of the agricultural population.(1,2)
The productivity per unit area in Japan is high compared with those in other countries,
Corresponding author: e-mail: shibatas@daido-it.ac.jp

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owing to the skill of seasoned farmers.(3) However, the impending retirement of theseskilled farmers, approximately 62% of whom are over 65 years old, is a serious concern.
Therefore, to maintain and improve self-sufficiency, the productivity of young andinexperienced farmers needs to increase.
The difference between skilled and inexperienced farmers is the ability to makeconsiderable judgments.(4) Skilled farmers, on the basis of their experience, can respondto the growth status of their crops. For example, they can diagnose plant diseases byvisual inspection and determine the appropriate treatment. Making a sound decision
about plant growth conditions requires much background knowledge regarding plants.
Nowadays, evaluating the freshness and quality of crops has to depend on inspection
by skilled practitioners, which necessitates determining optimal growth conditions
by objective evaluation. If standardized production can be conducted by objective
evaluation, farmers can steadily supply food owing to the solution of these issues. In thisstudy, we discuss the application of plant information to the objective evaluation of plant
growth conditions.
There are several methods suitable for measuring parameters necessary for growth,
including nitrogen level and dryness.(4) However, these methods are invasive because
they require collecting plant parts for analysis; therefore, it is difficult to use thesemethods in growing crops. For this reason, noninvasive means of evaluating growthsuch as chlorophyll fluorescence or 3D diagnostic imaging have become part ofagricultural research.(5) However, all these methods are useful only after the conditions
deteriorate. They cannot be used to make predictions before the conditions deteriorate.
The use of bioelectric potential as a noninvasive means of evaluating growth is being
investigated. The plant bioelectric potential is the potential difference caused byvariations in the concentrations of ions in the plant cell membranes. It has been shownto vary according to environmental conditions and the growth process.(6–9) However,
little research has been conducted on the bioelectric potential as an indicator of crop
growth.(10,11) If the bioelectric potential inside the plant could provide direct information
about the physiology and surrounding environment, this information could be applied toeffectively manage the growth environment.
We have been studying the use of plant bioelectric potential as a means ofdetermining the growth state, using the already established chlorophyll fluorescence asa standard. Previously, we tested the suitability of bioelectric potential measurements
under conditions that would definitely damage plant growth. The results showed that
there was a difference between the frequency components of the bioelectric potential
before and after spraying plants with herbicide, indicating physiological disorder.(12)In this study, we evaluated bioelectric potential frequency components using
chlorophyll fluorescence as a standard. A discriminatory model was formulated on thebasis of the results of the logistic regression analysis of frequency components calculated
from the characteristics of the fluctuation in the bioelectric potential as compared with
an abnormal condition. A system of diagnosing growth based on the bioelectric potential
inside the plant may aid in determining the growth state before this becomes visuallyapparent on the surface of the plant.

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2.

Experimental Methods

2.1

Experimental plant

2.2

Experimental system

The easily cultivated ornamental plant pothos (Epipremnum aureum) was used inthis study. It grows well over a wide range of temperatures, has broad leaves, and isamenable to experimentation. Figure 1 shows a photograph of this plant. Six subject
plants were used in the experiment and controlled in the same environment. After initial
measurements, herbicide was sprayed on each of the plants, and analysis was performedon the plants with significantly different chlorophyll fluorescence values.
A simplified diagram of the plant bioelectric potential measuring equipment is shownin Fig. 2. The recording electrode came from an electroencephalogram (EEG) (NE117A, NIHONKODEN). The electrode was attached to the leaf surface of the plantusing an electrically conducting paste (Z-401CE, NIHONKODEN). The ground wasplaced in the soil in the plant pot. Because the plant bioelectric potential is very small,the potential from the electrode was amplified by a differential amplifier (MEG-2100,
NIHONKODEN) through an input box (JB-220J, NIHONKODEN). A filter circuit wasincorporated into the amplifier to remove noise. To remove noise from the utility power,a band-pass filter (0.08–300 Hz) and a HUM filter were used. The amplified potential
was converted to a digital signal using an A/D converter and interfaced to a PC, whichanalyzed the data. The sampling frequency for the bioelectric potential was 100 Hz. The
measurement time was 1 min and each plant was measured 16 times. The measurement
period was 2 weeks. Because the bioelectric potential was very small, the effect ofnoise was a concern. All measuring equipment and plants were accordingly placed ina shield box. We blocked out light in the experiment room and experimented underfluorescent light to remove sunshine effects on measurement time. The illuminance wasapproximately 340 lux in the measurement time range considered.

Band-Pass Filter
Detection Range:(0.83-300Hz)
Hum Filter 5μV/DIV〜10mV/DIV
Input Box

Shield Box

Shield Box

Measurement Environment
Temperature
28.0°C
Humidity
52.7%RH
Illuminance342.4lux

Fig. 1 (left). (Color online) Photograph of subject plant.
Fig. 2 (right). (Color online) Measurement system in this experiment.

MEG-2100

Power Lab

PC

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2.3

Experimental agricultural chemical herbicides

3.

Methods of Analysis

3.1

Chlorophyll fluorescence

The herbicide used in the experiment was Nekosogi Quick Pro (Rainbow
Pharmaceutical Co., Ltd., registration number 21593). The liquid herbicide (15 ml) was
diluted 100 times, in accordance with the specifications. The amount of herbicide used
was based on the volume of the pot, which was calculated from the internal diameter ofthe pot (9 cm; pot 3). The herbicide (1.17 ml) was sprayed twice onto the surfaces of theleaves on days 1 and 9 of measurement.

Growth inhibition should become apparent 3–4 d after the herbicide application.
This is the time required for the suppression of growth factors in the plant particularly
vital amino acids and proteins. Chlorophyll fluorescence is used as a noninvasivemeans of evaluating the photosynthesis. The chlorophyll fluorescence value isexpressed as the plant growth condition and the value of approximately 0.7 indicatesthat the plant is healthy. This response was taken as an indicator for evaluating
growth.(13) In this experiment, measurements were made before and for 1 week after
herbicide application. The fluctuations in electrical potential were evaluated withreference to changes in the chlorophyll fluorescence value. The chlorophyll fluorescence
was measured using a pulse-modulator-type chlorophyll fluorescence meter (MINI-PAM,
WALZ). A photograph of this measurement procedure is shown in Fig. 3. Chlorophyll
Y (Fv/Fm) was measured on the surfaces of the leaves to which the plate electrodes forelectric potential measurement were attached. F refers to the fluorescence intensity(photon emission reaction rate). Fv/Fm is an index of chlorophyll fluorescence based ona typical saturation pulse measurement. It represents the largest quantum yield for thephotosynthesis II chemical pathway, as calculated using eq. (1).
Fv /Fm =(Fm − F0 )Fm

(1)Fig. 3. (Color online) Photograph of measurement procedure using chlorophyll fluorescencemeasured by MINI-PAM.

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Here, F0 is the instantaneous minimum fluorescence yield captured between small
fluctuations. Fm is the maximum fluorescence yield. Fv is calculated from the difference
between F0 and Fm. Because of variations in the measurements, six replicates wererecorded.

3.2

Characteristics of fluctuation in plant bioelectric potential

In this section, we describe the analysis of data collected according to the method
explained in § 2.2. Time series data for bioelectric potentials can vary considerably
owing to artificial stimuli such as the amount of light or direct contact.(7,10–12) The tiny
and irregular wave forms from internal sources are accordingly difficult to detect.
Prior attempts to find a correlation between the plant bioelectric potential andthe plant physiological response have been made by exposing the plant to different
environmental pollutants.(14–16) These studies showed a correlation between pollutant
gases and bioelectric potential. After the injection of pollutants, the power spectrum
showed a reduction. In this present study, we also focused on frequency component
analysis as a means of measuring correlations particularly the use of the characteristicsof the fluctuation in frequency component as a discrimination model. The fluctuation
analysis method is shown in Fig. 4. First, the bioelectric potential signal frequencies
are determined, and a straight line is approximated using both logarithmic axes. The
characteristic values are used as a means of formulating a model that can identify theslope and tangent of the approximate straight line calculated. In the frequency analysis,
we did not use conventional fast Fourier transformation (FFT), instead, we used the
maximum entropy method (MEM).(17) MEM for limited data provides a spectral estimate
by maximizing the autocorrelation function entropy. This method was used by Spcana,which is the software for frequency analysis(18) in this study because it is suitable for timeseries data over a short time frame and for measurement with high noise levels.

Fig. 4. (Color online) Analysis approach for fluctuation characteristics.

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3.3

Logistic regression

4.

Experimental Result

4.1

Chlorophyll fluorescence

Logistic regression is a method of analysis that models an explanatory sigmoid curveusing explanatory variables to explain objective variables. The objective variables are
represented by categorical variables. It is one method of multivariate analysis used
to construct a model for the presence and absence of certain events, such as disease
outbreaks, car accidents, or earthquakes.(19,20) Logistic regression analysis was chosen for
its special features. The explanatory variable need not follow a normal distribution, andthe method also allows the effect of the objective variable on each explanatory variable
to be determined. These features make it useful for identifying different parameters.
The state of the plant was fitted as an objective variable (see below), on the basis of theslope and intercept of the explanatory variable.
• 0: Growth inhibited• 1: Growth normal
The growth conditions were characterized using individual differences in chlorophyll
fluorescence on the basis of a model derived from logistic regression analysis to generate
new test data.

Figure 5 shows individual differences in chlorophyll fluorescence produced by
the herbicide, and the data represents the mean and standard deviation of chlorophyll
fluorescence. The first data set was recorded before herbicide application, and the eighth
was recorded 1 week after herbicide application. The ninth set of measurements wasmade before the second herbicide application, and the sixteenth was recorded 1 week
after the second application. There were two herbicide applications, and the data provide
a comparison between 1 week before and after. For two of the six plants, the chlorophyll
fluorescence after the second herbicide application fell to less than 0.4, and there was asignificant difference between the ninth and sixteenth data points.(11) The chlorophyll
fluorescence value decreased markedly on the sixteenth. The surface color of leaves
on the sixteenth turned from green to pale green or yellow. In contrast, there was noFig. 5. Chlorophyll fluorescence values of subjects A and B as a function of measurement time.

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significant difference observed among the other four subject plants. The characteristicsof the fluctuation in the bioelectric potential in two plants, A and B, were evaluated.

4.2

Plant bioelectric potential

The fluctuation characteristics in the two plants that showed a significant differencein chlorophyll fluorescence were calculated for each 4 d period. The distribution ofthese fluctuation characteristics is shown in Fig. 6. The vertical axis shows the slope
and the horizontal axis shows the tangent in these characteristics. The data groups forthe fluctuation characteristics on day 16 (marked with a ▼) tend to be grouped at the
top left, compared with the points for the other 3 days (1st marked with a square, 8thmarked with a circle, and 9th marked with a triangle). The data for day 16 show theeffect of growth inhibition, which caused the chlorophyll fluorescence to drop below thestandard value of 0.4. In this study, we assumed that the data for 3 d (1st, 8th, and 9th) is
the growth normal condition under which the values are above 0.6, and that the data forday 16 is the growth inhibited condition because of the half value of approximately 0.7,
which is observed under the healthy condition.
Figure 7 shows the approximate curves generated from the fluctuation characteristics
over 4 d. The approximate curves from chlorophyll fluorescence values of 0.4 or less (dashedline) are different from the curves for which the chlorophyll fluorescence value exceeded 0.6.
The above results show that the sixteenth data set represents growth inhibition,
whereas the sets for the other 3 d show normal growth. Linear discrimination is achieved
by logistic regression analysis.

(a)

(b)

Fig. 6. (Color online) Scatter plots in fluctuation characteristics [(a) subject A and (b) subject B].

(a)

(b)

Fig. 7. (Color online) Approximate curve in fluctuation characteristics [(a) subject A and (b)subject B; both logarithmic axes].

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4.3

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Logistic regression analysis

The fluctuation characteristics of the two plants (A and B) described in the previous
section were subjected to logistic regression analysis by first analyzing the data. Three
explanatory variables were compared: the slope, the intercept, and both variables. The
results are shown in Tables 1–3. This table shows that the discrimination rate washigher when both variables, rather than the fluctuation characteristics, were used. The
calculated logistic regression equation is shown below aslogp= 1.130xintercept − 12.877xslop + 53.297 .
1− p

(2)

Next, the test data were identified using eq. (2), and the growth status of the test data (C, D,
E, and F) was determined. The test data came from the test plants measured on the otherdays, where there were significant differences between chlorophyll fluorescence values.
The three explanatory variables were compared and the results are shown in Table 4.
Table 4 shows that even for the test data, using both fluctuation variables as adiscriminant increased the discrimination rate. Furthermore, for the test plant D1, thediscrimination rate was low, regardless of the feature used. Table 5 shows the standard
Table 1Result of discrimination rate in the case of using slope.
Predicted condition
Discrimination rate
0: Growth inhibited 1: Growth normal
0: Growth inhibited
20
10
66.7%
Correct condition
1: Growth normal
4
87
95.6%
Percentage total
88.4%
Table 2
Result of discrimination rate in the case of using the intercept.
Predicted condition
Discrimination rate
0: Growth inhibited 1: Growth normal
0: Growth inhibited
27
3
90.0%
Correct condition
1: Growth normal
4
87
97.8%
Percentage total
94.2%
Table 3Result of discriminination rate in the case of using the intercept.
Predicted condition
Discrimination rate
0: Growth inhibited 1: Growth normal
0: Growth inhibited
29
1
96.7%
Correct condition
1: Growth normal
2
89
97.6%
Percentage total
97.5%

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Table 4
Results of discrimination rate in relation to test data set.
C1
C2
D1
D2
E1
E2
F1
F2
Average
Chlorophyll fluorescence Y 0.65
0
0.66
0.4
0.54
0.42
0.64
0.49
Slope
100.0% 100.0% 12.5% 100.0% 100.0% 100.0% 75.0% 100.0% 85.9%
Indification
Intercept
100.0% 60.0% 50.0% 100.0% 100.0% 100.0% 68.8% 100.0% 84.8%
percentage
Slope and
100.0% 80.0% 12.5% 100.0% 100.0% 100.0% 100.0% 100.0% 86.9%
intercept

Table 5
Standard deviation among fluctuation characteristics in test data set.
Standard deviation 0: Growth inhibited 1: Growth normal
Slope
0.34
0.17
Intercept
3.91
2.56

deviations for the fluctuation characteristics calculated from the bioelectric potentials.
The standard deviations for both the slope and the intercept were 1.5 times greater in thehealthy plants than in the unhealthy plants.

5.

Discussion

The experimental results show that the tangent and slope of the fluctuation
characteristics for plants in a normal state of growth approximate a normal distribution.
The tangent and slope are reduced in the growth-inhibited plants. The herbicide creates
unfavorable growth conditions by inhibiting photosynthesis. During photosynthesis,
the chlorophyll in chloroplasts extracts an electron from water with the addition of light.
Blocking this function appears to be reflected in a reduction in bioelectric potential,
reducing the power spectrum and tangent. Random features similar to white noise aresaid to become strong as the slope of the fluctuation shows a decline.(21) The resultsappear to show that the growth state of the plant affects the fluctuation characteristics ofthe bioelectric potential, regardless of individual differences.
The logistic regression analysis shows that using the slope and tangent as
explanatory variables provides the best means of discrimination, although the accuracyof discrimination is low for plant D1. A possible explanation is that the standard
deviations of the fluctuation characteristics are larger for the normal growth state thanfor the growth-inhibited state. The chlorophyll fluorescence for plant D1 was 0.66, thehighest value among all the test data. However, these experiments have shown that if thechlorophyll fluorescence value is 0.4 or lower, it may be possible to identify the growthstate of the plant.

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Sensors and Materials, Vol. 26, No. 7 (2014)

Conclusions

In this paper, we describe experiments on measuring the bioelectric potential ofplants with inhibited growth as a way of determining their growth state and finding ameans of evaluating plant growth. We formulated a model for discriminating the growthstate using the characteristics of the fluctuation in the bioelectric potential of growthinhibited plants to improve our knowledge of how to evaluate plant growth. In thisstudy, six plants were sprayed with a herbicide, and those that showed a difference inchlorophyll fluorescence were subjected to logistic regression analysis to identify adiscriminant function. We used a herbicide that inhibited photosynthesis as a standard
way of determining the growth state. Photosynthetic inhibition can be measured bychlorophyll fluorescence. Plants that showed a significant difference in chlorophyll
fluorescence were subjected to the frequency analysis of their bioelectric potential using
MEM. Each coefficient in the fluctuation characteristics was treated as a feature todevise a discrimination model.
Logistic regression analysis showed that discrimination power was higher whenboth the slope and intercept features were used together than when either was usedalone. As a supplementary experiment, when the plants with a significant differencein chlorophyll fluorescence were tested, using the already calculated discriminatorymodel, a discrimination rate of 86.6% was obtained. When the individual chlorophyll
fluorescence values dropped to 0.4 or below, the growth state could be predicted to someextent.
In this study, we demonstrated that it is possible to distinguish two distinct growthstates: healthy and unhealthy. However, it is necessary to evaluate gradations in plant
health in more detail and to determine the length of time that growth inhibition has been
occurring. Additional experiments on finer gradations of plant growth state using alarger number of plants are required.

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