JXB Advance Access originally published online on November 21, 2006
Journal of Experimental Botany 2007 58(4):807-814; doi:10.1093/jxb/erl207
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Leaf Scale Studies: Combination Imaging and Stress Diagnosis |
Multispectral fluorescence and reflectance imaging at the leaf level and its possible applications
1Botanical Institute 2, University of Karlsruhe, Kaiserstrasse 12, D-76128 Karlsruhe, Germany
2Unit of Plant Hormone Signaling and Bio-imaging, Ghent University, K.L. Ledeganckstraat 35, B-9000 Gent, Belgium
3Julius-von-Sachs-Institut für Biowissenschaften, University of Würzburg, Julius-von-Sachs-Platz, D-97082 Würzburg, Germany
* To whom correspondence should be addressed. E-mail: claus.buschmann{at}botanik2.uni-karlsruhe.de
Received 21 May 2006; Accepted 7 September 2006
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Abstract |
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Images taken at different spectral bands are increasingly used for characterizing plants and their health status. In contrast to conventional point measurements, imaging detects the distribution and quantity of signals and thus improves the interpretation of fluorescence and reflectance signatures. In multispectral fluorescence and reflectance set-ups, images are separately acquired for the fluorescence in the blue, green, red, and far red, as well as for the reflectance in the green and in the near infrared regions. In addition, ‘reference’ colour images are taken with an RGB (red, green, blue) camera. Examples of imaging for the detection of photosynthetic activity, UV screening caused by UV-absorbing substances, fruit quality, leaf tissue structure, and disease symptoms are introduced. Subsequently, the different instrumentations used for multispectral fluorescence and reflectance imaging of leaves and fruits are discussed. Various types of irradiation and excitation light sources, detectors, and components for image acquisition and image processing are outlined. The acquired images (or image sequences) can be analysed either directly for each spectral range (wherein they were captured) or after calculating ratios of the different spectral bands. This analysis can be carried out for different regions of interest selected manually or (semi)-automatically. Fluorescence and reflectance imaging in different spectral bands represents a promising tool for non-destructive plant monitoring and a ‘road’ to a broad range of identification tasks.
Key words: Fruit quality, hypersensitive reaction, near infrared reflectance, photosynthetic activity, stress, tobacco mosaic virus (TMV), UV screening
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Introduction |
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Multispectral fluorescence and reflectance imaging of plant material are applied from the microscopic scale to remote sensing via satellites. The captured images are used to identify plants, and to characterize their state of health, from basic science up to applied quality assessment, as well as in environmental control and monitoring. Through recent developments in optical technologies, images of low light signals (e.g. fluorescence) can now be acquired under conditions of high background/excitation light, where previously only point measurements were possible. Imaging has the inherent advantage of showing the distribution and variation of signals over a sample; the temporal changes of these patterns can be monitored by time-lapse acquisition. This review focuses on applications on the leaf or fruit level, with an imaging field of view from several centimetres to
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Applications |
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Chlorophyll fluorescence and photosynthesis
Commercial instruments for chlorophyll (Chl) fluorescence imaging use high speed shutters to synchronize fluorescence detection with the pulse width-modulated output of LED (light-emitting diode) sources. These systems can use three types of light: (i) a low intensity pulsed measuring light with a constant modulation frequency; (ii) a medium intensity continuous actinic light; and (iii) single high intensity pulses saturating photosynthesis. Fluorescence is measured in a broad band in the long wavelength range of the red Chl fluorescence. By applying only measuring light or, in addition, actinic light and/or saturating light, different fluorescence parameters can be detected, which are then interpreted in terms of photosynthetic activity (Buschmann, 1999). In most applications, the maximum quantum yield (FV/FM), the actual or effective quantum yield (
F/F'M), the photochemical quenching (qP), and/or the non-photochemical quenching (qN or NPQ) are calculated for each pixel of the image and presented in a black and white or a false-colour scale (Genty and Meyer, 1994; Siebke and Weis, 1995; Niyogi et al., 1997; Oxborough and Baker, 1997b; Osmond et al., 1999; Leipner et al., 2001; Omasa and Takayama, 2003; Soukupová et al., 2003; Nedbal and Whitmarsh, 2004; Oxborough, 2004b; Quilliam et al., 2005). Alternatively, Rfd values (ratio of fluorescence decrease; Lichtenthaler and Babani, 2004) can be calculated. When using a multicolour fluorescence imaging system, the ratio between the short wavelength (F690) and the long wavelength Chl fluorescence (F740; Buschmann and Lichtenthaler, 1998; Lichtenthaler et al., 2005) can be used as a measure of photosynthetic activity and Chl content of the leaf, respectively.
UV screening
Leaves exposed to the sun accumulate UV-absorbing substances (i.e. mainly hydroxycinnamic acids and flavonoids) in the epidermis, shielding the Chl-containing tissue below the epidermis from UV damage (Caldwell et al., 1998). Using fluorescence ratios of UV (UVF690) and blue (BF690) excited Chl fluorescence as proposed by Bilger et al. (1997), significantly higher UV screening (or UV shielding) was detected in sun leaves of beech (Fagus sylvatica L.) in comparison with shade leaves (Lenk and Buschmann, 2006). In addition, the upper side of each leaf type had a higher shielding than the lower side (see Fig. 1 for shade leaves). Fluorescence imaging also visualizes the distribution of UV screening pigments (Fig. 1). In this case, an inhomogeneous UV screening was revealed for the lower side of sun leaves, as well as for the upper side of the shade leaves. In addition, a significant difference in UV screening was also found between the sun-exposed and the shaded halves of grape wine berries (Kolb et al., 2003).
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Fruit quality
Camera-based fruit quality monitoring applications are of great interest, because they could allow a non-destructive, fast method to determine the time of harvest in the field and/or to control and predict post-harvest fruit quality. The content of pigments, e.g. Chls and carotenoids, which can be directly assessed from the fluorescence and reflectance images, is, however, not of major importance to agricultural production. One of the most important components from the commercial viewpoint is sugar content. Studies with different fruits indicated that the decay of Chl parallels the ripening process (e.g. banana, Smilie et al., 1987; Blackbourn et al., 1990; kiwi, Kempler et al., 1992; papaya, Bron et al., 2004). By using an imaging system, higher Chl fluorescence (BF690) was found for unripe white grape clusters than for ripe grapes, as expected, since the Chl content decreases during the ripening of the grapes (Fig. 2). Ripening can also be monitored by reflectance measurements which reveal changes of fruit colour in the visible spectral range (Bron et al., 2004) or changes in the near infrared as determined by light scattering properties (Sharpe and Barber, 1972).
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Stress imposed on fruits may be detected via changes in Chl fluorescence (Smillie et al., 1987; Smillie, 1992), in a similar way to that described above for leaves. Surface defects reducing the quality of fruits can also be detected by fluorescence (Nedbal et al., 2000) and reflectance imaging techniques (Kim et al., 2002; Mehl et al., 2004).
Near infrared reflectance
Leaf reflectance is very high in the near infrared at 800 nm. Leaves are also largely transparent in the near infrared (Merzlyak et al., 2002), which is demonstrated by an 800 nm image showing a small leaf behind a superimposed larger leaf. In the near infrared, the absorption by leaf pigments is strongly reduced, and thus both reflectance and transmittance are much higher than in the visible spectral range. The small leaf behind the large leaf can not be seen on the reflectance images acquired in the visible range (Fig. 3, upper part). Images taken at 800 nm are not influenced by the distribution of leaf pigments, as exemplified by a variegated leaf showing a homogeneous intensity in an R800 image (Fig. 3, lower part). The amount of reflectance in the near infrared is determined by the scattering of the leaf tissue (Gausman et al., 1970). A decrease of the reflectance at 800 nm may be taken as an indicator of reduced aerial interspaces in the mesophyll of leaves under stress conditions (Gausman and Quisenberry, 1990; Buschmann et al., 1991).
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Disease symptoms
Leaf diseases in plants can be readily revealed with Chl fluorescence imaging, due to their effects on light energy usage (Soukupová et al., 2003; Chaerle et al., 2004; Scharte et al., 2005). Plant resistance responses to pathogen attack commonly involve the accumulation of specific compounds with either signalling or antimicrobial properties. The latter can include both structural modifications to impair pathogen ingress and direct toxic effects on the pathogen (e.g. phytoalexins). Key components in plant disease resistance include salicylic acid (Shah, 2003) and phenylpropanoid compounds, including flavonoids and coumarins (Dixon et al., 2002). Phenolic compounds can also accumulate as a stress response, without being linked to increased resistance (Krecic-Stres et al., 2005). As phenolics generally have the property of emitting fluorescence after UV excitation (Lichtenthaler and Schweiger, 1998), they provide an elegant way to reveal stress symptoms (Lichtenthaler et al., 1996; Chaerle and Van Der Straeten, 2000, 2001). As an example, infection of tobacco with Phytophthora nicotianae leads to a hypersensitive reaction with early callose deposition and cell wall lignification at a later stage (Scharte et al., 2005). At the microscopic scale, lignification was visualized as yellow fluorescence after UV excitation whereas callose could only be revealed as green fluorescence after destructive staining. Parallel high resolution Chl fluorescence imaging revealed a very low signal at the time of lignification. Possibly these signals can also be monitored at a macroscopic scale. During the hypersensitive reaction of the resistant tobacco cultivar Samsun NN induced by tobacco mosaic virus, parallel Chl and blue-green fluorescence imaging were used for continuous follow-up and early detection of symptoms (Fig. 4; Chaerle et al., 2006). Early symptoms of virus infection can be seen in the fluorescence images; the blue (UVF440) and green (UVF550) fluorescence images show a better contrast and larger affected area than the red (BF690) fluorescence images. Fluorescence changes are detected earlier than symptoms visible to the human eye or captured with a separate RGB (red, green, blue) camera (video signal). Near infrared reflectance imaging revealed symptoms only marginally earlier than monitored in the visual spectrum. Moreover, the discrimination of the two main components (salicylic acid and scopoletin) known to accumulate during the hypersensitive reaction was possible. The increased blue-green fluorescence appearing during the hypersensitive reaction was attributed largely to the accumulation of scopoletin, which has a higher blue and green fluorescence signal than salicylic acid (Fig. 5). Infiltrated salicylic acid emits almost no fluorescence in the green. Applications of fluorescence imaging in screening for disease and stress resistance have a clear potential for quantitative assessment (Chaerle et al., 2007).
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Instrumentation |
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A fluorescence or reflectance imaging system consists of the following components (Fig. 6: general scheme): (i) irradiation/excitation light source(s); (ii) detector(s); (iii) image acquisition and data handling routines; and (iv) image processing.
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Irradiation/excitation light source(s)
The choice of excitation light source determines the fluorescence information that can be obtained. Light sources may provide continuous output or modulated signals up to kHz frequencies. As an advantage of modulated light sources, the fluorescence signal can be specifically detected through elimination of the background signal (ambient and reflected light). This also has the benefit that the intensity of the modulated light can better be reduced to a level at which no photosynthetic activity is induced and, consequently, the Chl fluorescence is at a low and constant level (F0=‘ground fluorescence’; Nedbal and Whitmarsh, 2004).
In addition, other properties of the light source, such as intensity and spectral characteristics, are also important. Excitation with different wavelength ranges may provide information from different plant tissues (Vogelmann, 1993). In the visible range, blue excitation is absorbed with high quantum efficiency by carotenoids and by Chls. Thus instruments with blue light excitation give information about the Chl fluorescence of the upper leaf tissue below the epidermis facing the detector. Red light is absorbed only by Chls and thus does not have such a broad absorption range as blue light where carotenoids are also absorbing. Green light with low Chl absorption penetrates even deeper into the leaf than blue or red light (Buschmann and Lichtenthaler, 1998). In contrast to excitation with visible (blue) light, UV radiation—in addition to red Chl fluorescence—also excites blue-green fluorescence emitted mainly from the epidermis. The Chl fluorescence excited with UV strongly depends on the presence of UV-absorbing substances in the epidermis. These compounds reduce the penetration of UV excitation to the Chl molecules located in the tissue below the epidermis.
The light sources for fluorescence excitation consist of lasers, lamps, or LEDs.
Lasers: Continuous lasers are used on the microscopic scale for fluorescence imaging of blue-green fluorescent components of the epidermis (Hideg et al., 2002) or for revealing the red fluorescence of chloroplasts (Vácha et al., 2000). Continuous laser excitation for fluorescence imaging at the leaf scale is rather expensive and causes more optical problems (e.g. beam expansion for homogeneous irradiance). However, pulsed laser light sources with short and intensive pulses are used which allow the measurement of blue-green and Chl fluorescence in ambient light (Edner et al., 1994; Lichtenthaler and Miehé, 1997; Kim et al., 2003).
Lamps: Xenon and halogen lamps with high energy output are frequently used in combination with bandpass filters, particularly for exciting Chl fluorescence with blue light. Continuous light sources have the advantage of providing relatively intensive fluorescence signals. However, transients of the Chl fluorescence induction are too fast to be captured by commonly used charged coupled device (CCD) cameras (Nedbal and Whitmarsh, 2004). In this case, measurements are carried out at key time points (Omasa et al., 1987) or in the steady-state at different excitation light levels (Genty and Meyer, 1994; Siebke and Weis, 1995; Oxborough and Baker, 1997a; Chaerle and Van Der Straeten, 2000, 2001; Ciscato et al., 2001; Rolfe and Scholes, 2002). Excitation with UV for measuring blue-green and Chl fluorescence was used in continuous mode for steady-state fluorescence imaging (Kim et al., 2001). Xenon light sources in pulsed mode were applied for exciting Chl fluorescence in the visible spectrum (Fenton and Crofts, 1990) and for exciting blue-green and Chl fluorescence with UV radiation (Buschmann et al., 2000).
LEDs: LEDs are used more and more for fluorescence imaging. With LEDs, the light level and pulse duration can be better controlled than with lamps. Orange LEDs are often chosen for measuring Chl fluorescence kinetics, because they are available with higher intensity output than blue LEDs (Nedbal et al., 2000). Blue LEDs are, however, preferable because blue light is more efficiently absorbed by the leaf pigments. Recently, high intensity blue LEDs became available and are nowadays used for Chl fluorescence imaging (Barbagallo et al., 2003; Hill et al., 2004; Valcke, 2007). A light source with LEDs emitting in the UV range was developed for blue-green and Chl fluorescence imaging (Dieleman and Marcelis, 2005).
For reflectance imaging, various light sources can be used, the basic requirement being that the light source should have sufficient quanta fluence rate in the spectral range where the reflected signal is to be detected. For reflectance imaging in a broad spectral range in the visible and the near infrared, a halogen lamp is adequate. However, when measuring in certain narrow bands, LEDs may be more suitable. In the field, solar irradiation is most often used. Importantly, reflectance imaging then has to be complemented with radiometric data in order to take into account the spectral characteristic of the incident illumination and its changes in time.
Detector(s)
While reflectance images in the visible range can be detected with commercial digital cameras, fluorescence images usually require a more sensitive and thus more expensive system. Fluorescence imaging is possible with conventional photography (Björn and Forsberg, 1979) but, for better image statistics and analysis, fluorescence imaging set-ups with attached monochrome CCD cameras combined with different emission filters are usually chosen (Omasa et al., 1987; Genty and Meyer, 1994; Siebke and Weis, 1995; Nedbal et al., 2000; Ciscato et al., 2001; Chaerle and Van Der Straeten, 2001; Barbagello et al., 2003; Hill et al., 2004). Application of colour CCDs is not reasonable because of their lower sensitivity. For fluorescence imaging at the leaf level, conventional monochrome CCD cameras with 300–1000 elements per row and column are used. If one wants to measure a particularly low fluorescence signal (e.g. F0), cooled CCD cameras with a longer integration time and low dark signal are needed (Oxborough and Baker, 1997a; Kim et al., 2001; Rolfe and Scholes, 2002; Omasa and Takayama, 2003; Oxborough, 2004a). In general, the use of image-intensified cameras is advantageous because of their shorter integration time, which helps to eliminate the influence of ambient light (Edner et al., 1994; Lichtenthaler and Miehé, 1997; Kim et al., 2003). Conventional CCDs are working with a frequency of 50 Hz in ‘interlaced’ mode. More expensive high speed CCDs are available for acquiring images at higher capture rate.
Image acquisition and data handling routines
In addition to light sources and detectors, fluorescence imaging systems normally have two other important components: a computer with imaging software for controlling measurement and acquisition, and a frame grabber for acquiring and digitizing the video signal. However, nowadays commercial imaging systems are available using CCD cameras with a Firewire interface (CF Imager of Technologica Ltd and Imaging-PAM of Walz) or a USB2 interface (FluorCam of Photon Systems Instruments), which feature digitization within the camera, thus eliminating the need for a frame grabber. Other machine vision applications (e.g. traffic surveillance, Bramberger et al., 2004) apply smart cameras (cameras with real-time image processing for a specific application). Up to now, research with smart cameras in fluorescence imaging of plants has not been published. Specific applications can be foreseen for real-time, high throughput (e.g. fruit post-harvest) screening.
Image processing
The commercial imaging systems are supplied with dedicated fluorescence imaging software, which combine the above-described image acquisition and several image processing tools in one program. They allow the calculation and analysis of fluorescence parameters pixel by pixel or for a region of interest (‘Animater’, Lichtenthaler et al., 1996; ‘Fluorimager’, Oxborough and Baker, 1997a; ‘FluorCam’, Nedbal et al., 2000; ‘ImagingWin’, Hill et al., 2004). However, image processing can be done independently of the image acquisition software. Numerous general and more specific image processing software tools are available for bio-imaging, remote sensing, or machine vision applications (‘Optilab’ and ‘NIH Image’, Meyer et al., 2001; ‘ENVI’, Kim et al., 2002; Leinonen and Jones, 2004; Mehl et al., 2004; ‘ERDAS’, Omasa and Takayama, 2003; ‘ImageJ’, Rasband, 2006 and ‘ImageMagick’, Chaerle et al., 2004; ‘ImageJ’, Lenk and Buschmann, 2006; Lenk et al. 2006; for a general overview see http://image.nih.gov/software/ip_packages.html).
Image analysis is often restricted to the calculation of statistics (mean value and standard deviation of the whole image or regions of interest). Other basic statistic tools, such as profiles and histograms, are often used for presenting image data. Recently, advanced fluorescence image processing methods were developed using filters, edge detection, object recognition, image segmentation, etc. (Lefcourt et al., 2005; Valcke, 2007). Automated algorithms were applied to create overview montages of different images (Rolfe and Scholes, 2002) or image series (Chaerle et al., 2002; Lenk et al., 2006). Nowadays automated methods help to detect regions of interest and calculate statistics for image series (Chaerle et al., 2007).
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Perspectives and outlook |
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The combination of fluorescence and reflectance images taken in different spectral ranges will allow a better description and interpretation of the obtained signatures. Adding hyperspectral resolution could even lead to non-destructive quantification (and possibly identification) of individual substances. New types of diodes for illumination with higher intensity and in a broader spectral range (from UV-C to infrared) will help to improve the signal detection (Khan, 2006).
Thermographic measurements could be a valuable asset to aid stress characterization and, in particular, to characterize water status of leaves (Jones, 2004; Chaerle et al., 2007). The development of software routines that automatically select and process the most contrasting images could be a way ahead to design efficient plant monitoring systems (L Nedbal, Matous K, Trtilek M, unpublished data). Statistical texture features including defect detection, object recognition, image segmentation, or content-based image retrieval of the images have a clear potential for furthering a broad range of identification tasks (Valcke, 2007). The application of these approaches to images captured in parallel with multiple sensors (reflectance, fluorescence, thermal, and possibly others) will further the possibilities of early stress detection.
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Acknowledgements |
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DVDS and CB are grateful to the European Community for financial support provided through the Human Potential Programme under contract HPRN-CT-2002-00254, STRESSIMAGING. SL and DH are research fellows in this European network. LC is a post-doctoral fellow of the Research Foundation-Flanders. We thank Mr Alexander Doll from the Winzergenossenschaft (cave cooperative) Weingarten/Baden, Germany for providing us with the grape berries.
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Abbreviations |
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Chl, chlorophyll; CCD, charged coupled device; LED, light-emitting diode; RGB, red, blue, green.
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References |
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Barbagallo RP, Oxborough K, Pallett KE, Baker NR. (2003) Rapid, noninvasive screening for perturbations of metabolism and plant growth using chlorophyll fluorescence imaging. Plant Physiology 132 485–493.
Bilger W, Veit M, Schreiber L, Schreiber U. (1997) Measurement of leaf epidermal transmittance of UV radiation by chlorophyll fluorescence. Physiologia Plantarum 101 754–763.[CrossRef]
Björn L-O and Forsberg AS. (1979) Imaging by delayed light emission (phytoluminography) as a method for detecting damage to the photosynthetic system. Physiologia Plantarum 47 215–222.[CrossRef]
Blackbourn HD, Jeger MJ, John P, Telfer A, Barber J. (1990) Inhibition of degreening in the peel of bananas ripened at tropical temperature. IV. Photosynthetic capacity of ripening bananas and plantains in relation to changes in the lipid composition of ripening banana peel. Annals of Applied Biology 117 163–174.
Bramberger M, Brunner J, Rinner B, Schwabach H. (2004) Real-time video analysis on an embedded smart camera for traffic surveillance. Proceedings of the IEEE real-time and embedded technology and application symposium, 174–181.
Bron IU, Ribeiro RV, Azzolini M, Jacomino AP, Machado EC. (2004) Chlorophyll fluorescence as a tool to evaluate the ripening of ‘Golden’ papaya fruit. Postharvest Biology and Technology 33 163–173.
Buschmann C. (1999) Photochemical and non-photochemical quenching coefficients of the chlorophyll fluorescence: comparison of variation and limits. Photosynthetica 37 217–224.[CrossRef]
Buschmann C, Langsdorf G, Lichtenthaler HK. (2000) Imaging of the blue, green and red fluorescence emission of plants. Photosynthetica 38 483–491.[CrossRef]
Buschmann C and Lichtenthaler HK. (1998) Principles and characteristics of multi-colour fluorescence imaging of plants. Journal of Plant Physiology 152 297–314.
Buschmann C, Rinderle U, Lichtenthaler HK. (1991) Detection of stress in coniferous forest trees with the VIRAF spectrometer. IEEE Transactions of Geoscience and Remote Sensing 29 96–100.[CrossRef]
Caldwell MM, Björn LO, Bornman JF, Flint SD, Kulandaivelu G, Teramura AH, Tevini M. (1998) Effects of increased solar ultraviolet radiation on terrestrial ecosystems. Journal of Photochemistry and Photobiology B: Biology 46 40–52.[CrossRef]
Chaerle L, Hagenbeek D, De Bruyne E, Valcke R, Van Der Straeten D. (2004) Thermal and chlorophyll-fluorescence imaging distinguish plant–pathogen interactions at an early stage. Plant and Cell Physiology 45 887–896.
Chaerle L, Leinonen I, Jones H, Van Der Straeten D. (2007) Monitoring and screening plant populations with combined thermal and chlorophyll fluorescence imaging. Journal of Experimental Botany 58 773–784.
Chaerle L, Lenk S, Hagenbeek D, Buschmann C, Van Der Straeten D. (2006) Multi-color fluorescence imaging for early detection of the hypersensitive reaction to tobacco mosaic virus. Journal of Plant Physiology doi:10.1016/j.jplph.2006.01.011.
Chaerle L and Van Der Straeten D. (2000) Imaging techniques and the early detection of plant stress. Trends in Plant Science 5 495–501.[CrossRef][Web of Science][Medline]
Chaerle L and Van Der Straeten D. (2001) Seeing is believing: imaging techniques to monitor plant health. Biochimica et Biophysica Acta 1519 153–166.[Medline]
Chaerle L, vande Ven M, Valcke R, Van Der Straeten D. (2002) Visualisation of early stress responses in plant leaves. Proceedings of SPIE 4710 417–423.[CrossRef]
Ciscato M, Sowinska M, vanden Ven M, Heisel F, Deckers T, Bonany J, Valcke R. (2001) Fluorescence imaging as a diagnostic tool to detect physiological disorders during storage of apples. Acta Horticulturae 553 507–512.
Dieleman JA and Marcelis LFM. (2005) Closed system for water and nutrient management in horticulture (CLOSYS, Final report)Wageningen Plant Research International.
Dixon RA, Achnine L, Kota P, Liu C-J, Reddy MSS, Wang L. (2002) The phenylpropanoid pathway and plant defence: a genomics perspective. Molecular Plant Pathology 3 371–390.[CrossRef]
Edner H, Johansson J, Svanberg S, Wallinder E. (1994) Fluorescence lidar multicolor imaging of vegetation. Applied Optics 33 2471–2479.
Fenton JM and Crofts AR. (1990) Computer aided fluorescence imaging of photosynthetic systems—application of video imaging to the study of fluorescence induction in green plants and photosynthetic bacteria. Photosynthesis Research 26 59–66.
Gausman HW, Allen WA, Cardenas R, Richardson AJ. (1970) Relation of light reflectance to histological and physical evaluation of cotton leaf maturity. Applied Optics 9 545–552.
Gausman HW and Quisenberry JE. (1990) Spectrophotometric detection of plant leaf stress. In Katterman F (Ed.). Environmental injury to plantsSan Diego Academic Press pp. 257–280.
Genty B and Meyer S. (1994) Quantitative mapping of leaf photosynthesis using chlorophyll fluorescence imaging. Australian Journal of Plant Physiology 22 277–284.
Hideg É, Juhász M, Bornman JF, Asada K. (2002) The distribution and possible origin of blue-green fluorescence in control and stressed barley leaves. Photochemical and Photobiological Sciences 1 934–941.[CrossRef]
Hill R, Schreiber U, Gademann R, Larkum AWD, Kühl M, Ralph PJ. (2004) Spatial heterogeneity of photosynthesis and the effect of temperature-induced bleaching conditions in three species of corals. Marine Biology 144 633–640.[CrossRef]
Jones HG. (2004) Application of thermal imaging and infrared sensing in plant physiology and ecophysiology. Advances in Botanical Research Incorporating Advances in Plant Pathology 41 107–163.[CrossRef]
Kempler C, Kabaluk JT, Toivonen MA. (1992) Effect of environment and harvest date on maturation and ripening of kiwifruit in British Columbia. Canadian Journal of Plant Science 72 863–869.
Khan A. (2006) A bug-beating diode. Nature 441 299.[CrossRef][Medline]
Kim MS, Lefcourt AM, Chao K, Chen YR, Kim I, Chan DE. (2002) Multispectral detection of fecal contamination on apples based on hyperspectral imagery: part I. Application of visible and near-infrared reflectance imaging. Transactions of ASAE 45 2027–2037.
Kim MS, McMurtrey JE, Mulchi CL, Daughtry CST, Chappelle EW, Chen YR. (2001) Steady-state multispectral fluorescence imaging system for plant leaves. Applied Optics 40 157–166.
Kim MS, Lefcourt AM, Chen YR. (2003) Multispectral laser-induced fluorescence imaging system for large biological samples. Applied Optics 42 3927–3934.[Web of Science][Medline]
Kolb CA, Kopeck
J, Riederer M, Pfündel E. (2003) UV screening by phenolics in berries of grapevine (Vitis vinifera). Functional Plant Biology 30 1177–1186.[CrossRef]
Krecic-Stres H, Vucak C, Ravnikar M, Kovac M. (2005) Systemic potato virus YNTN infection and levels of salicylic and gentisic acids in different potato genotypes. Plant Pathology 54 441–447.[CrossRef]
Lefcourt AM, Kim MS, Chen YR. (2005) A transportable fluorescence imaging system for detecting fecal contaminants. Computers and Electronics in Agriculture 48 63–74.
Leinonen I and Jones HG. (2004) Combining thermal and visible imagery for estimating canopy temperature and identifying plant stress. Journal of Experimental Botany 55 1423–1431.
Leipner J, Oxborough K, Baker NR. (2001) Primary sites of ozone-induced perturbations of photosynthesis in leaves: identification and characterization in Phaseolus vulgaris using high resolution chlorophyll fluorescence imaging. Journal of Experimental Botany. 52 1689–1696.
Lenk S and Buschmann C. (2006) Distribution of UV-shielding of the epidermis of sun and shade leaves of the beech (Fagus sylvatica L.) as monitored by multi-colour fluorescence imaging. Journal of Plant Physiology doi:10.1016/j.jplph.2005.08.018.
Lenk S, Buschmann C, Van Der Straeten D, Chaerle L. (2006) Using Image J for processing fluorescence and reflectance image sequence of plant leaves. Proceeding of the ImageJ User and Developer Conference, Luxembourg, May 18–19, 2006, 123–127.
Lichtenthaler HK and Babani F. (2004) Light adaptation and senescence of the photosynthetic apparatus. Changes in pigment composition, chlorophyll fluorescence parameters and photosynthetic activity. In Papageorgiou GC and Govindjee GC (Eds.). Chlorophyll a fluorescence: a signature of photosynthesisDordrecht, The Netherlands Springer pp. 713–736.
Lichtenthaler HK, Lang M, Sowinska M, Heisel F, Miehé JA. (1996) Detection of vegetation stress via a new high resolution fluorescence imaging system. Journal of Plant Physiology 148 599–612.
Lichtenthaler HK, Langsdorf G, Lenk S, Buschmann C. (2005) Chlorophyll fluorescence imaging of photosynthetic activity with the flash-lamp fluorescence imaging system. Photosynthetica 43 355–369.[CrossRef]
Lichtenthaler HK and Miehé J. (1997) Fluorescence imaging as a diagnostic tool for plant stress. Trends in Plant Science 2 316–320.[CrossRef]
Lichtenthaler HK and Schweiger J. (1998) Cell wall bound ferulic acid, the major substance of the blue-green fluorescence emission of plants. Journal of Plant Physiology 152 272–282.[Web of Science]
Mehl PM, Chen YR, Kim MS, Chan DE. (2004) Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations. Journal of Food Engineering 61 67–81.[CrossRef]
Merzlyak MN, Chivkunova OB, Melø TB, Naqvi KR. (2002) Does a leaf absorb radiation in the near infrared (780–900 nm) region? A new approach to quantifying optical reflection, absorption and transmission of leaves. Photosynthesis Research 72 263–270.[CrossRef][Web of Science][Medline]
Meyer S, Saccardy-Adji K, Rizza F, Genty B. (2001) Inhibition of photosynthesis by Colletotrichum lindemuthianum in bean leaves determined by chlorophyll fluorescence imaging. Plant, Cell and Environment 24 947–955.[CrossRef]
Nedbal L, Soukupová J, Kaftan D, Whitmarsh J, Trtílek M. (2000) Kinetic imaging of chlorophyll fluorescence using modulated light. Photosynthesis Research 66 3–12.[CrossRef][Web of Science][Medline]
Nedbal L and Whitmarsh J. (2004) Chlorophyll fluorescence imaging of leaves and fruits. In Papageorgiou GC and Govindjee GC (Eds.). Chlorophyll a fluorescence: a signature of photosynthesisDordrecht, The Netherlands Springer pp. 389–407.
Niyogi KK, Björkman O, Grossman AR. (1997) Chlamydomonas xanthophyll cycle mutants identified by video imaging of chlorophyll fluorescence quenching. The Plant Cell 9 1369–1380.[Abstract]
Omasa K, Shimazaki KI, Aiga I, Larcher W, Onoe M. (1987) Image analysis of chlorophyll fluorescence transients for diagnosing the photosynthetic system of attached leaves. Plant Physiology 84 748–752.
Omasa K and Takayama K. (2003) Simultaneous measurements of stomatal conductance, non-photochemical yield of photochemical II in intact leaves by thermal and chlorophyll fluorescence imaging. Plant Cell Physiology 44 1290–1300.
Osmond CB, Kramer D, Lüttge U. (1999) Reversible, water stress-induced non-uniform chlorophyll fluorescence quenching in wilting leaves of Potentilla reptans may not be due to patchy stomatal response. Plant Biology 1 618–624.
Oxborough K. (2004a) Imaging of chlorophyll a fluorescence: theoretical and practical aspects of an emerging technique for monitoring of photosynthetic performance. Journal of Experimental Botany 84 748–752.
Oxborough K. (2004b) Using chlorophyll a fluorescence imaging to monitor photosynthetic performances. In Papageorgiou GC and Govindjee GC (Eds.). Chlorophyll a fluorescence: a signature of photosynthesisDordrecht, The Netherlands Springer pp. 409–428.
Oxborough K and Baker NR. (1997a) An instrument capable of imaging chlorophyll a fluorescence from intact leaves at very low irradiance and at cellular and subcellular levels of organization. Plant, Cell and Environment 20 1473–1483.[CrossRef]
Oxborough K and Baker NR. (1997b) Resolving chlorophyll a fluorescence images of photosynthetic efficiency into photochemical and non-photochemical components—calculation of qP and Fv'/Fm' without measuring Fo'. Photosynthesis Research 54 135–142.[CrossRef]
Quilliam RS, Swarbrick PJ, Scholes JD, Rolfe SA. (2005) Imaging photosynthesis in wounded leaves of Arabidopsis thaliana. Journal of Experimental Botany 57 55–69.
Rasband WS. (2006) ImageJ, U.S. National Institutes of Health, Bethesda, Maryland, USA. http://rsb.info.nih.gov/ij/, 1997–2006.
Rolfe SA and Scholes JD. (2002) Extended depth-of-focus imaging of chlorophyll fluorescence from intact leaves. Photosynthesis Research 72 107–115.[CrossRef][Web of Science][Medline]
Scharte J, Schon H, Weis E. (2005) Photosynthesis and carbohydrate metabolism in tobacco leaves during an incompatible interaction with Phytophthora nicotianae. Plant, Cell and Environment 28 1421–1435.[CrossRef]
Shah J. (2003) The salicylic acid loop in plant defense. Current Opinion in Plant Biology 6 365–371.[CrossRef][Web of Science][Medline]
Sharpe PJH and Barber HN. (1972) Near infrared reflectance of colored fruits. Applied Optics 11 2902–2906.
Siebke K and Weis E. (1995) Assimilation images of leaves of Glechoma hederacea: analysis of non-synchronous stomata related oscillations. Planta 196 155–165.[Web of Science]
Smillie RM. (1992) Calvin cycle activity in fruit and the effect of heat stress. Scientia Horticulturae 51 83–95.[CrossRef]
Smillie RM, Hetherington SE, Nott R, Chaplin GR, Wade NL. (1987) Application of chlorophyll fluorescence to the postharvest physiology and storage of mango and banana fruit and the chilling tolerance of mango cultivars. Asian Food Journal 3 55–59.
Soukupová J, Smatanová S, Nedbal L, Jegorov A. (2003) Plant response to destruxins visualized by imaging of chlorophyll fluorescence. Physiologia Plantarum 118 399–405.[CrossRef]
Vácha F, Vácha M, Bumba L, Hashizume K, Tani T. (2000) Inner structure of intact chloroplasts observed by a low temperature laser scanning microscope. Photosynthetica 38 493–496.[CrossRef]
Valcke R. (2007) Chlorophyll fluorescence imaging for predicting shelf-life and quality of fruits. Journal of Experimental Botany (this issue).
Vogelmann TC. (1993) Plant tissue optics. Annual Review of Plant Physiology and Plant Molecular Biology 44 231–251.[CrossRef][Web of Science]
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