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Relationship Between Hyperspectral Parameters and Physiological and Biochemical Indexes of Flue-Cured Tobacco Leaves
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Agricultural Sciences in China
2007, 6(6): 665-672
ScienceDirect
June 2007
Relationship Between Hyperspectral Parameters and Physiological and Biochemical Indexes of Flue-Cured Tobacco Leaves LI Xiang-yang, LIU Guo-shun, YANG Yong-feng,ZHAO Chun-hua, YU Q1-wei and SONG Shi-xu National Tobacco Cultivation and Physiology and Biochemistry Research Center of Henan Agricultural University, Zhengzhou 450002, P . R. China
Abstract The experiment was set up for examining the physiological and biological indexes quickly and exactly, for obtaining information of tobacco-field fertilizing and tobacco growing. The ASD Field spec FR 2500 was used to measure spectra reflectance of flue-cured tobacco and the relationship between hyperspectral parameters and biochemical contents (total nitrogen, chlorophyll, carotenoid), and physiological indexes (fresh weight, dry weight, moisture content) of flue-cured tobacco leaves was studied by correlation and stepwise regression statistic methods at different nitrogen and potassium levels. The results indicated that the spectra curves of different treatments had obvious rules and great diversities. There were high correlations between different types of spectra parameters and ten physiological and biochemical indexes of flue-cured tobacco leaves. Hyperspectral characteristic variables of ten physiological and biochemical indexes were found through stepwise regression, and SDr/SDb was the characteristic variable closest to seven biochemical contents. Simultaneously, the R2 and regression coefficient of equations reached 0.05 significant level and the equations had good estimating effects through the examination of other samples. Accordingly, this study suggested that the ten physiological and biochemical indexes could be estimated quickly by the estimating models, at the same time nitrogen-potassium fertilization and growth condition of flue-cured tobacco could be inspected.
Key words: flue-cured tobacco leaves, hyperspectral parameters, physiological and biochemical indexes
INTRODUCTION In recent times, numerous hyperspectral researches have greatly promoted agricultural modernization. However, tobacco cultivation in China still depends on human experience, as a result, using hyperspectral technique is an urgent need to exactly and swiftly monitor tobacco growth status. And now, tobacco farmers apply a large amount of fertilizer to improve yields, but these actions not only decrease tobacco leaf quality, but also cause fertilizer pollution, therefore it is very important to inspect fertilizer quantity by the
hyperspectral technique. Spectra of green plants are determined by their chemical and morphological characters, which have a close relation to growth periods, health conditions, and seasonal phenomenon (Curran 1989; Yang et al. 1999,2000). Daughtry’s study on corn nitrogen experiment (Daughtry et al. 2000) showed that vegetation indexes with near-infrared and red reflectance could reduce background influence, and had good predicting effects for chlorophyll contents, which was helpful to precisely determine nitrogen quantity. Doraiswaniy et al. (2004) used Landsat and MODIS imagery to assess corn and soybean leaf area index and yields. Soil moisture
This paper is translated from its Chinese version in Scientia Agricultura Sinica. LI Xiang-yang. Ph D candidate, Mobile: 13803842507. E-mail: [email protected]: CorrespondenceLIU Guo-shun. Professor, Mobile: 1390371 1851, Fax: +8637 1-63558121, E-mail: liugsh@371 .net
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changes were simulated as part of the crop model, as one of the critical parameters influencing crop condition and yields. Tang et al. (2004) studied canopy and leaf spectra of rice, corn, and cotton, and pointed out that spectral reflectance of three crops had differences, and the red edge parameters had a close relationship with contents of chlorophyll a, b. carotenoid, LAI, and leaf fresh weight. The research on rice spectra in different fertilizer treatments (Patel et al. 1985; Salisbury 1986) showed that agricultural parameters had good correlation with red and near-infrared reflectance, such as leaf nitrogen contents, total chlorophyll contents, and above-ground biomass. Moreover, scientists used excessive regression to analyze the relationships between visible, near-infrared, middle-infrared spectra and LAI, dry biomass, and yield during different periods of rice (Shibayama and Akiyama 1989, L991). Femandez et al. (1994) found that a linearity combination of red light (660 nm) and green light (545 nm) could predict wheat leaf nitrogen contents. The study of Stone et al. ( 1996) also showed that nitrogen spectral indexes had significant correlation with wheat leaf nitrogen absorbability. And it was also feasible to use spectral reflectance to predict cotton leaf nitrogen contents (Lee er al. 2000). Excessive regression was used to study the relationship between seven chemical contents and hyperspectral data mainly in wheat leaves (Niu et al. 2000), the results showed that leaf spectral characters could reflect chemical contents, especially crude protein, nitrogen, potassium, and R2 was greater than 0.8. Research on the red edge parameters of different varieties and fertilizer-water treatments suggested that the red edge and near-infrared amplitude was possible to inspect wheat growth condition and direct fertilizer-water control (Zhao et al. 2002). Wheat grain protein content, sedimentation value, and falling number could also be predicted by canopy reflectance spectra during filling. And it was highly reliable to monitor gliadin and glutenin contents in grains by canopy reflectance spectra at maturity (Li et al. 2005). However, at present, these researches mainly focused on rice, corn, wheat, cotton, and so on, little was related to flue-cured tobacco. Flue-cured tobacco is an important economical crop in China, and the planting areas are beyond one million hectares. Flue-cured tobacco is a broadleaf crop and
LI Xiang-yang et al.
its leaves are the harvest organ, so it has essential differences from those crops that have narrow-small leaves, and harvest flowers and fruits. Thus, the current study was aimed to investigate flue-cured tobacco hyperspectral reflectance characters at different nitrogen, potassium levels, to analyze the relationshipsbetween 10physiologicaland biochemical indexes and hyperspectral parameters, and then to set up the predicting models of physiological-biochemical indexes. As a result, a convenient and effective method to monitor tobacco-field fertilizer and flue-cured tobacco growth could be obtained.
METERIALS AND METHODS Field experimental setups The experiment was carried out on the farm of Henan Agricultural University in Zhengzhou City, China (34"30'N,113"24'E) in 2005. The basic properties of soil were as follows: middle level fertility, 13 g kg-' organic matter, 56.35 mg kg-' alkali hydrolysable N, 15.72 mg kg-I available phosphorus, 97.00 mg kg-' available potassium, pH 7.95, and previous crop was green manure. Experimental setups were: (1) Nitrogen treatments. The flue-cured tobacco K326 was planted in pots. Each pot was filled with 25 kg soil taken from the local field and planted with one tobacco plant. Three nitrogen levels: NO was no nitrogen applied. N1 was 3 g nitrogen applied in each pot. N2 was 6 g nitrogen applied in each pot. In addition, to each pot was applied 4.5 g P,O, and 9 g K,O. (2) Potassium treatments. Material was flue-cured tobacco K326. Five potassium levels: KO was no potassium applied. K1 was 90 kg ha-' K,O. K2 was 180 kg ha-' K,O. K3 was 270 kg ha-' K,O. K4 was 360 kg ha-' K,O. 45 kg ha-' nitrogen and 90 kg ha-' P,O, were applied in each treatment. Each treatment area was 66 mz and the soil was spaced out by plastic to avoid potassium eluviating. The fertilizers were NH,NO,, K,SO,, Ca(H,PO,),H,O. All P,O,, 70% N, and 30% K,O were applied before transplanting. Additional manuring was 30% N and 70% K,O, which was applied at 15 and 30 days after transplanting. The distance between rows was 120 cm and it between plants was
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Relationship Between Hyperspectral Parameters and Physiological and Biochemical Indexes of Flue-Cured Tobacco Leaves
60 cm. Transplanting was conducted on May 10. Cultivation was dependent on normal field management, except for watering in drought.
Testing items Leaf spectra Using America ASD Field spec FR 2500 spectral instrument, which provided spectral coverage from 350 nm to 2 500 nm with sampling intervals of 1.4 nm in the 350-1 000 nm range and 2 nm in the 1000-2 500 nm range, the laboratory spectra was tested with 50 W glass halogen lamp as the radiation source, a pressed barium sulfate (BaSO,) reference standard, 45 cm distance from lamp to leaves, and 70" azimuth angle (lamp to leaf surface). Six data were measured, and the first two, the second two, and the third two were measured respectively in leaf top ends, middle parts, and near leafstalks. One point was tested 10 times, and the sample spectra were the average of 10 times. Leaves were put flatly on the black rubber, whose reflectivity was near zero. Probe of the spectrum instrument was down straight with 8" field of view optics. The distance was 10 cm from probe to leaves and view diameter was about 1.4 cm. Samples were selected from healthy leaves at 50, 75, 95, and 115 days after transplanting, which were periods of fast growing, harvest time of lower leaves, cutters, and upper leaves. At every stage, three plants were selected, which grew consistently and reflected 'fertilizer conditions. Then each leaf was taken from 4, 10, 16 stalk positions, except for the 4 stalk position in the harvest time of cutters and upper leaves, and the 10 stalk position in the harvest time of upper leaves. Stalk positions were marked from bottom to top at 40 days after transplanting and after four primings. Physiological and biochemical indexes The leaf physiological and biochemical indexes were tested after their spectra were tested. The items and testing methods were as follows. Physiologicalindexes: (1) Leaf fresh weight (FW) weighing : the fresh leaves at once after picking off from plants. (2) Leaf dry weight (DW): 15 minutes in 105"C, drying in 60°C. (3) Moisture content (%) = (Fresh weight - Dry weight)/Fresh weight x 100.
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Biochemical indexes: (1) Total nitrogen (TN): Micro-Kjeldahl (China Standardization Press 1998). (2) Chlorophyll a (Chla), chlorophyll b (Chlb), carotenoid (Car): spectrophotometer method (Zhang et aE 1994). (3) Total chlorophyll a (Tchla), total chlorophyll b (Tchlb), total carotenoid (TCar): Chla, Chlb, Car multiply leaf fresh weight.
Analysis methods Spectra analysis Spectral differential technique was used to calculate the first derivative reflectance spectra (Tsai and Philpot 1988) and 19 spectral variables were obtained, which were divided into three types. (1) Position variables: The red edge amplitude (Dr), the blue edge amplitude (Db), the yellow edge amplitude (Dy) were the maximum of derivative reflectance spectra in the wavelength of 680-760, 490-530, and 560-640 nm. The red edge position (hr),the blue edge position (hb),and the yellow edge position (hy) were the wavelength of the red edge, blue edge, and yellow edge amplitude. The green peak amplitude (Rg) was the maximal reflectance in a wavelength of 510-560 nm. The green peak position (hg) was the wavelength of the green peak. The red vale amplitude (Rr) was the minimal reflectance in wavelength of 640-680 nm. The red vale position (hv) was the wavelength of the red vale amplitude. (2) Area positions: The red edge area (SDr), the blue edge area (SDb), the yellow edge area (Sdy) were the sum of all the first derivative reflectance spectra 680760,490-530, and 560-640 nm, respectively. (3) Vegetation indexes: Ratio of Rg and Rr (Rg/Rr); normalized difference green peak and red vale (Rg Rr)/(Rg + Rr); ratio of SDr and SDb (SDr/SDb); ratio of SDr and SDy (SDr/SDy); normalized difference red edge area and blue edge area (SDr - SDb)/(SDr + SDb); normalized difference red edge area and yellow edge area (SDr - SDy)/(SDr + SDy). Data analysis The Excel was used to analyze the basic data and SPSS12.0 to analyze the correlation and regression and to test its significant level. The samples of correlation and regreseion were 128, and the testing samples were 21.
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RESULTS Reflectance of flue-cured tobacco leaves in different nitrogen and potassium treatments In Fig., reflectance spectral curves of flue-cured tobacco leaves in different N, K treatments had obvious variation and showed great difference between treatments. In the visible light range, reflectance had obvious peak value in green light of 510-560 nm, and absorbing vale in red light of 620-760 nm. Reflectance reduced with an increase in applied nitrogen, especially in green light range, as pigment contents increase with applied nitrogen and potassium. In the near-infrared plateau area (760-1 300 nm), leaf reflectance was higher than other wavelengths, mainly because leaf tissue made light reflected and scattered many times. Reflectance increased with applied nitrogen, and potassium also had relation with leaf tissue diversity. Spectra reflectance changed during different growth periods, but the change rules of different treatments appeared consistent, and hence, it is not listed here.
Correlation between flue-cured tobacco leaf physiological and biochemical indexes and the hyperspectralposition variables The physiological and biochemical indexes of flue-cured tobacco leaf had a high correlation with the hyperspectral position variables in Table 1. Correlation -NO
coefficients reached 0.05 significant level, except for Tchlb and Dy, Chlb and Dy, and Mc and Dr. And most of them reached 0.01 significant level, except for Car and Dy, F W and Dr. By comparison, the correlations between lr, Dr, Db, Rg, lv and seven biochemical contents were better than that with three physiological indexes. In seven biochemical indexes, the correlations between total nitrogen, total chlorophyll a, chlorophyll a contents, and most position variables except for Dy were better than other biochemical indexes. All of the 10 physiological and biochemical indexes had positive correlation with lr, lb, ly, and negative correlation with other position variables. It has been shown, through analysis of correlation, that in 10 position variables, Rg had the closest relationship with TN. hr had the closest relationship with Chla, Chlb, Car, Tchla, TChlb, and Tcar. hg had the closest relationship with FW and MC. Rg had the closest relationship with DW.
Correlation between flue-cured tobacco leaf physiological and biochemical indexes and the hyperspectralarea and vegetation variables It could be found from Table 2 that there were quite higher correlations between the physiological and biochemical indexes and the hyperspectral area and vegetation variables. Most correlation coefficients reached 0.05 significant level, except correlation between Tchla, Car, MC, and (SDr - SDy)/(SDr + SDy), all others
-KO
0.7 r
-K I
I
-N1
0.6 0.5 0.4
0.3 0.2 0.1
0I 350
850
1350
1850
Wavelength (nm)
2350
iiil
I
1
I
850
1350
1850
2 350
Wavelength (nm)
Fig. The leaf spectra reflectance under different treatments 75 days after transplanting. A, different N levels; B, different K levels.
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RelationshiD Between Hvuers~ectralParameters and Physiological and Biochemical Indexes of Flue-Cured Tobacco Leaves
reached 0.01 significant level. The correlation not reaching 0.05 significant level were as follows: Ten physiological and biochemical indexes and SDr/SDy, eight physiological and biochemical indexes except F W and MC with SDy, Tcar, and DW with (SDr - SDy)/ (SDr + SDy). Comparing correlation coefficients, SDr, SDb, SDr/SDb, and (SDr - SDb)/(SDr + SDb) had better correlation with TN, Tchla, Tchlb, Tcar, Chla, Chlb, and Car, than with FW, DW, and MC, and the correlation coefficients of TN, Tchla, Tchlb were bigger than others. In all area variables, the negative correlation coefficients were as follows: SDb, SDr/SDy and 10 physiological and biochemical indexes; (SDr SDy)/(SDr + SDy) and TN, Tchla, Tchlb, Tcar, Chla, Chlb, Car; SDy and Tcar, FW, DW, and MC. And other correlation coefficients were positive. By analyzing the correlation in Table 2, it was found that SDb had the closest relationship with 10 physiological and biochemical indexes in all area variables. SDr/SDb had the closest relationship with TN, Tchla, Tchlb, Tcar, Chla, Chlb, and Car. (Rg - Rr)/(Rg + Rr) and FW, (SDr - SDb)/(SDr + SDb) and DW, Rg/Rr
and MC had the closest relationship.
Constitution and verification of regression equations of fluecuredtobacco physiological and biochemicalindexes For estimating physiological and biochemical indexes of flue-cured tobacco using spectral methods, the spectral regression and contrary evolutive models need to be set up by hyperspectral indexes. Theoretically, the independent could be found by stepwise regression, and had the closest correlation with the dependent, so it had the highest estimating precision. In Table 3, all R2of stepwise regression equations reached 0.05 significant. Independents were screened out by different regression equations of physiological and biochemical indexes, and most regression coefficients reached 0.01 significant. According to relative importance from big to small, characteristic variables of TN and Car were SDr/SDb and hv. Characteristic variables of Tchla were SDr/SDb, hv, hg, and (SDr SDb)/(SDr + SDb). Characteristic variables of Tchlb
Table 1 Correlation analyses between leaf physiological and biochemical indexes and the position variables of hyperspectra TN Tchla Tchlb TCar Chla Chlb Car
Fw DW
MC
k
Dr
b
Db
XY
0.787" 0.850" 0.695" 0.652" 0.888" 0.729"
-0.448" -0.443" -0.343" -0.293" -0.470" -0.362"
0.57 1** 0.627" 0.434" 0.534" 0.652" 0.463"
0.366" 0.398" 0.252" 0.352" 0.425" 0.272"
0.621" 0.289" 0.251" 0.390"
-0.281"
0.478" 0.457" 0.347" 0.473"
-0.740" -0.766" -0.622" -0.578" -0.842" -0.675" -0.592"
-0.182' -0.260" -0.175
-0.258" -0.320" -0.315"
DY -0.250" -0.268" -0.136 -0.276" -0.265" -0.14
0.319" 0.322" 0.209'
-0.198' -0.399" -0.254"
0.325"
-0.377"
;hS -0.691" -0.757" -0.592" -0.649" -0.755" -0.605"
Rg -0.797" -0.827" -0.622" -0.651" -0.87 1'' -0.660"
-0.535" -0.498" -0.330" -0.575"
-0.606" -0.424" -0.368" -0.484"
b
Rr
-0.779" -0.835" -0.641" -0.625" -0.805" -0.635"
-0.546" -0.577" -0.403" -0.491" -0.600" -0.426" -0.437"
-0.486" -0.47 1*' -0.35 I" -0.531"
-0.389" -0.280" -0.428"
"Significant (P < 0.01); 'significant (P < 0.05). The same as below.
Table 2 Correlation analyses between leaf physiological and biochemical indexes and the area variables and vegetation indices of hyperspectra TN Tchla Tchlb
l k Chla Chlb
car Fw DW MC
SDr
SDb
SDV
Re&
(Rfi-Rr)/(Rfi+Rr)
SDrlSDb
SDdSDv
(SDr-SDb)/(SDr+SDb)
0.522"
-0.797"
0.625" 0.655" 0.437" 0.549" 0.673" 0.46 1*' 0.464" 0.495" 0.350" 0.544"
-0.123
0.755"
-0.235"
-0.830" -0.643" -0.640" -0.877" -0.681" -0.601" -0.397" -0.378" -0.452"
0.651" 0.673" 0.449" 0.558" 0.691" 0.475" 0.473" 0.491" 0.347" 0.549"
0.808"
0.570" 0.407" 0.462" 0.648" 0.461" 0.480" 0.286" 0.261"
0.054 0.034 0.1 15
0.867" 0.719" 0.677" 0.928" 0.768" 0.675" 0.290" 0.294" 0.373"
-0.131 -0.09 -0.13 -0.138 -0.098 -0.118 -0.136 -0.125 -0.157
0.804" 0.608" 0.639" 0.865" 0.654" 0.613" 0.411" 0.373" 0.461"
-0.230' -0.262" -0.088 -0.299" -0.303" -0.203' 0.241" 0.023 0.203'
0.333"
-0.049 0.054 0.126 0.026 -0.277" -0.109 -0.259"
(SDr-SDv)/(SDr+SDv)
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and Chlb were SDr/SDb. Characteristic variables of Tcar were SDr/SDb, hg, and (SDr - SDb)/(SDr + SDb). Characteristicvariables of Chla were SDr/SDb, hg, and (SDr - SDy)/(SDr + SDy). Characteristic variables of FW were SDr, SDb, Db, (SDr - SDy)/(SDr + SDy), and Rg/Rr. Characteristic variables of DW were (SDr - SDb)/(SDr + SDb), hr, and Dr. Characteristic variables of MC were hg, SDr, hv, (SDr - SDy)/(SDr + SDy). The order of characteristic variable relative importance was almost consistent with correlation coefficient values, except for FW. Through analysing the results of SPSS, hg was the main characteristic variable of FW when independent numbers were less. With the numbers increasing, the importance of hg reduced, hence the characteristic variables of FW changed. Fourteen random leaves were selected to compute the values of physiological and biochemical indexes by regression models, and to test the difference of mea-
sures and estimated values. The results are shown in Table 4. All the differences of measures and estimated values did not reach 0.05 significant level, which showed that there were no obvious differences between measures and estimated values. Accordingly, the regression of physiological and biochemical indexes had good estimating effect.
DISCUSSION Nitrogen is an essential element to influence and determine flue-cured tobacco growth, quality, and yield. Lack of potassium could degrade the quality of fluecured tobacco leaves. However, too much fertilizer could make tobacco plants grow too big and become black tobacco after curing. At the same time, nitrogen and potassium have strong eluviation to pollute soil and
'IBble 3 Stepwise regression equations of leaf physiological and biochemical indexes by the hyperspectral parameters Dependent
TN Tcbla
Regression equation
j
+
= 77.751 0 . 3 3 9 ~ - ~0.114x2
j=666.928+1.631x1- 0 . 3 1 7 ~- ~0 . 8 0 9 ~-~1 1 . 9 6 8 ~ ~
Independent
Thenameof
Regression
indeDendent
coeffcient mbabilitv
R=
SDrISDb
0.000
0.680"
av
0.801"
SDrISDb
0.005 0.000
b
0.002
119
0.000
(SDr-SDb)l(SDr+SDb)
0.001
Tchlb
= -0.168 + 0 . 5 1 4 ~ ~
SDrlSDb
0.000
0.514"
Tcra
=121.618+0.33~1-0.217~2- 2.898~3
SDrISDb
0.000
0.541"
hg
0.000
(SDr-SDb)l(SDr+SDb)
0.004
SDrISDb
0.000 0.000
Chla
j
+
= 12778.59 4 4 . 1 8 ~ -~2 2 . 5 3 9 ~-~144.334%
lig (SDr-SDy)/(SDr+SDy)
0.830"
0.003
Chlb
j =-27.372 + 2 0 . 3 3 9 ~ ~
SDrlSDb
0.000
0.560"
cat
j=-1581.31+12.736xl +2.368x2
SDrlSDb
0.000
0.445'
av
0.045
SDr
0.000
SDb
0.000
Db
0.000
(SDr-SDy)l(SDr+SDy)
0.000
Rg/Rr (SDr-SDb)l(SDr+SDb)
0.003
k
0.000
Fw
DW
MC
0.000
Dr
0.003
hg SDr
0.000
av
0.000 0.000
(SDr-SDY)/(SDr+SDy)
0.000
0.626"
0.444'
0.548"
Relationshiu Between HvuersDectral Parameters and Physiological and Biochemical Indexes of Flue-Cured Tobacco Leaves
67 1
Table 4 Test for the stepwise regression equations of leaf physiological and biochemical indexes Estimated value-Measured value
TN Tchla Tchlb Tcar Chla Chlb Cin
Fw DW MC
Paired differences Mean
Std. deviation
Std. error mean
df
Sig. (?-tailed)
0.233 0.3159 -0.1177 -0.2074 -9.3895 -1.4006 -4.5325 4.5161 0.4114 -0.0043
0.6953 1.2282 0.9054 0.5129 40.8528 33.0314 23.998 10.7676 1.4811 0.0629
0.1858 0.3282 0.242 0.1371 10.9184 8.828 6.4137 2.9864 0.4108 0.0174
13 13 13 13 13 13 13 12 12 12
0.232 0.353 0.635 0.154 0.405 0.876 0.492 0.156 0.336 0.810
groundwater. Thus, the need is to diagnose tobaccofield fertilizer and obtain tobacco growth information quickly so as to protect agricultural environment and realize sustainabledevelopment. Therefore, hyperspectral detection is a convenient arid effective method. Out of the above purposes, the hyperspectral technique was applied to flue-cured tobacco production in this experiment. It described the spectral characteristics of flue-cured tobacco with special botany properties and analyzed the relationship between hyperspectral variables and physiological and biochemical indexes. Also, the regression models were set up to estimate physiological and biochemical indexes. The results showed that the spectral curves of different nitrogen and potassium treatments had obvious differences, especially in green and near-infrared light range, so the relative height of the spectral curves could reflect the difference of fertilizer quantity. Correlation analysis indicated that spectral characteristics had a close relationship with 10 physiological and biochemical indexes. The R2 and regression coefficients of equations reached 0.05 significant levels, and they reached 0.01 significant levels except for Car and DW. The regression equations had good estimating effects by other sample tests, which also showed that the characteristic variables screened out by these equations were of precision and credibility. Of course, the relationship discussed in this article aimed at all the tobacco leaves, including different growth periods and treatments. The idiographic analysis of each period and treatment was not carried through because of the limit on article length. Furthermore, spectral observations in the room have good effect due to the strict light-house and environment, which can provide reference for tobaccofield observations. However, it is necessary to per-
form field observations and apply altitude remote sensing to realize site-specific inspection at the correct time in the future.
CONCLUSION Through the observation of flue-cured tobacco leaf hyperspectral reflectance in the room, primary cognition has been obtained regarding the spectral characteristics in different N, K treatments. The spectral characteristic variables screened by correlation and regression have briefness, definitude, and strong maneuverability. SDdSDb is the closest spectral characteristic variable with TN, Tchla, Tchlb, Tcar, Chla, Chalb, and Car. SDr is the closest spectral characteristic variable with FW. (SDr - SDb)/(SDr + SDb) is the closest spectral characteristic variable with DW. hg is the closest spectral characteristic variable with MC. The regression equations can estimate 10 physiological and biochemical indexes quickly and accurately. The testing effects are also good. Therefore, our research indicates that hyperspectral technique can provide new and operable methods to realize a quick measurement of physiological and biochemical components and precision agriculture.
Acknowledgements The study was supported by the Key Program of National Tobacco Monoply Bureau of China (1 10200401021).
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Daughtry C S T, Walthall C L, Kim M S , Brown de Colstoun E, McMurtrey I11 J E. 2000. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74, 229-239. Doraiswamy P C, Hatfield J H, Jackson T J, Akhmedov B, h e g e r J, Stem A. 2004. Crop condition and yield simulations using Landsat and MODIS. Remote Sensing of Environment, 92, 548-559. Fernandez S , Vidal D, Simon E, Sole-Sugranes L. 1994. Radiometric characteristics of Triticium aestivum cv. Astral under water and nitrogen stress. International Journal of Remote Sensing, 15, 1867-1884. Lee T, Raja Reddy K, Sassenrath-Cole G F. 2000. Reflectance indices with precision and accuracy in predicting cotton leaf nitrogen concentration. Crop Science, 40, 1814-1819. Li Y X, Zhu Y, Tian Y C, You X T, Zhou D Q , Cao W X. 2005. Relationship of grain protein content and relevant quality traits to canopy reflectance spectra in wheat. Scientia Agricultura Sinica, 38, 1332-1338. (in Chinese) Liu G S , Ye X F, Wang Y T, Li X L, Ma L X. 2004. Effect of different potassium application rates on the contents of aroma constituents in flue-cured tobacco. Chinese Tobacco Science, 4, 1-4. (in Chinese) Liu G S. 2003. Tobacco Cultivation. China Agricultural Press, Beijing. pp. 142-145. (in Chinese) N u Z, Chen Y H, Sui H Z, Zhang Q Y, Zhao C J . 2000. Mechanism analysis of leaf biochemical concentration by high spectral remote sensing. Journal of Remote Sensing, 4, 125-129. (in Chinese) Patel N K, Singh T P, Sahai B, Patel M S. 1985. Spectral response of rice crop and its relation to yield and yield attributes. International Journal of Remote Sensing, 6,657-664. Pu R L,Gong P. 2000. Hyper Spectral Remote Sensing and Its Applications. Higher Education Press, Beijing. pp. 82-83. (in Chinese) Salisbury J W. 1986. Preliminary measurements of leaf spectral reflectance in the 8-14 pm region. International Journal of Remote Sensing, 7, 1879-1886.
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Shibayama M, Akiyama T. 1989. Seasonal visible, near-infrared and mid-infrared spectra of rice canopies in relation to LA1 and above-ground dry phytomass. Remote Sensing of Environment, 27, 1 19-127. Shibayama M, Akiyama T. 1991. Estimating grain yield of maturing rice canopies using high spectral resolution reflectance measurements. Remote Sensing of Environment, 36,45-53. Stone M L, Solie J B, Raun W R, Whitney R W, Taylor S L, Ringer J D. 1996. Use of spectral radiance for correcting inseason fertilizer nitrogen deficiencies in winter wheat. Transactions of ASAE (American Society of Agricultural Engineering), 39,1623-1631. Tang Y L, Huang J F, Wang X Z, Wang R C, Wang F M. 2004. Comparison on the characteristics of hyperspectral and the red edge in rice, corn and cotton. Scientia Agricultura Sinica, 37, 29-35. (in Chinese) Tsai F, Philpot W. 1998. Derivative analysis of hyperspectral data. Remote Sensing of Environment, 66,41-51. Yang H, Zhang J, van Der Meer F, Kroonenberg S B. 2000. Imaging spectrometry data correlated to hydrocarbon microseepage. International Journal of Remote Sensing, 21, 197-202. Yang H, Zhang J, van Der Meer F, Kroonenberg S B. 1999. Spectral characteristics of wheat associated with hydrocarbon microseepages. International Journal of Remote Sensing, 20, 807-81 3. Zhang Z L, Qu W Q. 2003. The Experimental Guide f o r Plant Physiology. Higher Education Press, Beijing. pp. 154-157. (in Chinese) Zhang Z L, Qu W Q. 2003. The Experimental Guide f o r Plant Physiology. Higher Education Press, Beijing. pp. 67-70. (in Chinese) Zhao C J, Huang W J, Wang J H, Yang M H, Xue X Z. 2002. Studies on the red edge parameters of spectrum in winter wheat under diferent varieties, fertilizer and water treatments. Scientia Agricultura Sinica, 35,980-987. (in Chinese) (Edited by ZHAO Qi)
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Agricultural Sciences in China
2007, 6(6): 665-672
ScienceDirect
June 2007
Relationship Between Hyperspectral Parameters and Physiological and Biochemical Indexes of Flue-Cured Tobacco Leaves LI Xiang-yang, LIU Guo-shun, YANG Yong-feng,ZHAO Chun-hua, YU Q1-wei and SONG Shi-xu National Tobacco Cultivation and Physiology and Biochemistry Research Center of Henan Agricultural University, Zhengzhou 450002, P . R. China
Abstract The experiment was set up for examining the physiological and biological indexes quickly and exactly, for obtaining information of tobacco-field fertilizing and tobacco growing. The ASD Field spec FR 2500 was used to measure spectra reflectance of flue-cured tobacco and the relationship between hyperspectral parameters and biochemical contents (total nitrogen, chlorophyll, carotenoid), and physiological indexes (fresh weight, dry weight, moisture content) of flue-cured tobacco leaves was studied by correlation and stepwise regression statistic methods at different nitrogen and potassium levels. The results indicated that the spectra curves of different treatments had obvious rules and great diversities. There were high correlations between different types of spectra parameters and ten physiological and biochemical indexes of flue-cured tobacco leaves. Hyperspectral characteristic variables of ten physiological and biochemical indexes were found through stepwise regression, and SDr/SDb was the characteristic variable closest to seven biochemical contents. Simultaneously, the R2 and regression coefficient of equations reached 0.05 significant level and the equations had good estimating effects through the examination of other samples. Accordingly, this study suggested that the ten physiological and biochemical indexes could be estimated quickly by the estimating models, at the same time nitrogen-potassium fertilization and growth condition of flue-cured tobacco could be inspected.
Key words: flue-cured tobacco leaves, hyperspectral parameters, physiological and biochemical indexes
INTRODUCTION In recent times, numerous hyperspectral researches have greatly promoted agricultural modernization. However, tobacco cultivation in China still depends on human experience, as a result, using hyperspectral technique is an urgent need to exactly and swiftly monitor tobacco growth status. And now, tobacco farmers apply a large amount of fertilizer to improve yields, but these actions not only decrease tobacco leaf quality, but also cause fertilizer pollution, therefore it is very important to inspect fertilizer quantity by the
hyperspectral technique. Spectra of green plants are determined by their chemical and morphological characters, which have a close relation to growth periods, health conditions, and seasonal phenomenon (Curran 1989; Yang et al. 1999,2000). Daughtry’s study on corn nitrogen experiment (Daughtry et al. 2000) showed that vegetation indexes with near-infrared and red reflectance could reduce background influence, and had good predicting effects for chlorophyll contents, which was helpful to precisely determine nitrogen quantity. Doraiswaniy et al. (2004) used Landsat and MODIS imagery to assess corn and soybean leaf area index and yields. Soil moisture
This paper is translated from its Chinese version in Scientia Agricultura Sinica. LI Xiang-yang. Ph D candidate, Mobile: 13803842507. E-mail: [email protected]: CorrespondenceLIU Guo-shun. Professor, Mobile: 1390371 1851, Fax: +8637 1-63558121, E-mail: liugsh@371 .net
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changes were simulated as part of the crop model, as one of the critical parameters influencing crop condition and yields. Tang et al. (2004) studied canopy and leaf spectra of rice, corn, and cotton, and pointed out that spectral reflectance of three crops had differences, and the red edge parameters had a close relationship with contents of chlorophyll a, b. carotenoid, LAI, and leaf fresh weight. The research on rice spectra in different fertilizer treatments (Patel et al. 1985; Salisbury 1986) showed that agricultural parameters had good correlation with red and near-infrared reflectance, such as leaf nitrogen contents, total chlorophyll contents, and above-ground biomass. Moreover, scientists used excessive regression to analyze the relationships between visible, near-infrared, middle-infrared spectra and LAI, dry biomass, and yield during different periods of rice (Shibayama and Akiyama 1989, L991). Femandez et al. (1994) found that a linearity combination of red light (660 nm) and green light (545 nm) could predict wheat leaf nitrogen contents. The study of Stone et al. ( 1996) also showed that nitrogen spectral indexes had significant correlation with wheat leaf nitrogen absorbability. And it was also feasible to use spectral reflectance to predict cotton leaf nitrogen contents (Lee er al. 2000). Excessive regression was used to study the relationship between seven chemical contents and hyperspectral data mainly in wheat leaves (Niu et al. 2000), the results showed that leaf spectral characters could reflect chemical contents, especially crude protein, nitrogen, potassium, and R2 was greater than 0.8. Research on the red edge parameters of different varieties and fertilizer-water treatments suggested that the red edge and near-infrared amplitude was possible to inspect wheat growth condition and direct fertilizer-water control (Zhao et al. 2002). Wheat grain protein content, sedimentation value, and falling number could also be predicted by canopy reflectance spectra during filling. And it was highly reliable to monitor gliadin and glutenin contents in grains by canopy reflectance spectra at maturity (Li et al. 2005). However, at present, these researches mainly focused on rice, corn, wheat, cotton, and so on, little was related to flue-cured tobacco. Flue-cured tobacco is an important economical crop in China, and the planting areas are beyond one million hectares. Flue-cured tobacco is a broadleaf crop and
LI Xiang-yang et al.
its leaves are the harvest organ, so it has essential differences from those crops that have narrow-small leaves, and harvest flowers and fruits. Thus, the current study was aimed to investigate flue-cured tobacco hyperspectral reflectance characters at different nitrogen, potassium levels, to analyze the relationshipsbetween 10physiologicaland biochemical indexes and hyperspectral parameters, and then to set up the predicting models of physiological-biochemical indexes. As a result, a convenient and effective method to monitor tobacco-field fertilizer and flue-cured tobacco growth could be obtained.
METERIALS AND METHODS Field experimental setups The experiment was carried out on the farm of Henan Agricultural University in Zhengzhou City, China (34"30'N,113"24'E) in 2005. The basic properties of soil were as follows: middle level fertility, 13 g kg-' organic matter, 56.35 mg kg-' alkali hydrolysable N, 15.72 mg kg-I available phosphorus, 97.00 mg kg-' available potassium, pH 7.95, and previous crop was green manure. Experimental setups were: (1) Nitrogen treatments. The flue-cured tobacco K326 was planted in pots. Each pot was filled with 25 kg soil taken from the local field and planted with one tobacco plant. Three nitrogen levels: NO was no nitrogen applied. N1 was 3 g nitrogen applied in each pot. N2 was 6 g nitrogen applied in each pot. In addition, to each pot was applied 4.5 g P,O, and 9 g K,O. (2) Potassium treatments. Material was flue-cured tobacco K326. Five potassium levels: KO was no potassium applied. K1 was 90 kg ha-' K,O. K2 was 180 kg ha-' K,O. K3 was 270 kg ha-' K,O. K4 was 360 kg ha-' K,O. 45 kg ha-' nitrogen and 90 kg ha-' P,O, were applied in each treatment. Each treatment area was 66 mz and the soil was spaced out by plastic to avoid potassium eluviating. The fertilizers were NH,NO,, K,SO,, Ca(H,PO,),H,O. All P,O,, 70% N, and 30% K,O were applied before transplanting. Additional manuring was 30% N and 70% K,O, which was applied at 15 and 30 days after transplanting. The distance between rows was 120 cm and it between plants was
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Relationship Between Hyperspectral Parameters and Physiological and Biochemical Indexes of Flue-Cured Tobacco Leaves
60 cm. Transplanting was conducted on May 10. Cultivation was dependent on normal field management, except for watering in drought.
Testing items Leaf spectra Using America ASD Field spec FR 2500 spectral instrument, which provided spectral coverage from 350 nm to 2 500 nm with sampling intervals of 1.4 nm in the 350-1 000 nm range and 2 nm in the 1000-2 500 nm range, the laboratory spectra was tested with 50 W glass halogen lamp as the radiation source, a pressed barium sulfate (BaSO,) reference standard, 45 cm distance from lamp to leaves, and 70" azimuth angle (lamp to leaf surface). Six data were measured, and the first two, the second two, and the third two were measured respectively in leaf top ends, middle parts, and near leafstalks. One point was tested 10 times, and the sample spectra were the average of 10 times. Leaves were put flatly on the black rubber, whose reflectivity was near zero. Probe of the spectrum instrument was down straight with 8" field of view optics. The distance was 10 cm from probe to leaves and view diameter was about 1.4 cm. Samples were selected from healthy leaves at 50, 75, 95, and 115 days after transplanting, which were periods of fast growing, harvest time of lower leaves, cutters, and upper leaves. At every stage, three plants were selected, which grew consistently and reflected 'fertilizer conditions. Then each leaf was taken from 4, 10, 16 stalk positions, except for the 4 stalk position in the harvest time of cutters and upper leaves, and the 10 stalk position in the harvest time of upper leaves. Stalk positions were marked from bottom to top at 40 days after transplanting and after four primings. Physiological and biochemical indexes The leaf physiological and biochemical indexes were tested after their spectra were tested. The items and testing methods were as follows. Physiologicalindexes: (1) Leaf fresh weight (FW) weighing : the fresh leaves at once after picking off from plants. (2) Leaf dry weight (DW): 15 minutes in 105"C, drying in 60°C. (3) Moisture content (%) = (Fresh weight - Dry weight)/Fresh weight x 100.
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Biochemical indexes: (1) Total nitrogen (TN): Micro-Kjeldahl (China Standardization Press 1998). (2) Chlorophyll a (Chla), chlorophyll b (Chlb), carotenoid (Car): spectrophotometer method (Zhang et aE 1994). (3) Total chlorophyll a (Tchla), total chlorophyll b (Tchlb), total carotenoid (TCar): Chla, Chlb, Car multiply leaf fresh weight.
Analysis methods Spectra analysis Spectral differential technique was used to calculate the first derivative reflectance spectra (Tsai and Philpot 1988) and 19 spectral variables were obtained, which were divided into three types. (1) Position variables: The red edge amplitude (Dr), the blue edge amplitude (Db), the yellow edge amplitude (Dy) were the maximum of derivative reflectance spectra in the wavelength of 680-760, 490-530, and 560-640 nm. The red edge position (hr),the blue edge position (hb),and the yellow edge position (hy) were the wavelength of the red edge, blue edge, and yellow edge amplitude. The green peak amplitude (Rg) was the maximal reflectance in a wavelength of 510-560 nm. The green peak position (hg) was the wavelength of the green peak. The red vale amplitude (Rr) was the minimal reflectance in wavelength of 640-680 nm. The red vale position (hv) was the wavelength of the red vale amplitude. (2) Area positions: The red edge area (SDr), the blue edge area (SDb), the yellow edge area (Sdy) were the sum of all the first derivative reflectance spectra 680760,490-530, and 560-640 nm, respectively. (3) Vegetation indexes: Ratio of Rg and Rr (Rg/Rr); normalized difference green peak and red vale (Rg Rr)/(Rg + Rr); ratio of SDr and SDb (SDr/SDb); ratio of SDr and SDy (SDr/SDy); normalized difference red edge area and blue edge area (SDr - SDb)/(SDr + SDb); normalized difference red edge area and yellow edge area (SDr - SDy)/(SDr + SDy). Data analysis The Excel was used to analyze the basic data and SPSS12.0 to analyze the correlation and regression and to test its significant level. The samples of correlation and regreseion were 128, and the testing samples were 21.
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RESULTS Reflectance of flue-cured tobacco leaves in different nitrogen and potassium treatments In Fig., reflectance spectral curves of flue-cured tobacco leaves in different N, K treatments had obvious variation and showed great difference between treatments. In the visible light range, reflectance had obvious peak value in green light of 510-560 nm, and absorbing vale in red light of 620-760 nm. Reflectance reduced with an increase in applied nitrogen, especially in green light range, as pigment contents increase with applied nitrogen and potassium. In the near-infrared plateau area (760-1 300 nm), leaf reflectance was higher than other wavelengths, mainly because leaf tissue made light reflected and scattered many times. Reflectance increased with applied nitrogen, and potassium also had relation with leaf tissue diversity. Spectra reflectance changed during different growth periods, but the change rules of different treatments appeared consistent, and hence, it is not listed here.
Correlation between flue-cured tobacco leaf physiological and biochemical indexes and the hyperspectralposition variables The physiological and biochemical indexes of flue-cured tobacco leaf had a high correlation with the hyperspectral position variables in Table 1. Correlation -NO
coefficients reached 0.05 significant level, except for Tchlb and Dy, Chlb and Dy, and Mc and Dr. And most of them reached 0.01 significant level, except for Car and Dy, F W and Dr. By comparison, the correlations between lr, Dr, Db, Rg, lv and seven biochemical contents were better than that with three physiological indexes. In seven biochemical indexes, the correlations between total nitrogen, total chlorophyll a, chlorophyll a contents, and most position variables except for Dy were better than other biochemical indexes. All of the 10 physiological and biochemical indexes had positive correlation with lr, lb, ly, and negative correlation with other position variables. It has been shown, through analysis of correlation, that in 10 position variables, Rg had the closest relationship with TN. hr had the closest relationship with Chla, Chlb, Car, Tchla, TChlb, and Tcar. hg had the closest relationship with FW and MC. Rg had the closest relationship with DW.
Correlation between flue-cured tobacco leaf physiological and biochemical indexes and the hyperspectralarea and vegetation variables It could be found from Table 2 that there were quite higher correlations between the physiological and biochemical indexes and the hyperspectral area and vegetation variables. Most correlation coefficients reached 0.05 significant level, except correlation between Tchla, Car, MC, and (SDr - SDy)/(SDr + SDy), all others
-KO
0.7 r
-K I
I
-N1
0.6 0.5 0.4
0.3 0.2 0.1
0I 350
850
1350
1850
Wavelength (nm)
2350
iiil
I
1
I
850
1350
1850
2 350
Wavelength (nm)
Fig. The leaf spectra reflectance under different treatments 75 days after transplanting. A, different N levels; B, different K levels.
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RelationshiD Between Hvuers~ectralParameters and Physiological and Biochemical Indexes of Flue-Cured Tobacco Leaves
reached 0.01 significant level. The correlation not reaching 0.05 significant level were as follows: Ten physiological and biochemical indexes and SDr/SDy, eight physiological and biochemical indexes except F W and MC with SDy, Tcar, and DW with (SDr - SDy)/ (SDr + SDy). Comparing correlation coefficients, SDr, SDb, SDr/SDb, and (SDr - SDb)/(SDr + SDb) had better correlation with TN, Tchla, Tchlb, Tcar, Chla, Chlb, and Car, than with FW, DW, and MC, and the correlation coefficients of TN, Tchla, Tchlb were bigger than others. In all area variables, the negative correlation coefficients were as follows: SDb, SDr/SDy and 10 physiological and biochemical indexes; (SDr SDy)/(SDr + SDy) and TN, Tchla, Tchlb, Tcar, Chla, Chlb, Car; SDy and Tcar, FW, DW, and MC. And other correlation coefficients were positive. By analyzing the correlation in Table 2, it was found that SDb had the closest relationship with 10 physiological and biochemical indexes in all area variables. SDr/SDb had the closest relationship with TN, Tchla, Tchlb, Tcar, Chla, Chlb, and Car. (Rg - Rr)/(Rg + Rr) and FW, (SDr - SDb)/(SDr + SDb) and DW, Rg/Rr
and MC had the closest relationship.
Constitution and verification of regression equations of fluecuredtobacco physiological and biochemicalindexes For estimating physiological and biochemical indexes of flue-cured tobacco using spectral methods, the spectral regression and contrary evolutive models need to be set up by hyperspectral indexes. Theoretically, the independent could be found by stepwise regression, and had the closest correlation with the dependent, so it had the highest estimating precision. In Table 3, all R2of stepwise regression equations reached 0.05 significant. Independents were screened out by different regression equations of physiological and biochemical indexes, and most regression coefficients reached 0.01 significant. According to relative importance from big to small, characteristic variables of TN and Car were SDr/SDb and hv. Characteristic variables of Tchla were SDr/SDb, hv, hg, and (SDr SDb)/(SDr + SDb). Characteristic variables of Tchlb
Table 1 Correlation analyses between leaf physiological and biochemical indexes and the position variables of hyperspectra TN Tchla Tchlb TCar Chla Chlb Car
Fw DW
MC
k
Dr
b
Db
XY
0.787" 0.850" 0.695" 0.652" 0.888" 0.729"
-0.448" -0.443" -0.343" -0.293" -0.470" -0.362"
0.57 1** 0.627" 0.434" 0.534" 0.652" 0.463"
0.366" 0.398" 0.252" 0.352" 0.425" 0.272"
0.621" 0.289" 0.251" 0.390"
-0.281"
0.478" 0.457" 0.347" 0.473"
-0.740" -0.766" -0.622" -0.578" -0.842" -0.675" -0.592"
-0.182' -0.260" -0.175
-0.258" -0.320" -0.315"
DY -0.250" -0.268" -0.136 -0.276" -0.265" -0.14
0.319" 0.322" 0.209'
-0.198' -0.399" -0.254"
0.325"
-0.377"
;hS -0.691" -0.757" -0.592" -0.649" -0.755" -0.605"
Rg -0.797" -0.827" -0.622" -0.651" -0.87 1'' -0.660"
-0.535" -0.498" -0.330" -0.575"
-0.606" -0.424" -0.368" -0.484"
b
Rr
-0.779" -0.835" -0.641" -0.625" -0.805" -0.635"
-0.546" -0.577" -0.403" -0.491" -0.600" -0.426" -0.437"
-0.486" -0.47 1*' -0.35 I" -0.531"
-0.389" -0.280" -0.428"
"Significant (P < 0.01); 'significant (P < 0.05). The same as below.
Table 2 Correlation analyses between leaf physiological and biochemical indexes and the area variables and vegetation indices of hyperspectra TN Tchla Tchlb
l k Chla Chlb
car Fw DW MC
SDr
SDb
SDV
Re&
(Rfi-Rr)/(Rfi+Rr)
SDrlSDb
SDdSDv
(SDr-SDb)/(SDr+SDb)
0.522"
-0.797"
0.625" 0.655" 0.437" 0.549" 0.673" 0.46 1*' 0.464" 0.495" 0.350" 0.544"
-0.123
0.755"
-0.235"
-0.830" -0.643" -0.640" -0.877" -0.681" -0.601" -0.397" -0.378" -0.452"
0.651" 0.673" 0.449" 0.558" 0.691" 0.475" 0.473" 0.491" 0.347" 0.549"
0.808"
0.570" 0.407" 0.462" 0.648" 0.461" 0.480" 0.286" 0.261"
0.054 0.034 0.1 15
0.867" 0.719" 0.677" 0.928" 0.768" 0.675" 0.290" 0.294" 0.373"
-0.131 -0.09 -0.13 -0.138 -0.098 -0.118 -0.136 -0.125 -0.157
0.804" 0.608" 0.639" 0.865" 0.654" 0.613" 0.411" 0.373" 0.461"
-0.230' -0.262" -0.088 -0.299" -0.303" -0.203' 0.241" 0.023 0.203'
0.333"
-0.049 0.054 0.126 0.026 -0.277" -0.109 -0.259"
(SDr-SDv)/(SDr+SDv)
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and Chlb were SDr/SDb. Characteristic variables of Tcar were SDr/SDb, hg, and (SDr - SDb)/(SDr + SDb). Characteristicvariables of Chla were SDr/SDb, hg, and (SDr - SDy)/(SDr + SDy). Characteristic variables of FW were SDr, SDb, Db, (SDr - SDy)/(SDr + SDy), and Rg/Rr. Characteristic variables of DW were (SDr - SDb)/(SDr + SDb), hr, and Dr. Characteristic variables of MC were hg, SDr, hv, (SDr - SDy)/(SDr + SDy). The order of characteristic variable relative importance was almost consistent with correlation coefficient values, except for FW. Through analysing the results of SPSS, hg was the main characteristic variable of FW when independent numbers were less. With the numbers increasing, the importance of hg reduced, hence the characteristic variables of FW changed. Fourteen random leaves were selected to compute the values of physiological and biochemical indexes by regression models, and to test the difference of mea-
sures and estimated values. The results are shown in Table 4. All the differences of measures and estimated values did not reach 0.05 significant level, which showed that there were no obvious differences between measures and estimated values. Accordingly, the regression of physiological and biochemical indexes had good estimating effect.
DISCUSSION Nitrogen is an essential element to influence and determine flue-cured tobacco growth, quality, and yield. Lack of potassium could degrade the quality of fluecured tobacco leaves. However, too much fertilizer could make tobacco plants grow too big and become black tobacco after curing. At the same time, nitrogen and potassium have strong eluviation to pollute soil and
'IBble 3 Stepwise regression equations of leaf physiological and biochemical indexes by the hyperspectral parameters Dependent
TN Tcbla
Regression equation
j
+
= 77.751 0 . 3 3 9 ~ - ~0.114x2
j=666.928+1.631x1- 0 . 3 1 7 ~- ~0 . 8 0 9 ~-~1 1 . 9 6 8 ~ ~
Independent
Thenameof
Regression
indeDendent
coeffcient mbabilitv
R=
SDrISDb
0.000
0.680"
av
0.801"
SDrISDb
0.005 0.000
b
0.002
119
0.000
(SDr-SDb)l(SDr+SDb)
0.001
Tchlb
= -0.168 + 0 . 5 1 4 ~ ~
SDrlSDb
0.000
0.514"
Tcra
=121.618+0.33~1-0.217~2- 2.898~3
SDrISDb
0.000
0.541"
hg
0.000
(SDr-SDb)l(SDr+SDb)
0.004
SDrISDb
0.000 0.000
Chla
j
+
= 12778.59 4 4 . 1 8 ~ -~2 2 . 5 3 9 ~-~144.334%
lig (SDr-SDy)/(SDr+SDy)
0.830"
0.003
Chlb
j =-27.372 + 2 0 . 3 3 9 ~ ~
SDrlSDb
0.000
0.560"
cat
j=-1581.31+12.736xl +2.368x2
SDrlSDb
0.000
0.445'
av
0.045
SDr
0.000
SDb
0.000
Db
0.000
(SDr-SDy)l(SDr+SDy)
0.000
Rg/Rr (SDr-SDb)l(SDr+SDb)
0.003
k
0.000
Fw
DW
MC
0.000
Dr
0.003
hg SDr
0.000
av
0.000 0.000
(SDr-SDY)/(SDr+SDy)
0.000
0.626"
0.444'
0.548"
Relationshiu Between HvuersDectral Parameters and Physiological and Biochemical Indexes of Flue-Cured Tobacco Leaves
67 1
Table 4 Test for the stepwise regression equations of leaf physiological and biochemical indexes Estimated value-Measured value
TN Tchla Tchlb Tcar Chla Chlb Cin
Fw DW MC
Paired differences Mean
Std. deviation
Std. error mean
df
Sig. (?-tailed)
0.233 0.3159 -0.1177 -0.2074 -9.3895 -1.4006 -4.5325 4.5161 0.4114 -0.0043
0.6953 1.2282 0.9054 0.5129 40.8528 33.0314 23.998 10.7676 1.4811 0.0629
0.1858 0.3282 0.242 0.1371 10.9184 8.828 6.4137 2.9864 0.4108 0.0174
13 13 13 13 13 13 13 12 12 12
0.232 0.353 0.635 0.154 0.405 0.876 0.492 0.156 0.336 0.810
groundwater. Thus, the need is to diagnose tobaccofield fertilizer and obtain tobacco growth information quickly so as to protect agricultural environment and realize sustainabledevelopment. Therefore, hyperspectral detection is a convenient arid effective method. Out of the above purposes, the hyperspectral technique was applied to flue-cured tobacco production in this experiment. It described the spectral characteristics of flue-cured tobacco with special botany properties and analyzed the relationship between hyperspectral variables and physiological and biochemical indexes. Also, the regression models were set up to estimate physiological and biochemical indexes. The results showed that the spectral curves of different nitrogen and potassium treatments had obvious differences, especially in green and near-infrared light range, so the relative height of the spectral curves could reflect the difference of fertilizer quantity. Correlation analysis indicated that spectral characteristics had a close relationship with 10 physiological and biochemical indexes. The R2 and regression coefficients of equations reached 0.05 significant levels, and they reached 0.01 significant levels except for Car and DW. The regression equations had good estimating effects by other sample tests, which also showed that the characteristic variables screened out by these equations were of precision and credibility. Of course, the relationship discussed in this article aimed at all the tobacco leaves, including different growth periods and treatments. The idiographic analysis of each period and treatment was not carried through because of the limit on article length. Furthermore, spectral observations in the room have good effect due to the strict light-house and environment, which can provide reference for tobaccofield observations. However, it is necessary to per-
form field observations and apply altitude remote sensing to realize site-specific inspection at the correct time in the future.
CONCLUSION Through the observation of flue-cured tobacco leaf hyperspectral reflectance in the room, primary cognition has been obtained regarding the spectral characteristics in different N, K treatments. The spectral characteristic variables screened by correlation and regression have briefness, definitude, and strong maneuverability. SDdSDb is the closest spectral characteristic variable with TN, Tchla, Tchlb, Tcar, Chla, Chalb, and Car. SDr is the closest spectral characteristic variable with FW. (SDr - SDb)/(SDr + SDb) is the closest spectral characteristic variable with DW. hg is the closest spectral characteristic variable with MC. The regression equations can estimate 10 physiological and biochemical indexes quickly and accurately. The testing effects are also good. Therefore, our research indicates that hyperspectral technique can provide new and operable methods to realize a quick measurement of physiological and biochemical components and precision agriculture.
Acknowledgements The study was supported by the Key Program of National Tobacco Monoply Bureau of China (1 10200401021).
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