Proc. 3rd International Conference on Multispectral
Color Science (MCS'01), June 18-21, Joensuu,
Finland, 31-34.
Vitality and reflectance spectra of living plants: experiments
S. Aario, O. Silvén, H. Kauppinen
Machine Vision and Intelligent Systems Group
Department of Electrical Engineering
P.O.Box 4500, 90014 University of Oulu, Finland
Abstract
The aim of the study was to examine the correlation mechanisms between the vitality of cucumber (Cucumis
Sativus) leaves and their reflectance spectra. The reflectance spectra were measured line by line from cucumber leaves
with an imaging spectrograph in direct sunlight. For evaluating the water-index and the amount of chlorophylls and
carotenoids in the leaf, the ratio of two characteristic reflectances of different wavelengths were calculated for every
substance and values were compared to the reference measurements, which were done directly from the leaf. The changes
in the distribution of the water-index were also examined during one measurement day, when the plants were desiccated.
The water-index and chlorophyll concentration can be very accurately measured from the spectra, but the determination
of the amount of carotenoids still needs further investigation. The distributions give us more information than mean
values calculated over the leaf.
1
Introduction
The plants take the energy needed for photosynthesis from the radiation of the sun. When light falls onto a leaf, a part of the
radiation is reflected from the surface, a part is scattered from the inner parts and a part goes through the leaf, while a part is
absorbed for the photosynthesis. Various pigments, primarily chlorophylls and carotenoids, absorb light in the leaves.
Chlorophylls absorb blue and red light and carotenoids absorb blue-green light, but photosynthetic pigments in plants do not
effectively absorb green and yellow light, which are either reflected by leaves or passes through the leaves. The reflectance
of the plant is governed by leaf surface properties, such as hairiness, and internal structure, as well as by the concentration
and distribution of biochemical components [1,2,3].
In the visible spectrum (400-700nm), the plants reflect 6-10% of the radiation and thus the reflectance is low. That is
because of the photosynthetic pigments, which absorb these wavelengths for photosynthesis. In the near-infrared domain
(700-1300nm), where there are no strong absorption features, the magnitude of reflectance is governed by structural
discontinuities encountered in the leaf [2]. When the plants’ growing conditions change, also the structural characteristics
change, what can be seen in the reflectance spectra.
The plant’s fluorescence and reflection spectrums are known to correlate with the plant’s welfare and photosynthesis
activity [2,3,4]. This fact has been used in remote sensing applications, for example, the chlorophyll fluorescence peaks at
wavelengths 690 and 730nm have been used to indicate the amount of water and nitrogen, damages of the leafs, or growing
temperature [3]. Most measurements have been done using an average value from a large area, which causes the mixing of
spectra of soil and different parts of the plants and their leaves, as well as leafs in shade and direct sunlight.
Making environmental measurements in the greenhouses and basing the manipulation of the conditions on these
measurements traditionally optimize plants’ growing conditions in the greenhouses. If the optimization could be based on
the plants’ true state instead of environmental conditions, the farmers could achieve major savings, for example, when extra
fertilization is minimized. In this context optimization means that every part of the plant, roots, stem and leafs, are strong
enough to make the effective photosynthesis possible, but not too large, so that they would consume too much energy. In
our research, the hypothesis is that the welfare of the plants can be manipulated by controlling the delivery of the light,
water, nutrients, temperature and humidity, based on the information obtained from reflectance spectra of the plants. The
ultimate goal is to detect changes in the welfare of the plant in a greenhouse rapidly. For research purposes it is important to
be able to select the spectrum of the most interesting parts of the plants and even those of the leaves. For this purpose we
use an imaging spectrographs.
2
Measurements
The measurements were made in varying illumination conditions without artificial lights in the greenhouse. The
measurement sessions began in the early morning and new measurements were done every hour. Reference measurements
were made in order to get knowledge of the cucumber plants’ vitality during the measurement sessions. The cucumber
plants were also exposed to desiccation conditions during some of the measurement sessions in order to find out
relationships between plant drought stress and reflectance spectra.
The measurements were made with a 14 bit imaging spectrograph ImSpector (Specim Oy) between the wavelengths 380
and 1100nm, with approximately 1.5nm spectral resolution. The spectrograph took one spectral line image at once and then
the machine was moved to the right position for imaging the next line. The images were captured with Apogee matrix
camera. The resulting matrix’s size was 240*511*509*14 (amount of image lines * spatial resolution * spectral resolution *
precision). In Fig. 1 there is an example of the RGB-picture of the spectral image of the leaf and a spectra from the chosen
area.
Spektrograph's response
14000
12000
10000
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0
400
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700
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Wavelength [nm]
900
Figure 1: A RGB-picture constructed from the spectral data and the mean spectra of the chosen area.
The plant activity was measured for the reference by other means such as destructive laboratory tests for determining
water and pigment content of the leaf. Also some environmental conditions were measured, like air temperature and
humidity, soil moisture and the amount of radiance outside of the greenhouse.
The water-index was calculated from the reflectance spectra using the reflectance ratio R900/R970 where R970 is
reflectance at the known water absorption wavelength 970nm and R900 is the reflectance at the reference wavelength
900nm. The results were compared to water content measurements from the leaf. The chlorophyll concentration was
calculated using reflectance ratio R675nm/R700nm. The carotenoid concentration was estimated using reflectance ratio at
R430nm/R665nm. The calculations were done at the same area than the reference measurements.
3
Results
There is a correlation between the water-index measured from the spectrum and water content measurements from the leaf
(Fig.2A). Dependencies between the reflectance spectra and plant physiological status were verified in our tests for
chlorophyll and carotenoid concentration (Fig. 2B and 2C). Changes in the water-index distributions at different watering
conditions were also verified (Fig. 3).
A)
B)
0.4
Chlorophyll-index from the spectrum
(R675nm/R700nm)
5.5
Water-index (R900nm/R970nm)
5.4
5.3
5.2
5.1
5
4.9
4.8
4.7
0.38
0.36
0.34
0.32
0.3
4.6
4.5
82
83
84
85
86
87
88
2.5
89
Water content (%)
2.7
2.9
3.1
3.3
3.5
3.7
Measured chlorophyll concentration mg/g FW
3.9
0.6
C)
Carotenoid-index R430nm/R665nm
0.55
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.24
0.26
0.28
0.3
0.32
0.34
Measured carotenoid concentration mg/g FW
0.36
Figure 2: (A) Water-index calculated from reflectance spectrum compared to water content, measured
from water-stressed (filled circles) and un-stressed (empty circles) plant leafs at two different measurement
sessions (black and red circles). (B) Chlorophyll-index calculated from the reflectance spectrum compared
to measured chlorophyll concentrations. (C) Carotenoid-index calculated from the reflectance spectrum
compared to the measured carotenoid concentrations.
Shot 01
Shot 02
1500
2000
86,6%
86,6%
1500
1000
1000
500
0
500
4
4.5
5
5.5
6
0
4
4.5
Shot 03
5
5.5
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Shot 04
1000
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86,1%
800
85,9%
1500
600
1000
400
500
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0
4
4.5
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4.5
Shot 05
5
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Shot 06
1200
82,8%
1500
87,0%
1000
800
1000
600
400
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0
4
4.5
5
5.5
6
0
4
4.5
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5.5
6
Figure 3: Water-index distributions during one measurement day. The distributions at the left are from
stressed plants and at the right from non-stressed plants. In the corners there is the measured water
content of the leaf.
4
Discussion
Illumination conditions have a great effect on spectrum, not only to the magnitude, but also to the shape of the spectrum.
This is because of the illumination’s effect on plant’s photosynthesis activity: when there is not enough light, the plants do
not assimilate. The plants reflection at near-infrared domain depends greatly on the illumination intensity and differences
can also be seen on photosynthesis active area at 550-700nm. There are also differences between morning and evening
spectrums and spectrums from the different areas of the leaf.
The reference plants maintained their water content at high levels during the measurement day, even though the water
content of the soil dropped slightly in the middle of the day because of the sunlight and warming of the greenhouse. The
stressed plants tried to maintain their water content at sufficient levels as long as possible. Because no extra water was given
in time, the plants finally collapsed and their leaves lost a large amount of water quickly. It has to be remembered that plant
vitality measurements were done after the spectral measurements from the harvested leafs, what causes contingency to the
reference measurements and some noise to the Figure 2A.
The chlorophyll concentration can be predicted quite accurately from the reflectance spectra. It looks like the
chlorophyll-index would be a really accurate measure of the chlorophyll concentration in each image pixel. The amount of
different chlorophylls in the leaf leaves to be determined in later studies.
The carotenoid concentration can not be predicted accurately from the reflectance spectra. We might be using wrong
wavelengths in the analysis or the reference measurements are not as accurate as assumed.
Water-index distributions changes clearly when plants are in different conditions. In Fig. 3 the upper left distribution is
from the plant that has only just been started to desiccate. It still has enough water. The middle left distribution has already
been broadened. It was measured from a plant that had been without water for a few hours, and the consequences are
already visible. The lowest left distribution is measured from a plant that has been without water for several hours, and its
leaves were already floppy. The mean value has clearly shifted to the left. On the right side the distributions are not changed
significantly. The peak value is in slightly different position depending on the water content of the leaves, but the shape of
the distribution stays the same.
5
Conclusions
The water-index seems to be rather good predictor of leaf water content. With this method, water index can be measured at
any area of the leaf quite accurately. For example, the water index is higher if the measurement area is chosen over the stalk
or in the shade parts of the leaf, where the water content is higher.
There has not been any non-invasive and fast measurement technique for greenhouses available before. Analyzing
methods for spectral measurements still need to be developed in order to get more accurate results. Self-organizing maps are
a possibility to classify leaves with different spectral characteristics. Spectral measurements appear to hold a promise to
rapid feedback of the plant vitality.
Acknowledgments
This research was made in the Machine Vision and Intelligent Systems (MVIS) group at the University of Oulu, in
cooperation with Agrifood Research Finland (MTT) and the Department of Biology at the University of Oulu. The National
Technology Agency (Tekes) and University of Oulu funded this study.
References
[1] J.W.Hart, Light and Plant Growth, Unwin Hyman Ltd, London 1998.
[2] J.Peñuelas and I.Filella, Visible and near-infrared reflectance techniques for diagnosing plant physiological status, In:
Trends in plant science, Vol. 3, No. 4, April 1998, pp. 151-156.
[3] H.K.Lichtenthaler and U.Rinderle, The Role of Chlorophyll Fluorescence in the Detection of Stress Conditions in
Plants, In: CRC Critical Rewievs in Analytical Chemistry, Vol. 19. Supplement 1, 1988, pp. S-29-S85.
[4] T.M. Lillesand and R.W. Kiefer, Remote sensing and image interpretation. 4th edition. John Wiley & Sons, Inc. USA
(1999).