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CHAPITRE 6 Near-field fluorescence measurements for nutrient deficiencies detection: Effects of the angle of view

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BÉLANGER, M.-C 1., VIAU, A.A. 1, SAMSON, G. 2, CHAMBERLAND, M. 3, soumis à International Journal of Remote Sensing, Near-field fluorescence measurements for nutrient deficienies detection : Effects of the angle of view. 1 Laboratoire de Géomatique Agricole et d’Agriculture de precision, Local 3731-A, Pavillon Casault, Université Laval, Qc, G1K 7P4. 2 Université du Québec à Trois-Rivières, Case postale 500, Trois-Rivières, Qc, G9A 5H7. 3 Telops, 100-2600, avenue St-Jean-Baptiste, Québec, Québec, G2E 6J5. Ce chapitre est le fruit du travail de la candidate. À titre de directeurs et superviseur, messieurs Viau, Samson et Chamberland ont apporté des suggestions et commentaires quant au design et à la préparation de ce chapitre.

Dans le cadre de ce travail nous avons évalué l’impact d’un changement d’angle de mesure sur le potentiel de détection des carences nutritives par fluorescence active. Des plants de pomme de terre soumis à trois carences nutritives (K, Mg, N) ont été cultivés en serre. Des mesures de fluorescence ont été prises à trois angles différents en inclinant la plante devant le capteur. Des images RGB de chacune des scènes ont été prises et le ratio nervures+tiges/limbe (V/L) a été déterminé. Il en ressort que la carence en azote est plus facilement détectée lorsque le fluorimètre cible les feuilles plus âgées (plant incliné à 45°). De plus, il semble que le ratio V/L ait un impact sur le potentiel de détection. Selon nos résultats, un V/L élevé semble favoriser la détection des carences en potassium et en magnésium alors qu’un V/L faible favorise la détection de carences en azote.

In this study we evaluated the impact of three measurement angles on the potential of nutrient deficiency detection using fluorescence. Potato plants were grown in a greenhouse and three nutrient deficiencies were induced (K, Mg and N). Fluorescence measurements were taken at three different angles by inclining the plant in front of the fluorometer. RGB pictures of each plant scene were taken and a veins/lamina ratio (V/L) was computed. Nitrogen deficiency was more easily detected when the fluorometer was aimed at older leaves (angle of 45°). Moreover, the veins/lamina ratio has an impact on the detection potential of fluorescence. Following our results, a high V/L improves the detection of K- and Mg-deficiency whereas a low V/L improves the detection of N-deficiency.

Nutrient deficiencies induce numerous changes in a plant, such as decreases in leaf growth and photosynthesis rate or increase of nutrient translocation from old leaves to younger ones resulting in significant yield reduction. Thus, the detection of nutrient deficiencies is essential and it is usually achieved by time-consuming chemical analyses of plant tissues. Rapid and reliable means of nutrient deficiency detection is provided by remote sensing through measurements of passive reflectance (Adams et al., 1993; Gamon et al., 1997; Haboudane et al., 2002; Vouillot et al., 1998; Zhao et al., 2003) or active fluorescence (Apostol et al., 2003; Chappelle et al., 1984; Corp et al., 2003; McMurthey III et al., 1994; Mercure et al., 2004; Tartachnyk & Rademacher, 2003). However, when using remote sensing to monitor vegetation through passive reflectance measurements, care must be taken regarding sensor view angle and the solar angle. Most vegetation indices are influenced by the solar or sensor view angle (Blackburn & Pitman, 1999; Walter-Shea et al., 1989). By taking multiple angle views, it has been possible to understand bidirectional reflectance and to correct satellite images from this phenomenon. To diminish the effects of these factors and if only a few measurements can be acquired, authors recommend taking measurements around a near-nadir angle of view (Duggin et al., 1982).

Contrary to reflectance, plant fluorescence is an active process and occurs upon excitation by UV radiation. Two types of fluorescence are emitted: blue-green fluorescence (BGF) and chlorophyll fluorescence (ChlF). Blue-green fluorescence (BGF) is emitted at 440 and 520 nm mainly by ferulic acid and other plant secondary metabolites. Because ferulic acid is present in cell walls of leaf veins and the epidermis, BGF mainly comes from leaf veins, petioles and stems (Cerovic et al., 1999). Chlorophyll fluorescence (ChlF), on the other hand, comes mainly from leaf lamina and is emitted at 690 and 740 nm (Lichtenthaler, 1990). UV-induced plant fluorescence has been tested for numerous applications and its potential for the detection of water stress (Apostol et al., 2003; Ferguson & Burke, 1991; Flexas et al., 2000; Lang et al., 1996; Norikane & Kurata, 2001), nutrient deficiency (Bélanger et al., in press; Chappelle et al., 1984; Corp et al., 2003; Heisel et al., 1996; Langsdorf et al., 2000; McMurthey III et al., 1994; Tartachnyk & Rademacher, 2003), species differentiation (Chappelle et al., 1984b; Codrea et al., 2003), photosynthetic rate variation (Freedman et al., 2002; Rosema et al., 1992), chlorophyll content (Gitelson et al., 1999), phenolic metabolites content (Mercure et al., 2004), leaf ageing (Meyer et al., 2003), ozone stress (Rosema et al., 1992), and temperature stress (Ferguson & Burke, 1991; Lang et al., 1996) has been assessed. Until now, fluorescence measurements were mainly done over leaf pieces, in laboratories or over whole leaves using imaging fluorescence, for instance (Heisel et al., 1996; Lang et al., 1996; Lichtenthaler et al., 1996). Spectral measurements of fluorescence can also be taken on freestanding plants and instruments have been developed to achieve near-field or tractor-mounted fluorescence measurements (Belzile et al., 2003; Cecchi et al., 1994; Corp et al., 2003; Norikane & Kurata, 1999; Sowinska et al., 1999). Those systems use laser, fluorescent and halogen lamps, or light emitting diodes (LED) as excitation sources. Measurements are made at different distances, between 0.2 m to 30 m and at different but constant sensor view angles (0, 10, 45 or 90°).

Plant fluorescence is an active process: it has its own excitation source and contrary to reflectance, the angle between the excitation source and the fluorometer is constant as is the area sensed. Moreover, by analyzing fluorescence data using ratios, the error attributed to a specific angle of view is constant for all subjects. We can thus estimate that the bidirectional effects will be negligible for fluorescence even if measurements are taken at different angles. Fluorescence measurements taken at different sensor inclinations will be more affected by leaf age or the proportion of veins and lamina exposed than by bidirectional effects. Indeed, by taking measurements directly over the plant top, fluorescence emanates mainly from young leaves. As fluorescence varies with leaf age, especially for nitrogen deficient plants where the youngest leaves have not yet been affected by the nitrogen shortage (Adams et al., 1993; Heisel et al., 1996), it is important to consider the angle between the fluorometer and the target while developing a system using fluorescence for the detection of nutrient stress. The ratio BGF/ChlF increases when plants are under stress (Schweiger et al., 1996) and as leaf veins and stems mainly emit BGF, and because the proportion of leaf area occupied by leaf veins increases under nitrogen deficiency (Lambers et al., 1998), the detection of a stress might be enhanced if more leaf veins and less lamina are exposed to the excitation source, especially over nitrogen deficiency.

The aims of this work are to evaluate 1) if different angles have the same potential for the detection of nutrient deficiencies in potato plants and 2) if the proportion of exposed leaf veins has an impact on the efficiency of fluorescence to detect nutrient stress.

Plant material was grown in a greenhouse on Laval University Campus (Quebec, Canada) in 2003. The experimental plan was set as a nested effects design including five blocks, three treatments (N, K, Mg) applied at three levels (15, 30, 60) and a control. A total of 50 potato plants, cv. Superior, were grown in 3L containers, at 22°C day, 16°C night controlled temperature and under a 16-hour photoperiod. The growing medium was a mix of 80 % vermiculite and 20 % quartz sand, washed with demineralized water prior to planting (Tukaki & Mahler, 1990). From emergence to the end of the experiment, control plants were fertilized using a complete mineral solution (Tukaki & Mahler, 1990) (N 160 ppm; P 29 ppm; K 234 ppm; Ca 160 ppm; Mg 48 ppm; S 62 ppm; Fe 1.83 ppm; Mn 0.5 ppm; B 0.5 ppm; Zn 0.05 ppm; Cu 0.02 ppm; Mn 0.01 ppm). To induce mineral deficiencies, other plants received modified mineral solutions providing 15, 30 or 60 % of N, K or Mg compared to the control treatment.

All subsequent data were collected twice during the experiment: at 37 and 44 days after emergence (DAE) except for destructive measurements such as shoot biomass, tuber fresh weight and numbers that were collected only once, at the end of the experiment (44 DAE).

Chlorophyll contents were estimated on the fourth fully expanded leaf with a Minolta SPAD-502 Chlorophyll Meter (Spectrum Technologies Inc., IL, USA). Growth was determined by the surface of the fourth fully expanded leaf using a leaf area meter (Li-3000, Li-Cor Inc, NE, USA) and by dry shoot biomass (dried during 48h at 65°C). Tuber fresh weight and numbers were also measured at the end of the experiment. Developmental stage was evaluated using the two-digits method of Radtke & Rieckmann (1991).

The FLUTE measurements were taken on a freestanding potato plant, under ambient light. FLUTE uses a Xenon flash lamp as excitation source which is located 0.5 m from the whole plant according to the instrument’s specifications. UV or blue excitation wavelength was chosen using respectively the following filters: 360±40 nm, 436± 20 nm (Chroma Technology Corp., VT, USA). Located at 0.5 m from the plant, the detection was made by a photodiode (Advanced Photonix inc., CA, USA) at 440, 520, 690 and 740 nm ± 10 nm using 5.08 cm diameter filters (CVI Laser, NM, USA). UV-induced fluorescence was measured at the four emission bands whereas blue-induced fluorescence intensities were measured at 690 and 740 nm. The excitation and detection filters were manually changed between each measurement. The Xe-flash lamp and the detector are driven by a trigger impulse (figure 1). A complete fluorescence measurement requires two actions occurring respectively at time t=1 and t=2. At time t=1, the Xe-flash lamp is turned on and the detector takes a reading that includes both reflectance and fluorescence measurement. At time t=2, the Xe-flash lamp is turned off and the detector takes a second reading including only reflectance. The fluorescence intensity is obtained by subtracting the measurement taken at time t=2 from the measurement taken at time t=1 and is given in Volts on a digital screen. Fluorescence measurements were taken twice, at 37 days after emergence (DAE) and at 44 DAE corresponding to a high growth rate period. Fluorescence data were calibrated against photodiode sensitivity and transmittance of the filters at the different wavelengths. The tab.1 presents the most commonly used fluorescence ratios in which our fluorescence data were computed.

Multi-angles data acquisition was done using a tripod and a pot holder. The potato plant was inclined at three different angles, 90°, 45° and 0°, as presented in figure 2. A digital picture of each plant scene (3 angles x 50 plants) was taken with a 3 Mega pixels HP 315 PhotoSmart digital camera (Hewlett-Packard, Palo Alto, CA). The digital camera was placed in front of the fluorescence detection system and a picture was taken using the 2.5 X digital zoom so that the scene taken was very similar to the one seen by the fluorometer. To calibrate both scenes, we took a 2.5 X picture of the excitation beam (delimited by placing in a sample holder, a white paper sheet emitting blue fluorescence). The pixels that were not included in the excited zone were extracted from the JPEG files using Matlab (The Mathworks V.6.5, 2000) resulting in images such as the ones presented in figure 2. The output files were formatted as TIFF, stored in a database and sent to the ArcInfo module ArcEdit and Grid (ESRI V.8.3, 2002) for processing. An Arc Macro Language (AML) digitizing program was developed to identify and draw three polygon types: a) Background, b) Lamina, c) Central Veins and Stems and to control that each polygon was closed and that the total picture area (411 975 pixels or 720 mm2) was constant.

The final output file was in ASCII containing the three polygon areas in pixels. The V/L ratio was computed using the equation [1].

Lamina

[1]

where Veins+ Stems = Veins + Stems polygons area (in pixels)

Lamina = Lamina polygons area (in pixels)

The effects of N, K, and Mg deficiencies on growth and development parameters are presented in Tab. 2. There were no significant impacts of K deficiency on growth parameters. Nitrogen is the driving element for plant development and, under our experimental conditions a nitrogen shortage induces significant changes in the plant. At 37 DAE the fourth leaf area of N-deficient plants was significantly decreased compared to the controls. At 44 DAE, N-deficiency induced no significant effect on the fourth leaf area, probably due to the reduced rate of growth for the vegetative parts when the plant enters the blooming stage. At 44 DAE there was also a decrease of 14.3 and 40.9 % in chlorophyll content and dry shoot biomass, respectively. N deficiency also caused a 50% decrease in the number of tubers although no significant effect was detected on the fresh weight of tubers. In addition to growth inhibition, developmental stages, estimated according to Radtke & Rieckmann, (1991), were retarded in N-deprived plants and in Mg-deficient plants, at 37 DAE. Thus, growth was slowed down by the nitrogen shortage whereas no significant impacts were detected on K- and Mg-deficient plants.

Tab. 2 Percentage of variation between the control and the induced mineral deficiency (K, Mg or N) on growth and development variables, at two different dates.

The effects of K, Mg and N deficiencies on fluorescence parameters are presented in Tab. 3. They were significantly affected by the angle between the fluorometer and the target. As the potato crop is a Na excluder, the K-deficiency will first appear on young fully expanded leaves (Ulrich, 1993). By taking measurements over the plant top, as is done using the 0° angle, the fluorometer targets younger leaves and should result in a better detection of K deficient plants. On the 37th DAE, the K deficiency induced a near-significant increase of F440/F690 and F440/F740 (respectively, p=0.0804 and p=0.0582) when measured at an angle of 0°. At 44 DAE, F690/F740 measured for K-deficient plant at an angle of 90° was significantly different from control plant. It is not clear why there is a significant difference of F690/F740 at 90°. The F690 fluorescence can be re-absorbed by the chlorophyll molecule and thus be reduced if the chlorophyll content is higher (Langsdorf et al., 2000). Therefore there might be less chlorophyll in older leaves, resulting in an increase of the F690 and F690/F740 measured.

There was no significant difference detected between Mg-deficient plants and the controls.

The detection of N-deficient plants occurred only when the fluorometer aimed to the plant’s mid-height. At both measurement dates, the two epidermal transmittances were significantly affected by the N-deficiency when measured at an angle of 45°. As mentioned above, when plants are affected by a nitrogen deficiency, the youngest leaves will be affected after oldest leaves (Heisel et al., 1996). By taking fluorescence measurements at an angle of 45° the fluorometer targets older leaves that are already affected by the nitrogen shortage.

According to Tab. 3, the standard method for the acquisition of remote sensing data, i.e., using a near-nadir sensor angle, may not be the most relevant option for the detection of specific nutrient deficiencies, especially nitrogen. A near-field fluorometer could be designed to be inclinable, in order to measure fluorescence over the most relevant leaves for specific nutrient deficiency detection.

The ‎ Tab. 4 presents the descriptive statistics of the proportion of vegetation present on the digital pictures, at the three measurement angles. At 0 and 45 almost 90 % of all pixels corresponds to vegetation (thus 10% corresponds to the background) whereas at 90°, this proportion is lower than 75 %. Moreover, the standard deviation of the measurements taken at 90° shows a wider data dispersion compared to measurements taken at 0° or 45°.

F figure 3 graphically presents the descriptive statistics of the V/L ratio. At 37 DAE, there is no significant difference between the V/L ratio measured at an angle of 0° and the one measured at 45°. The ratio measured at 90°, is significantly different from the ones measured at the two other angles, for both measurement dates. It shows that the fluorometer targetted at more leaf veins, stem or petiole at 90° than at 0 or 45°.

In Tab. 3, the significant differences between the control plants and the N-deficient plants appeared only at 45°. At similar V/L ratios (0°,45°), there was a different response of the fluorescence ratios to the induced deficiencies. For significantly different V/L ratios, there was similar response for N-deficient plants (0°, 90°) whereas different responses were observed between measurement taken at 45° and 90°.

According to these results, it seems that the potential of fluorescence for the detection of nutrient stresses was more influenced by leaf age than by the proportion of veins exposed. To confirm this hypothesis, we estimated the effect of the V/L ratio for all fluorescence parameters having a linear relation with V/L. Tab. 5 presents the correlation coefficients computed between the fluorescence indices and the V/L ratio. At 37 DAE, three parameters (F690/F740, UV690 and UV740) have a significant linear relation with V/L. At 44 DAE a significant, but weaker, relation appears for F440/F520, UV690 and UV740.

An ANCOVA using the V/L ratio as a covariate was realized only for the fluorescence indices having a significant relation with the V/L ratio (F690/F740, UV690 and UV740 taken at 37 DAE). The slopes of the relations were not equal for all treatments thus the computation of a standard ANCOVA was compromised. A modified ANCOVA (UCLA, 2004) was computed at four different V/L ratios corresponding to quartile 1, 2, 3, and V/L mean for angle 90°, to evaluate if the V/L ratio has an impact on the detection efficiency of the fluorescence parameters. Tab. 6 presents the results of the modified ANCOVA. One can see that the changes induced by mineral deficiencies were affected by the V/L ratios. The significant differences for K- and Mg-deficiency were found at higher V/L and at lower V/L for N-deficiency: meaning that the proportion of veins and lamina exposed has an impact on the detection effectiveness of the fluorescence parameters. For the detection of K- and Mg-deficiency detection, targeting a leaf area having more leaf veins and stem seems to improve the detection. For N-deficiency, the targeted leaf area should include at least six times more lamina than veins and stems.

This study evaluated if different angles of view would affect the detection potential of fluorescence over three nutrient deficiencies. According to our results, the inclination of the sensor is important and tends to increase the detection potential especially over specific nutrient deficiencies affecting primarily old leaves.

The inclination of the sensor will enhance the excitation of older leaves, of stems and leaf veins and reduce the area of lamina exposed. A ratio of the proportions of veins and lamina exposed was computed in order to evaluate its effects on the detection potential of fluorescence. The proportion of veins and lamina exposed affected the detection potential of three fluorescence ratios: F690/F740, UV690 and UV740 whereas no linear relations were detected between the V/L ratio and other fluorescence indices. Using a modified ANCOVA, it was possible to see that the changes induced by mineral deficiencies were affected by the V/L ratios: at 37 DAE, significant differences were found at higher V/L values for K- (UV740) and Mg-deficiency (F690/F740) and at lower V/L for N-deficiency (UV690, UV740).

Near-field fluorescence shows potential for the detection of nitrogen deficiency especially. To increase the chances of detection, the fluorometer could be inclined in order to target older leaves and a low V/L ratio instead of using the standard near-nadir field of view.

Our results suggest that further investigation be made using imaging fluorescence. That way, using fluorescence images from same age leaves, fluorescence indices could be computed from areas of plants’ leaves having specific V/L ratios and our results could be confirmed. A detection efficiency chart could be established for a whole potato plant using imaging fluorescence and 3D-mapping. Using this chart, it would then be possible to identify the best leaf to be targeted for nitrogen deficiency detection, for instance.

The authors thank the Fonds québécois de la recherche sur la nature et les technologies (FQRNT) through its industrial scholarship program, the Canadian Foundation for Innovation (CFI), and the Natural Sciences and Engineering Research Council of Canada (NSERC) for their financial support. Cultures Dolbec, inc. for supplying the potatoes; Ludovic Béland, Marie-Amélie Bélanger and Serge-Olivier Kotchi for their valuable help in the greenhouse; Gilles Lavoie for his collaboration with ArcInfo, Charles Belzile, Simon Roy, Nelson Landry and Stéfan Parmentier for their technical advice and the laboratoire de métrologie for the pot holder design.

Adams, M., W. Norvell, J. Peverly, and W. Philpot. 1993. Fluorescence and Reflectance Characteristics of Manganese Deficient Soybean Leaves - Effects of Leaf Age and Choice of Leaflet. Plant Soil 156:235-238.

Apostol, S., A.A. Viau, N. Tremblay, J.-M. Briantais, S. Prasher, L.-E. Parent, and I. Moya. 2003. Laser induced fluorescence signatures as a tool for remote monitoring of water and nitrogen stresses in plants. Canadian journal of remote sensing 29:57-65.

Bélanger, M.-C., A.A. Viau, G. Samson, and M. Chamberland. in press. Near-Field Reflectance and Fluorescence Measurements for Nutrient Deficiency Detections in Potato Crop (Solanum tuberosum, L.), In ASA-CSSA-SSSA, (ed.) 7th International conference on Precision Agriculture and other resources management. ASA-CSSA-SSSA, Minneapolis, Mn, July 24-28, 2004.

Belzile, C., M.-C. Bélanger, A.A. Viau, M. Chamberland, and S. Roy. 2003. An operational system for crop assessment, p. 244-252, In B. S. Bennedsen, et al., eds. Proceedings of Photonic East conference: Monitoring Food Safety, Agriculture, and Plant Health, Vol. 5271. SPIE, Providence, RI.

Bilger, W., M. Veit, L. Schreiber, and U. Schreiber. 1997. Measurement of leaf epidermal transmittance of UV radiation by chlorophyll fluorescence. Physiologia plantarum 101:754-763.

Blackburn, G.A., and J.I. Pitman. 1999. Biophysical controls on the directional spectral reflectance properties of bracken (Pteridium aquilinum) canopies: results of a field experiment. International Journal of Remote Sensing 20:2265-2282.

Cecchi, G., P. Mazzinghi, L. Pantani, R. Valentini, D. Tirelli, and P. De Angelis. 1994. Remote sensing of chlorophyll a fluorescence of vegetation canopies: 1. Near and far field measurements techniques. Remote Sensing of Environnement 47:18-28.

Cerovic, Z.G., G. Samson, F. Morales, N. Tremblay, and I. Moya. 1999. Ultraviolet-induced fluorescence for plant monitoring: present state and prospects. Agronomie 19:543-578.

Chappelle, E.W., J.E. McMurtrey III, F.M.J. Wood, and W.W. Newcomb. 1984. Laser-induced fluorescence of green plants. 2: LIF caused by nutrient deficiencies in corn. Applied Optics 23:139-142.

Chappelle, E.W., F.M. Wood, J.E. McMurtrey III, and W.W. Newcomb. 1984b. Laser-induced fluorescence of green plants. 1: A technique for the remote detection of plant stress and species differentiation. Applied Optics 23:134-138.

Codrea, C., T. Aittokallio, M. Keranen, E. Tyystjarvi, and O. Nevalainen. 2003. Feature learning with a genetic algorithm for fluorescence fingerprinting of plant species. Pattern recognition letters 24:2663 - 2673.

Corp, L.A., J.E. McMurthey III, E.M. Middleton, C.L. Mulchi, E.W. Chappelle, and C.S.T. Daughtry. 2003. Fluorescence sensing systems: In-vivo detection of biophysical variations in field corn due to nitrogen supply. Remote Sensing of Environment 86:470-479.

Duggin, J.J., D. Piwinski, V. Whitehead, and G. Ryland. 1982. Evaluation of NOAA-AVHRR data for crop assessment. Applied Optics 21:1873-1875.

Ferguson, D.L., and J.J. Burke. 1991. Influence of water and temperature stress on the temperature dependence of the reappearance of variable fluorescence following illumination. plant physiology 97:188-192.

Flexas, J., J.-M. Briantais, Z.G. Cerovic, H. Medrano, and I. Moya. 2000. Steady-State and Maximum Chlorophyll Fluorescence Responses to Water Stress in Grapevine Leaves: A New Remote Sensing System. Remote Sensing of Environment 73:283-297.

Freedman, A., J. Cavender-Bares, P.L. Kebabian, R. Bahaskar, H. Scott, and F.A. Bazzaa. 2002. Remote-sensing of solar-excited plant fluorescence as a measure of photosynthetic rate. Photosynthetica 40:127-132.

Gamon, J.A., L. Serrano, and J.S. Surfus. 1997. The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia 112:492-501.

Gitelson, A., C. Buschmann, and H. Lichtenthaler. 1999. The chlorophyll fluorescence ratio F-735/F-700 as an accurate measure of the chlorophyll content in plants. Remote Sensing of Environment 69:296 - 302.

Haboudane, D., J.R. Miller, N. Tremblay, P.J. Zarco-Tejada, and L. Dextrase. 2002. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment 81:416-426.

Heisel, F., M. Sowinska, J.A. Miehé, M. Lang, and H.K. Lichtenthaler. 1996. Detection of nutrient deficiencies of maize by laser induced fluorescence imaging. Journal of Plant Physiology 148:622-631.

Lambers, H., F.S. Chapin III, and T.L. Pons. 1998. Plant physiological ecology. 1st ed. Springer-Verlag, New York.

Lang, M., H.K. Lichtenthaler, M. Sowinske, F. Heisel, and J.A. Miehé. 1996. Fluorescence imaging of water and temperature stress in plant leaves. Journal of Plant Physiology 148:613-621.

Langsdorf, G., C. Buschmann, M. Sowinska, F. Babani, M. Mokry, F. Timmermann, and H.K. Lichtenthaler. 2000. Multicolour fluorescence imaging of sugar beet leaves with different nitrogen status by flash lamp UV-excitation. Photosynthetica 38:539-551.

Lichtenthaler, H.K. 1990. Applications of chlorophyll fluorescence in stress physiology and remote sensing, p. 287-305, In S. M.D. and C. J.A., eds. Applications of remote sensing in agriculture. Butterworhs Publisher.

Lichtenthaler, H.K., M. Lang, M. Sowinska, F. Heisel, and J.A. Miehé. 1996. Detection of vegetation stress via a new high resolution fluorescence imaging system. Journal of Plant Physiology 148:599-612.

McMurthey III, J.E., E.W. Chappelle, M.S. Kim, J.J. Meisinger, and L.A. Corp. 1994. Distinguishing Nitrogen fertilization levels in field corn (Zea mays L.) with actively induced fluorescence and passive reflectance measurements. Remote Sensing of Environment 47:36-44.

Mercure, S.-A., B. Daoust, and G. Samson. 2004. Causal relationship between growth inhibition, accumulation of phenolic metabolites, and changes of UV-induced fluorescence in nitrogen-deficient barley plants. Canadian journal of botany 82:815-821.

Méthy, M., A. Olioso, and L. Trabaud. 1994. Chlorophyll fluorescence as a tool for management of plant ressources. Remote Sensing of Environment 47:2-9.

Meyer, S., A. Cartelat, I. Moya, and Z.G. Cerovic. 2003. UV-induced blue-green and far-red fluorescence along wheat leaves: a potential signature of leaf ageing. Journal of experimental botany 54:757-769.

Norikane, J.H., and K. Kurata. 1999. Fluorescence monitoring using the relative referencing method on a free-standing tomato plant, In ASAE, ed. 1999 ASAE Annual international meeting, Vol. paper no; 993206. ASAE, Toronto, Ontario, Canada.

Norikane, J.H., and K. Kurata. 2001. Water stress detection by monitoring fluorescence of plants under ambient light. Transactions of the ASAE 44:1915-1922.

Radtke, W., and W. Rieckmann. 1991. Maladies et ravageurs de la pomme de terre. 1st ed., Gelsenkirchen-Buer.

Rosema, A., G. Cecchi, L. Pantani, B. Radicatti, M. Romuli, P. Mazzinghi, O. van Kooten, and C. Kliffen. 1992. Monitoring photosynthetic activity and ozone stress by laser-induced fluorescence in trees. International Journal of Remote Sensing 13:737-751.

Schweiger, J., M. Lang, and H.K. Lichtenthaler. 1996. Differences in fluorescence excitation spectra of leaves between stressed and non-stressed plants. Journal of Plant Physiology 148:536-547.

Sowinska, M., B. Cunin, A. Deruyver, F. Heisel, J.A. Miehé, G. Langsdorf, and H.K. Lichtenthaler. 1999. Near-field measurements of vegetation by laser-induced fluorescence imaging, In SPIE, ed. EUROPTO conference on remote sensing for earth science applications, Vol. 3868. SPIE, Florence, Italy.

Subbash, N., and C.N. Mohanan. 1994. Laser-induced red chlorophyll fluorescence signatures as nutrient stress indicator in rice plants. Remote Sensing of Environment 47:45-50.

Tartachnyk, I., and I. Rademacher. 2003. Estimation of Nitrogen Deficiency of Sugar beet and Wheat using Parameters of Laser Induced and Pulse Amplitude Modulated Chlorophyll Fluorescence. Journal of applied botany 77:61-67.

Tukaki, J.L., and R.L. Mahler. 1990. Evaluation of nutrient solution phosphorus concentration on potato plantlet tuber production under greenhouse conditions. Journal of plant nutrition 13:149-168.

UCLA. 2004. How do I handle interactions of continuous and categoriacl variables? [Online]. Available by UCLA Academic Technology Services http://www.ats.ucla.edu/stat/sas/library/hetreg.htm (verified 2004-11-30).

Ulrich, A. 1993. Potato, p. 149-156, In B. W. F, ed. Nutrient deficiencies and toxicities in crop plants. APS Press, St-Paul, Minnesota.

Vouillot, M., P. Huet, and P. Boissard. 1998. Early detection of N deficiency in a wheat crop using physiological and radiometric methods. AGRONOMIE 18:117 - 130.

Walter-Shea, E.A., J.M. Norman, and B.L. Blad. 1989. Leaf bidirectional reflectance and transmittance in corn and soybean. Remote Sensing of Environment 29:161-174.

Zhao, D., K.R. Reddy, V.G. Kakani, J.J. Read, and G.A. Carter. 2003. Corn (Zea mays L.) growth, leaf pigment concentration, photosynthesis and leaf hyperspectral reflectance properties as affected by nitrogen supply. Plant and Soil 257:205-217.

© Marie-Christine Bélanger, 2005