ISSN 1054-660X, Laser Physics, 2006, Vol. 16, No. 5, pp. 884–888.
© MAIK “Nauka /Interperiodica” (Russia), 2006.
Original Text © Astro, Ltd., 2006.
1. INTRODUCTION
Recently, much interest in the early detection of plant stress in agricultural crops has been generated by the increasing acceptance and use of precision agricul- tural technologies. Aerial and satellite remote sensing of vegetation color can be useful in detecting the gen- eral characteristics of vegetation [1]. However, reflec- tance from vegetation color sensing lacks the specific- ity and the selectivity necessary to discriminate plant stress, because different stress conditions may cause similar pigment losses. In addition, changes in color in most cases represent a late response of plants to stress [1, 2]. Incorrect diagnosis due to an inability to discrim- inate between stresses may lead to wrong management interventions with serious economic consequences.
Over the past decade, laser-induced fluorescence (LIF) of plants has been explored as a tool in vegetation studies [3–5]. Compared with other optical techniques, LIF may be a more accurate indicator of the physiolog- ical state of plants and may be able to detect the impacts of environmental stresses on them at earlier growth stages. The UV excitation of green leaves induces two distinct types of fluorescence: a blue-green fluores- cence (BGF) in the 400- to 600-nm range, and Chloro- phyll fluorescence (ChlF) in the red to far-red region (650–800 nm) of the spectrum. The relative intensities of these two fluorescent bands are highly sensitive to intrinsic leaf properties and environmental factors [6, 7]. In addition, LIF is a nondestructive and nonintrusive technique in plant biochemistry, physiology, and ecol- ogy, and it is easy to use for many purposes in labora-
tory and fieldwork [7, 8]. Chlorophyll fluorescence emission has been successfully used to detect mineral deficiencies, water and temperature stresses, and patho- gens [3]. Experiments involving water stress were reported successfully for maple leaves [9], olive leaves [10], and soybean [11]. Successful detection of patho- gens in plants using LIF was also reported in the litera- ture [3, 12].
In this work, we apply LIF in orange trees (Citrus aurantium L.) in laboratory to detect water stress and a pathogen infection called Citrus Canker, caused by the Xanthomonas axonopodis pv. Citri bacteria. We have chosen such species and plant disease due to its eco- nomical importance for Brazil, which is responsible for the production of half of the processed orange juice in the world with a market of around 10 billion US dollars per year. Our results suggest that it is possible to detect water stress with good accuracy using LIF. For citrus canker, the results show that a more detailed investiga- tion is necessary to discriminate between this disease and others. Nevertheless, these results may stimulate the development of a portable device to be used in the field.
2. MATERIALS AND METHODS 2.1. Plant Leaves and Water Stress Model The plants used in this report were orange trees (Cit- rus aurantium L.). Ten leaves were selected from each plant for a diary fluorescence measurement. A very simple water stress model was used in the experiment;
Fluorescence Spectroscopy Applied to Orange Trees
L. G. Marcassaa, M. C. G. Gasparotob, J. Belasque Juniorb, E. C. Linsc, F. Dias Nunesc, and V. S. Bagnatoa
a IFSC–Universidade de São Paulo, São Carlos, São Paulo, 135665-90, Brazil
b Departamento Cientifico–FUNDECITRUS, Araraquara, São Paulo, 20114 8 070-40, Brazil
c DES-Universidade Federal de Pernambuco, Recife, Pernambuco, 50670-901, Brazil
e-mail: lgmarcassa@uol.com.br Received December 26, 2005
Abstract—In this work, we have applied laser-induced fluorescence spectroscopy to investigate biological pro- cesses in orange trees (Citrus aurantium L.). We have chosen to investigate water stress and Citrus Canker, which is a disease caused by the Xanthomonas axonopodis pv. citri bacteria. The fluorescence spectroscopy was investigated by using as an excitation source a 442-nm 15-mW HeCd gas multimode discharge laser and a 532- nm 10-mW Nd3+:YAG laser. The stress manifestation was detected by the variation of fluorescence ratios of the leaves at different wavelengths. The fluorescence ratios present a significant variation, showing the possibility to observe water stress by fluorescence spectrum. The Citrus Canker’s contaminated leaves were discriminated from the healthy leaves using a more complex analysis of the fluorescence spectra. However, we were unable to discriminate it from another disease, and new fluorescence experiments are planned for the future.
PACS numbers: 07.60.RD, 07.57.TY, 82.50.-M DOI: 10.1134/S1054660X06050215
LASER METHODS IN BIOLOGY AND MEDICINE
FLUORESCENCE SPECTROSCOPY APPLIED TO ORANGE TREES 885
in this model, the plants under water stress did not receive water supply for fourteen days. The control plants continue to receive one liter of water per day. All plants were placed in a room with low natural lumines- cence and without direct contact with the environment.
They were fertilized with the same organic material and placed in identical vases to guarantee identical condi- tions.
2.2. Plant Leaves and Citrus Canker Samples About 500 orange leaves, presenting visual symp- toms of citrus canker, were selected from several fields around São Carlos city for the fluorescence measure- ment. The control leaves (about 500) were also col- lected from healthy plants. For comparison, leaves con- taminated with Citrus Variegates Chlorosis (caused by Xylella fastidiosa) were also collected [13]. This is important because both diseases present very similar visual symptoms. In order to discriminate between these two diseases, laboratorial tests were carried out after the fluorescence measurement.
2.3. Fluorescence Spectroscopy System Our fluorescence spectroscopy system is composed of (i) one spectrometer, which goes from 350 nm up to 850 nm; (ii) one Y-shaped fiber, which delivers the laser light through one central fiber and collects the fluores- cence from the tissue using six periferical fibers; and (iii) two excitation sources, one at 443 nm (He:Cd laser) and the other at 532 nm (second harmonic of Nd:YAG). The laser power is on the order of 10 mW, assuring no thermal effect on the incident spot. In Fig. 1 we show a schematic diagram of the fluorescence spec- troscopy system. We should point out that the signal
from the backscattering is about one thousand times more intense than the one from the inelastic scattering.
To simplify the analyses, we used an optical filter to reduce it one thousand times; this way, the signals (from back- and inelastic scattering) present a compa- rable intensity. Using this system, we submitted the leaves to the fluorescence spectroscopy technique. The entire procedure was carried out under aseptic condi- tions. The control groups showed no changes. The mea- surement was done keeping the catheter probe at a typ- ical distance of 2 mm from the leaf to prevent back- ground noise and fluorescence limitations as atmospheric scattering, leaf geometry, and low-power light capture. In each leaf, three spectra were taken.
3. FLUORESCENCE SPECTRUM ANALYSIS In Fig. 2, we show two typical fluorescence spectra for the excitation at 443 nm (solid line) and 532 nm (dashed line) for a normal leaf. It is easy to observe that the overall shape of the fluorescence spectrum depends on the wavelength of the laser light source. In order to simplify the analysis, we normalize the fluorescence of both tissues through the backscattering for the spec- trum obtained at both excitation wavelengths peaks.
Light in the green region excites the chlorophyll fluo- rescence directly, while light in the UV-blue region excites the fluorescence of chlorophyll and other pig- ments [14]. Chlorophyll fluorescence is an accurate and nondestructive probe of photosynthetic efficiency which can reflect the impacts of environmental changes on a plant. According to Cerovic et al. [3], the fluores- cence of leaves in the blue-green region and red–far red region change independently in response to different physiological and environmental factors, and these changes can be detected accurately by fluorescence Laser
Monocromator Computer
Optical fiber
Sample
Fig. 1. Schematic diagram of the fluorescence spectroscopy system.
886 MARCASSA etal.
ratios. In this work, we will use the following fluores- cence ratios to detect water stress:
(1) Red to far red (RF/FRF): This parameter is defined by the ratio between the fluorescence intensity
at 685 nm and the fluorescence intensity at 735 nm. It depends only the chlorophyll content of the leaf and is used to measure water stress in plants [9].
(2) Blue to red (BF/RF): It is defined by the ratio between the fluorescence intensity at 452 nm and the fluorescence intensity at 685 nm.
(3) Blue to far red (BF/FRF): It is defined by the ratio between the fluorescence intensity at 452 nm and the fluorescence intensity at 735 nm.
The BF/RF and BF/FRF ratios combined compose the blue to chlorophyll fluorescence ratio (BF/ChlF).
This fluorescence ratio is more sensitive to detect stress in plants and environmental changes than RF/FRF ratio, because the blue fluorescence and the chlorophyll fluorescence have distinct origins. The laser-induced fluorescence at 532 nm is limited to supply only the RF/FRF ratio.
In order to detect the citrus canker disease, we have defined two figures of merit (FM) to differentiate a nor- mal leaf from a leaf contaminated by citrus canker.
Considering the fluorescence spectrum, we can define the following relations:
(1)
where the figures of merit are the integration of the spectrum (I(λ)) in different wavelength ranges.
4. RESULTS AND DISCUSSION 4.1. Water Stress
From Fig. 2, it is easy to observe that the overall shape of the fluorescence spectrum depends on the wavelength of the laser light source. Therefore, in order to extract general information about the stress process, we have used three different ratios.
4.1.1. RF/FRF ratio. In Fig. 3a, we show the RF/FRF ratio as a function of water stress time for the water stress and control trees using a 442-nm laser as light source. The results shown here are an average over all the samples. We observe that the discrimination between the two situations is more evident starting from the 11th day of water stress. This variation may be caused by water stress because chlorophyll participates directly in the photosynthesis process. According to the results obtained by Dahn and coworkers in maize [15], the FR/FRF ratio decreased under water stress. How- ever, these results disagree with the results in maple leaves [8], where the RF/FRF increases between 0.81–
1.1. In order to make such discrimination more evident, we have plotted the (RF – FRF)/(RF + FRF) ratio (Fig. 3b). This ratio may be more useful than the RF/FRF for differentiation; however, more experiments
FM1
I( ) νν d
650 800
∫
I( ) νν d
547 620
∫
--- FM2
I( ) νν d
712 750
∫
I( ) νν d
680 712
∫
---,
= =
2000 1500 1000 500 0
400 450 500 550 600 650 700 750 800 850 Wavelength, nm Fluorescence, arb. units
442 nm
532 nm
Fig. 2. Typical fluorescence spectrum of orange leaves for the excitation at 442 (solid line) and 532 nm (dashed line).
2.0
1.5
1.0
Water stress Control RF/FRF, arb. units
(a)
0.4 0.3 0.2 0.1
0 2 4 6 8 10 12 14 16
Time, days (b)
(RF – FRF)/(RF + FRF), arb. units
Fig. 3. (a) The RF/FRF ratio as a function of water stress time for the water stress and control trees using a 442-nm excitation light. (b) The (RF – FRF)/(RF + FRF) ratio as a function of water stress time for the water stress and control trees using a 442-nm excitation light.
Water stress Control
FLUORESCENCE SPECTROSCOPY APPLIED TO ORANGE TREES 887
will be carried out to verify it. The RF/FRF ratio obtained using 532 nm as a light source did not show any discrimination between the stress and control sam- ples, and it is therefore not possible detect the water stress.
4.1.2. BF/RF and BF/FRF ratios. The Fig. 4 shows the BF/RF and BF/FRF ratios using excitation at 442 nm. We do not observe any significant difference between the control and stressed leaves. In the litera- ture, the BF/RF and BF/FRF ratios are used to detect mineral deficiencies in plants. Typical N-deficient plants show a large increase in the BF/RF and BF/FRF ratio compared to control plants, while K-deficient plants present a large decrease in BF/RF and BF/FRF ratio. The model chosen in this work does not prevent the control of minerals in plants; therefore, the decrease observed in the graph may be due to mineral deficien- cies, because during the whole experiment their vases did not receive any mineral complementation.
4.2. Citrus Canker
In Fig. 5, we show a graph where the figure of merit (FM2) is plotted versus the figure of merit (FM1), for healthy (open circles) and leaves contaminated with cit- rus canker (open triangles). It is possible to observe clearly that the contaminated leaves are concentrated in a region of low FM1 and FM2. In fact, most of the con- taminated leaves are in the region where FM1 < 400 and FM2 < 1.7. On the other hand, the healthy leaves are dispersed in a broader range. We believe that this dis- 0.015
0.010
0
Water stress Control BF/RF, arb. units
(a)
0.020 0.015 0.010 0.005
0 2 4 6 8 10 12 14 16
Time, days (b)
BF/FRF, arb. units 0.005
Fig. 4. BF/RF and BF/FRF ratios as a function of water stress time using excitation at 442 nm.
4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0
FM2, arb. units
Citrus canker Healthy leaves
0 500 1000
FM1, arb. units
Fig. 5. Figure of merit (FM2) is plotted versus the figure of merit (FM1) for healthy leaves and leaves contaminated by citrus canker.
2.5
2.0
1.5
1.0
0.5
0
FM2, arb. units
Citrus canker Citrus canker leaves
0 200 400
FM1, arb. units
Fig. 6. Figure of merit (FM2) is plotted versus the figure of merit (FM1) for citrus canker leaves and citrus variegates chlorosis.
Water stress Control
888 MARCASSA etal.
persion may reflect the fact that the samples were col- lected in several different fields, which may have differ- ent conditions of water and nutrient supply. For a future field application, such dispersion must be taken into account.
We also compared the results obtained for leaves contaminated with citrus canker with leaves contami- nated with Citrus Variegates Chlorosis in Fig. 6; as pointed out before, both diseases present very similar visual symptoms. Unfortunately, it is not possible to observe a clear discrimination between the two dis- eases, because the points are very much overlapped.
This may indicate that both diseases produce similar pigment losses, which modifies the chlorophyll fluores- cence in the same way. This is inconvenient for any future field application, because the management of such diseases is completely different and costly. At the moment, we are carrying out time-resolved fluores- cence spectroscopy to try to differentiate between these two diseases.
5. CONCLUSIONS
In this work, we concluded that the RF/FRF ratio may be used successfully as a detection parameter of water stress by a UV-blue excitation laser in orange trees. The BF/RF and BF/FRF ratios did not show any ability to detect the water stress. As pointed out above, the water stress model used in this work may present limitations in detecting water stress using chlorophyll ratios due to the lack of mineral-deficiency control in the experiment. This is a progress report; other mea- surements will be carried out to understand the plants' response to environmental effects and the development of other techniques of water stress detection.
We are also able to successfully discriminate between a healthy leaf and one contaminated by citrus canker using the analysis of figures of merit of the flu- orescence spectrum. However, we were unable to dis- tinguish between the Citrus Canker and the Citrus Var- iegates Chlorosis; we believe that this is due to the fact that both diseases cause a similar loss of pigments in
the leaves. At the moment, we are carrying out a differ- ent analysis and experiments involving time-resolved fluorescence to try to discriminate between these two diseases.
ACKNOWLEDGMENTS
This work received financial support from Fapesp (Fundação de Ámparo à Pesquisa do Estado de São Paulo), Programa CEPID, and was carried out at the Center for Optical and Photonics Research.
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