Predicting the efficiency of using the RGB (Red, Green and Blue) reflectance for estimating leaf chlorophyll content of Durum wheat (Triticum durum Desf.) genotypes under semi arid conditions.
Pigments are integrally related to the physiological function of leaves. Chlorophylls absorb light energy and transfer it into the photosynthetic apparatus. Carotenoids (yellow pigments) can also contribute energy to the photosynthetic system. However, when incident light energy exceeds that needed for photosynthesis, the carotenoids that compose the xanthophyll cycle dissipate excess energy, thus avoiding damage to the photosynthetic system (Demmig-Adams and Adams, 1996). Anthocyanins (pink, purple, and red pigments) may also protect leaves from excess light or from UV light (Woodall and Stewart, 1998). Because of the importance of pigments for leaf function, variations in pigment content may provide information concerning the physiological state of leaves. Green plants all have unique spectral features, mainly because of the chlorophyll and carotenoid and other pigments and water content can together constitute the spectral feature of a plant (Philip and Shirly, 1978). Traditionally, the predominant method for measuring chlorophyll content is using the spectrophotometer; with this method plant tissues are used, which costs more time and sustain some damage to the plants. The chlorophylls have strong absorbance peaks in the red and blue regions of the spectrum (Figure 1). Since the blue peak overlaps with the absorbance of the carotenoids, it is not generally used for estimation of chlorophyll content. Maximal absorbance in the red region occurs between 660 and 680 nm. However, reflectance at these wavelengths has not proved as useful for prediction of chlorophyll content as has reflectance at slightly longer or shorter wavelengths. This is because relatively low chlorophyll contents are sufficient to saturate absorption in the 660-680 nm region, thus reducing the sensitivity to high chlorophyll contents of spectral indices based on these wavelengths. Consequently, empirical models for prediction of chlorophyll content from reflectance are largely based on reflectance in the 550 or 700 nm regions where higher chlorophyll contents are required to saturate the absorptance (Buschman and Nagel, 1993). Since anthocyanin also absorbs around 550 nm (Figure 1), we chose to work only with chlorophyll indices based on absorptance around 700 nm. As a leaf senesces, there is an increase in the reflectance of visible radiation presumably due to the degradation of chlorophyll (Knipling, 1967). Changes in leaf reflectance of green leaves with maturation and senescence have been attributed to changes in chlorophyll and mesophyll arrangement (Grant, 1987). Recently, digital imagery has become a new trend in plant color analysis. Digital cameras or scanners in combination with computers and appropriate software can be used to photograph, scan, and evaluate leaves for color with relative ease and at an affordable cost. In agriculture, digital technology has been used to characterize color in apples (Schrevens and Raeymaeckers, 1992), distinguish weeds from crops (Perez, et al, 2000), identify storageassociated color change in chickory (Zhang, et al., 2003) and apple (Vervaeke, et al., 1994), and evaluate senescence rates in spring wheat (Adamsen, et al., 1999) and durum wheat (Hafsi, e al., 2000 and Guendouz and Maamari, 2011). The objective of this study is to evaluate the efficiency of using numerical image analysis (NIA) for estimate the reflectance at Red, Green and Blue (RGB) and evaluate the efficiency of using the reflectance at RGB to estimate the chlorophyll content in durum wheat cultivars under semi arid conditions.
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Materials and methods
Field experiment was done during the 2010-2011 growing season at the experimental field of ITGC, Setif, Algeria (5[degrees]20'E, 36[degrees]8'N, 958m above mean sea level). A set of 10 genotypes of durum wheat (Triticum durum Desf.) (Table 1) were planted on November 30, 2010 on a clay-silt. The seeds were sown using an experimental drill in 1.2 m x 2.5 m plots consisting of 6 rows with a 20 cm row space and the seeding rates for experiments were about 300 seeds per [m.sup.2]. The plots were fertilized with SULFAZOT (26 % N, 12 % S, 120 Kg [ha.sup.-1]) was applied at tillage. Weeds were removed chemically by TOPIC (0.75 L [ha.sup.-1]) and GRANSTAR (15 g [ha.sup.-1]). In this study, we use the numerical image analysis (NIA) for estimate the reflectance at Red, Green and Blue (RGB). Leaves were photographed on black surface, between 11:00 and 12:00 solar time with a color digital camera (Canon, Power Shot A460, AiAF, CHINA). Images were stored in a JPEG (Joint Photographic Expert Group) prior to downloading onto a PC computer and analyzed using Mesurim Pro (Version 3.3) software (Figure 2). In addition, senescence was expressed as the ratio of senesced area to total leaf area (in per cent). Average senescence (Sa %) was calculated as the mean of the S1 to S6 values using Mesurim Pro software. The SPAD502 measures the content of chlorophyll (CC) in the leaf, which is related to leaf greenness, by transmitting light from light emitting diodes (LED) through a leaf at wavelengths of 650 and 940 nm.
Results and discussion
As shown in Table 2, analysis of variance revealed that the reflectance at different wavelengths Red, Green and Blue (RGB) and Average senescence (Sa%) were highly significant (P < 0.001) affected by different genotypes. The reflectance at Red is ranged between 46.92% for Oued Zenati to 10.81% for Waha, at Green wavelength the reflectance is ranged between 46.47% for Oued Zenati to 12.87% for Waha. In addition, the reflectance at Blue is ranged from 42.39% for Oued Zenati to 11.05% for Kucuk. At different wavelengths Red, Green and Blue, Oued Zenati register the high values of reflectance (46.92%, 46.47% and 42.39% respectively). The lowest reflectance was observed in the Blue range of the spectrum from 400 to 500 nm (24.99%). This result is confirmed with the study of Merzlyak and Gitelson (1995). Average senescence was ranged from 63.44% for Waha to 44.51% for Oued Zenati. Average senescence correlated positively and significantly with the leaf reflectance at Red and Blue (r = 0.84**, r = 0.67* respectively). Chlorophyll tends to decline more rapidly than carotenoids when plants are under stress or during leaf senescence (Gitelson and Merzlyak, 1994). Variations in leaf chlorophyll content detectable by spectral reflectance have also been shown to be related to leaf development and senescence (Carter and Knapp, 2001). The genotypic effect was shown highly significant for the chlorophyll content (P < 0.001). The chlorophyll content is ranged from 60.7 for Sooty to 50.96 for Polonicum (Table 2). The study of correlation shows that there is a significant and negative correlation between the reflectance at Red and Blue and chlorophyll content (r = -0.77*, r = -0.66*; respectively) (Figure 3), but, there is not a significant correlation between leaf reflectance at Green and chlorophyll content. The chlorophylls have strong absorbance peaks in the Red and Blue regions of the spectrum. The negative and significant correlation between reflectance at Red and Blue and chlorophyll content suggest that the decrease in the photosynthetic capacity of the canopy increase leaf reflectance at Red and Blue due to the degradation of chlorophyll content. Since the Blue peak overlaps with the absorbance of the carotenoids, it is not generally used for estimation of chlorophyll content (Sims and Gamon, 2002). Given that the estimation of canopy chlorophyll content by crop reflectance depends on the product of green biomass and chlorophyll concentration at the leaf level (Filella et al., 1995). In the Blue region, both chlorophylls and carotenoids have high absorbances (Penuelas and Filella, 1998). Provided negative coefficients in the region where only chlorophylls absorb (500-700 nm), the positive coefficients in the Blue region might account for variations in the ratio between carotenoids and chlorophylls. Red reflectance, especially when standardized by reflectance in a non-absorbing waveband is highly correlated with chlorophyll content (Turrell et al. 1961; Everitt et al. 1985). Therefore, Red reflectance should be a reliable metric for chlorophyll content (Horler et al., 1980).
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The significant negative correlation between leaf reflectance at Red, Blue and Chlorophyll content (r = 0.77 - 0.77*, r = -0.66 *; respectively) suggests that the degradation in chlorophyll content is paralleled with the increase in leaf reflectance. In addition, the significant and positive correlation between average senescence and leaf reflectance suggests that the development of leaf senescence and reflectance are synchronically. Over all, the rustles of this study prove proportionally the efficiency of using numerical image analysis (Mesurim Pro v 3.3) for estimating leaf reflectance at RGB and the efficiency of using leaf reflectance to estimate chlorophyll content in durum wheat cultivars.
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(1) A. Guendouz, (2) S. Guessoum, (2) K. Maamari and (2) M. Hafsi
(1) National institute of the agronomic research Of Algeria, Research Unit of Setif (INRAA).
(2) University Ferhat ABBAS, Setif, Department of Agronomy, Algeria.
Corresponding Author: A. Guendouz, National institute of the agronomic research Of Algeria, Research Unit of Setif (INRAA).
E-mail: firstname.lastname@example.org / email@example.com
Table 1: Origin of the ten genotypes used in the study. Cultivar Name Origin Cultivar Name Origin 1 Bousselem Algeria 6 Altar Mexico 2 Hoggar Algeria 7 Dukem Mexico 3 Oued Zenati Algeria 8 Kucuk Mexico 4 Polonicum Algeria 9 Mexicali Mexico 5 Waha Algeria 10 Sooty Mexico Table 2: Ranking of tested genotypes for the reflectance at Red, Green and Blue; Chlorophyll content and Average senescence. Reflectance (%) at Genotype RED GREEN BLUE Oued Zenati 46,92(a) 46,47(a) 42,39(a) Altar 32,21(c) 31,17(c) 28,79(c) Sooty 27,52(e) 27,53(f) 29,34(bc) Polonucum 34,63(b) 34,79(b) 30,24(b) Waha 10,81(h) 12,87(i) 13,20(g) Dukem 17,6(g) 18,35 (h) 18,85(f) Mexicali 26,46(e) 25,81(g) 21,64(e) Kucuk 20,47(f) 19,44(h) 11,05(h) Hoggar 29,5(d) 30,19(d) 29,41(bc) Bousselem 29,3(d) 28,6(e) 24,01(d) Mean 27,54 27,55 24,99 Min 10,81 12,87 11,05 Max 46,92 46,47 42,39 [LSD.sub. 1,096 0,957 1,004 0,05] Genotype *** *** *** effect Chlorophyll Average content Genotype (SPAD unit) senescence (%) Oued Zenati 51,48(f) 44,51(f) Altar 56,22(cd) 58,94(bc) Sooty 60,24(ab) 55,5(cde) Polonucum 50,96(f) 51,53(e) Waha 58,65(abc) 63,44(a) Dukem 60,7(a) 60,26(ab) Mexicali 57,94(bcd) 54,18(de) Kucuk 58,85(abc) 54,12(de) Hoggar 55,37(de) 57,53(bcd) Bousselem 53,18(ef) 56,79(bcd) Mean 56,359 55,68 Min 50,96 44,51 Max 60,7 63,44 [LSD.sub. 2,73 4,12 0,05] Genotype *** *** effect Different letters indicated significant difference at 0.05 level.
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|Title Annotation:||Original Articles|
|Author:||Guendouz, A.; Guessoum, S.; Maamari, K.; Hafsi, M.|
|Publication:||American-Eurasian Journal of Sustainable Agriculture|
|Date:||Apr 1, 2012|
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