Abstract

Successful classifications of reflectance and vibrational data are to a large extent dependent upon robustness of input data. In this study, a well-known geostatistical approach, variogram analysis, was described and its robustness was assessed through comprehensive evaluation of 3,200 variogram settings. High-resolution hyperspectral imaging data were acquired from greenhouse maize plants, and the robustness (radiometric repeatability) of three variogram parameters (nugget, sill, and range) was examined when generated from imaging data collected from two different sets of plants and with imaging data collected on seven different days in two years. Robustness of variogram parameters was compared with average reflectance values in six spectral bands, three standard vegetation indices (NDVI, SI, and PRI), and PCA scores from principal component analysis.

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    [CrossRef]
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2010

C. Nansen, N. Abidi, A. J. Sidumo, and A. H. Gharalari, “Using spatial structure analysis of hyperspectral imaging data and fourier transformed infrared analysis to determine bioactivity of surface pesticide treatment,” Remote Sens. 2(4), 908–925 (2010).
[CrossRef]

C. Nansen, A. J. Sidumo, and S. Capareda, “Variogram analysis of hyperspectral data to characterize the impact of biotic and abiotic stress of maize plants and to estimate biofuel potential,” Appl. Spectrosc. 64(6), 627–636 (2010).
[CrossRef] [PubMed]

2009

C. Nansen, T. Macedo, R. Swanson, and D. K. Weaver, “Use of spatial structure analysis of hyperspectral data cubes for detection of insect-induced stress in wheat plants,” Int. J. Remote Sens. 30(10), 2447–2464 (2009).
[CrossRef]

J. M. Amigo and C. Ravn; “Direct quantification and distribution assessment of major and minor components in pharmaceutical tablets by NIR-chemical imaging,” Eur. J. Pharm. Sci. 37(2), 76–82 (2009).
[CrossRef] [PubMed]

2008

C. Ravn, E. Skibsted, and R. Bro, “Near-infrared chemical imaging (NIR-CI) on pharmaceutical solid dosage forms-comparing common calibration approaches,” J. Pharm. Biomed. Anal. 48(3), 554–561 (2008).
[CrossRef] [PubMed]

C. Nansen, M. Kolomiets, and X. Gao, “Considerations regarding the use of hyperspectral imaging data in classifications of food products, exemplified by analysis of maize kernels,” J. Agric. Food Chem. 56(9), 2933–2938 (2008).
[CrossRef] [PubMed]

2007

H. Ma and C. A. Anderson, “Optimisation of magnification levels for near infrared chemical imaging of blending of pharmaceutical powders,” J. Near Infrared Spectrosc. 15(2), 137–151 (2007).
[CrossRef]

2006

A. M. Lefcout and M. S. Kim, “Technique for normalizing intensity histograms of images when the approximate size of the target is known: Detection of feces on apples using fluorescence imaging,” Comp. Elec. Agricult. 50(2), 135–147 (2006).
[CrossRef]

B. Park, K. C. Lawrence, R. W. Windham, and D. P. Smith, “Performance of hyperspectral imaging system for poultry surface fecal contaminant detection,” J. Food Eng. 75(3), 340–348 (2006).
[CrossRef]

M. Baghzouz, D. A. Devitt, and R. L. Morris, “Evaluating temporal variability in the spectral reflectance response of annual ryegrass to changes in nitrogen applications and leaching fractions,” Int. J. Remote Sens. 27(19), 4137–4157 (2006).
[CrossRef]

2005

K. Peleg, G. L. Anderson, and C. Yang, “Repeatability of hyperspectral imaging systems – quantification and improvement,” Int. J. Remote Sens. 26(1), 115–139 (2005).
[CrossRef]

A. M. Vargas, M. S. Kim, T. Yang, A. M. Lefcourt, Y.-R. Chen, Y. Luo, Y. Song, and R. Buchanan, “Detection of fecal contamination on cantaloupes using hyperspectral fluorescence imagery,” J. Food Sci. 70(8), 471–476 (2005).
[CrossRef]

2004

R. Cogdill, C. Hurburgh, and G. Rippke, “Single-kernel maize analysis by near-infrared hyperspectral imaging,” Trans. ASAE 47, 311–320 (2004).

P. M. Mehl, Y. Chen, M. S. Kim, and D. E. Chan, “Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations,” J. Food Eng. 61(1), 67–81 (2004).
[CrossRef]

S. G. Kong, Y. R. Chen, I. Kim, and M. S. Kim, “Analysis of hyperspectral fluorescence images for poultry skin tumor inspection,” Appl. Opt. 43(4), 824–833 (2004).
[CrossRef] [PubMed]

2001

G. A. Carter and A. K. Knapp, “Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration,” Am. J. Bot. 88(4), 677–684 (2001).
[CrossRef] [PubMed]

1999

D. I. Givens and E. R. Deaville, “The current and future role of near infrared reflectance spectroscopy in animal nutrition,” Aust. J. Agric. Res. 50(7), 1131–1145 (1999).
[CrossRef]

1998

S. R. Delwiche, “Protein content of single kernels of wheat by near-infrared reflectance spectroscopy,” J. Cereal Sci. 27(3), 241–254 (1998).
[CrossRef]

1996

A. Masoni, L. Ercoli, and M. Mariotti, “Spectral properties of leaves deficient in iron, sulfur, magnesium, and manganese,” Agron. J. 88(6), 937–943 (1996).
[CrossRef]

Abidi, N.

C. Nansen, N. Abidi, A. J. Sidumo, and A. H. Gharalari, “Using spatial structure analysis of hyperspectral imaging data and fourier transformed infrared analysis to determine bioactivity of surface pesticide treatment,” Remote Sens. 2(4), 908–925 (2010).
[CrossRef]

Amigo, J. M.

J. M. Amigo and C. Ravn; “Direct quantification and distribution assessment of major and minor components in pharmaceutical tablets by NIR-chemical imaging,” Eur. J. Pharm. Sci. 37(2), 76–82 (2009).
[CrossRef] [PubMed]

Anderson, C. A.

H. Ma and C. A. Anderson, “Optimisation of magnification levels for near infrared chemical imaging of blending of pharmaceutical powders,” J. Near Infrared Spectrosc. 15(2), 137–151 (2007).
[CrossRef]

Anderson, G. L.

K. Peleg, G. L. Anderson, and C. Yang, “Repeatability of hyperspectral imaging systems – quantification and improvement,” Int. J. Remote Sens. 26(1), 115–139 (2005).
[CrossRef]

Baghzouz, M.

M. Baghzouz, D. A. Devitt, and R. L. Morris, “Evaluating temporal variability in the spectral reflectance response of annual ryegrass to changes in nitrogen applications and leaching fractions,” Int. J. Remote Sens. 27(19), 4137–4157 (2006).
[CrossRef]

Bro, R.

C. Ravn, E. Skibsted, and R. Bro, “Near-infrared chemical imaging (NIR-CI) on pharmaceutical solid dosage forms-comparing common calibration approaches,” J. Pharm. Biomed. Anal. 48(3), 554–561 (2008).
[CrossRef] [PubMed]

Buchanan, R.

A. M. Vargas, M. S. Kim, T. Yang, A. M. Lefcourt, Y.-R. Chen, Y. Luo, Y. Song, and R. Buchanan, “Detection of fecal contamination on cantaloupes using hyperspectral fluorescence imagery,” J. Food Sci. 70(8), 471–476 (2005).
[CrossRef]

Capareda, S.

Carter, G. A.

G. A. Carter and A. K. Knapp, “Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration,” Am. J. Bot. 88(4), 677–684 (2001).
[CrossRef] [PubMed]

Chan, D. E.

P. M. Mehl, Y. Chen, M. S. Kim, and D. E. Chan, “Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations,” J. Food Eng. 61(1), 67–81 (2004).
[CrossRef]

Chen, Y.

P. M. Mehl, Y. Chen, M. S. Kim, and D. E. Chan, “Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations,” J. Food Eng. 61(1), 67–81 (2004).
[CrossRef]

Chen, Y. R.

Chen, Y.-R.

A. M. Vargas, M. S. Kim, T. Yang, A. M. Lefcourt, Y.-R. Chen, Y. Luo, Y. Song, and R. Buchanan, “Detection of fecal contamination on cantaloupes using hyperspectral fluorescence imagery,” J. Food Sci. 70(8), 471–476 (2005).
[CrossRef]

Cogdill, R.

R. Cogdill, C. Hurburgh, and G. Rippke, “Single-kernel maize analysis by near-infrared hyperspectral imaging,” Trans. ASAE 47, 311–320 (2004).

Deaville, E. R.

D. I. Givens and E. R. Deaville, “The current and future role of near infrared reflectance spectroscopy in animal nutrition,” Aust. J. Agric. Res. 50(7), 1131–1145 (1999).
[CrossRef]

Delwiche, S. R.

S. R. Delwiche, “Protein content of single kernels of wheat by near-infrared reflectance spectroscopy,” J. Cereal Sci. 27(3), 241–254 (1998).
[CrossRef]

Devitt, D. A.

M. Baghzouz, D. A. Devitt, and R. L. Morris, “Evaluating temporal variability in the spectral reflectance response of annual ryegrass to changes in nitrogen applications and leaching fractions,” Int. J. Remote Sens. 27(19), 4137–4157 (2006).
[CrossRef]

Ercoli, L.

A. Masoni, L. Ercoli, and M. Mariotti, “Spectral properties of leaves deficient in iron, sulfur, magnesium, and manganese,” Agron. J. 88(6), 937–943 (1996).
[CrossRef]

Gao, X.

C. Nansen, M. Kolomiets, and X. Gao, “Considerations regarding the use of hyperspectral imaging data in classifications of food products, exemplified by analysis of maize kernels,” J. Agric. Food Chem. 56(9), 2933–2938 (2008).
[CrossRef] [PubMed]

Gharalari, A. H.

C. Nansen, N. Abidi, A. J. Sidumo, and A. H. Gharalari, “Using spatial structure analysis of hyperspectral imaging data and fourier transformed infrared analysis to determine bioactivity of surface pesticide treatment,” Remote Sens. 2(4), 908–925 (2010).
[CrossRef]

Givens, D. I.

D. I. Givens and E. R. Deaville, “The current and future role of near infrared reflectance spectroscopy in animal nutrition,” Aust. J. Agric. Res. 50(7), 1131–1145 (1999).
[CrossRef]

Hurburgh, C.

R. Cogdill, C. Hurburgh, and G. Rippke, “Single-kernel maize analysis by near-infrared hyperspectral imaging,” Trans. ASAE 47, 311–320 (2004).

Kim, I.

Kim, M. S.

A. M. Lefcout and M. S. Kim, “Technique for normalizing intensity histograms of images when the approximate size of the target is known: Detection of feces on apples using fluorescence imaging,” Comp. Elec. Agricult. 50(2), 135–147 (2006).
[CrossRef]

A. M. Vargas, M. S. Kim, T. Yang, A. M. Lefcourt, Y.-R. Chen, Y. Luo, Y. Song, and R. Buchanan, “Detection of fecal contamination on cantaloupes using hyperspectral fluorescence imagery,” J. Food Sci. 70(8), 471–476 (2005).
[CrossRef]

S. G. Kong, Y. R. Chen, I. Kim, and M. S. Kim, “Analysis of hyperspectral fluorescence images for poultry skin tumor inspection,” Appl. Opt. 43(4), 824–833 (2004).
[CrossRef] [PubMed]

P. M. Mehl, Y. Chen, M. S. Kim, and D. E. Chan, “Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations,” J. Food Eng. 61(1), 67–81 (2004).
[CrossRef]

Knapp, A. K.

G. A. Carter and A. K. Knapp, “Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration,” Am. J. Bot. 88(4), 677–684 (2001).
[CrossRef] [PubMed]

Kolomiets, M.

C. Nansen, M. Kolomiets, and X. Gao, “Considerations regarding the use of hyperspectral imaging data in classifications of food products, exemplified by analysis of maize kernels,” J. Agric. Food Chem. 56(9), 2933–2938 (2008).
[CrossRef] [PubMed]

Kong, S. G.

Lawrence, K. C.

B. Park, K. C. Lawrence, R. W. Windham, and D. P. Smith, “Performance of hyperspectral imaging system for poultry surface fecal contaminant detection,” J. Food Eng. 75(3), 340–348 (2006).
[CrossRef]

Lefcourt, A. M.

A. M. Vargas, M. S. Kim, T. Yang, A. M. Lefcourt, Y.-R. Chen, Y. Luo, Y. Song, and R. Buchanan, “Detection of fecal contamination on cantaloupes using hyperspectral fluorescence imagery,” J. Food Sci. 70(8), 471–476 (2005).
[CrossRef]

Lefcout, A. M.

A. M. Lefcout and M. S. Kim, “Technique for normalizing intensity histograms of images when the approximate size of the target is known: Detection of feces on apples using fluorescence imaging,” Comp. Elec. Agricult. 50(2), 135–147 (2006).
[CrossRef]

Luo, Y.

A. M. Vargas, M. S. Kim, T. Yang, A. M. Lefcourt, Y.-R. Chen, Y. Luo, Y. Song, and R. Buchanan, “Detection of fecal contamination on cantaloupes using hyperspectral fluorescence imagery,” J. Food Sci. 70(8), 471–476 (2005).
[CrossRef]

Ma, H.

H. Ma and C. A. Anderson, “Optimisation of magnification levels for near infrared chemical imaging of blending of pharmaceutical powders,” J. Near Infrared Spectrosc. 15(2), 137–151 (2007).
[CrossRef]

Macedo, T.

C. Nansen, T. Macedo, R. Swanson, and D. K. Weaver, “Use of spatial structure analysis of hyperspectral data cubes for detection of insect-induced stress in wheat plants,” Int. J. Remote Sens. 30(10), 2447–2464 (2009).
[CrossRef]

Mariotti, M.

A. Masoni, L. Ercoli, and M. Mariotti, “Spectral properties of leaves deficient in iron, sulfur, magnesium, and manganese,” Agron. J. 88(6), 937–943 (1996).
[CrossRef]

Masoni, A.

A. Masoni, L. Ercoli, and M. Mariotti, “Spectral properties of leaves deficient in iron, sulfur, magnesium, and manganese,” Agron. J. 88(6), 937–943 (1996).
[CrossRef]

Mehl, P. M.

P. M. Mehl, Y. Chen, M. S. Kim, and D. E. Chan, “Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations,” J. Food Eng. 61(1), 67–81 (2004).
[CrossRef]

Morris, R. L.

M. Baghzouz, D. A. Devitt, and R. L. Morris, “Evaluating temporal variability in the spectral reflectance response of annual ryegrass to changes in nitrogen applications and leaching fractions,” Int. J. Remote Sens. 27(19), 4137–4157 (2006).
[CrossRef]

Nansen, C.

C. Nansen, A. J. Sidumo, and S. Capareda, “Variogram analysis of hyperspectral data to characterize the impact of biotic and abiotic stress of maize plants and to estimate biofuel potential,” Appl. Spectrosc. 64(6), 627–636 (2010).
[CrossRef] [PubMed]

C. Nansen, N. Abidi, A. J. Sidumo, and A. H. Gharalari, “Using spatial structure analysis of hyperspectral imaging data and fourier transformed infrared analysis to determine bioactivity of surface pesticide treatment,” Remote Sens. 2(4), 908–925 (2010).
[CrossRef]

C. Nansen, T. Macedo, R. Swanson, and D. K. Weaver, “Use of spatial structure analysis of hyperspectral data cubes for detection of insect-induced stress in wheat plants,” Int. J. Remote Sens. 30(10), 2447–2464 (2009).
[CrossRef]

C. Nansen, M. Kolomiets, and X. Gao, “Considerations regarding the use of hyperspectral imaging data in classifications of food products, exemplified by analysis of maize kernels,” J. Agric. Food Chem. 56(9), 2933–2938 (2008).
[CrossRef] [PubMed]

Park, B.

B. Park, K. C. Lawrence, R. W. Windham, and D. P. Smith, “Performance of hyperspectral imaging system for poultry surface fecal contaminant detection,” J. Food Eng. 75(3), 340–348 (2006).
[CrossRef]

Peleg, K.

K. Peleg, G. L. Anderson, and C. Yang, “Repeatability of hyperspectral imaging systems – quantification and improvement,” Int. J. Remote Sens. 26(1), 115–139 (2005).
[CrossRef]

Ravn, C.

J. M. Amigo and C. Ravn; “Direct quantification and distribution assessment of major and minor components in pharmaceutical tablets by NIR-chemical imaging,” Eur. J. Pharm. Sci. 37(2), 76–82 (2009).
[CrossRef] [PubMed]

C. Ravn, E. Skibsted, and R. Bro, “Near-infrared chemical imaging (NIR-CI) on pharmaceutical solid dosage forms-comparing common calibration approaches,” J. Pharm. Biomed. Anal. 48(3), 554–561 (2008).
[CrossRef] [PubMed]

Rippke, G.

R. Cogdill, C. Hurburgh, and G. Rippke, “Single-kernel maize analysis by near-infrared hyperspectral imaging,” Trans. ASAE 47, 311–320 (2004).

Sidumo, A. J.

C. Nansen, N. Abidi, A. J. Sidumo, and A. H. Gharalari, “Using spatial structure analysis of hyperspectral imaging data and fourier transformed infrared analysis to determine bioactivity of surface pesticide treatment,” Remote Sens. 2(4), 908–925 (2010).
[CrossRef]

C. Nansen, A. J. Sidumo, and S. Capareda, “Variogram analysis of hyperspectral data to characterize the impact of biotic and abiotic stress of maize plants and to estimate biofuel potential,” Appl. Spectrosc. 64(6), 627–636 (2010).
[CrossRef] [PubMed]

Skibsted, E.

C. Ravn, E. Skibsted, and R. Bro, “Near-infrared chemical imaging (NIR-CI) on pharmaceutical solid dosage forms-comparing common calibration approaches,” J. Pharm. Biomed. Anal. 48(3), 554–561 (2008).
[CrossRef] [PubMed]

Smith, D. P.

B. Park, K. C. Lawrence, R. W. Windham, and D. P. Smith, “Performance of hyperspectral imaging system for poultry surface fecal contaminant detection,” J. Food Eng. 75(3), 340–348 (2006).
[CrossRef]

Song, Y.

A. M. Vargas, M. S. Kim, T. Yang, A. M. Lefcourt, Y.-R. Chen, Y. Luo, Y. Song, and R. Buchanan, “Detection of fecal contamination on cantaloupes using hyperspectral fluorescence imagery,” J. Food Sci. 70(8), 471–476 (2005).
[CrossRef]

Swanson, R.

C. Nansen, T. Macedo, R. Swanson, and D. K. Weaver, “Use of spatial structure analysis of hyperspectral data cubes for detection of insect-induced stress in wheat plants,” Int. J. Remote Sens. 30(10), 2447–2464 (2009).
[CrossRef]

Vargas, A. M.

A. M. Vargas, M. S. Kim, T. Yang, A. M. Lefcourt, Y.-R. Chen, Y. Luo, Y. Song, and R. Buchanan, “Detection of fecal contamination on cantaloupes using hyperspectral fluorescence imagery,” J. Food Sci. 70(8), 471–476 (2005).
[CrossRef]

Weaver, D. K.

C. Nansen, T. Macedo, R. Swanson, and D. K. Weaver, “Use of spatial structure analysis of hyperspectral data cubes for detection of insect-induced stress in wheat plants,” Int. J. Remote Sens. 30(10), 2447–2464 (2009).
[CrossRef]

Windham, R. W.

B. Park, K. C. Lawrence, R. W. Windham, and D. P. Smith, “Performance of hyperspectral imaging system for poultry surface fecal contaminant detection,” J. Food Eng. 75(3), 340–348 (2006).
[CrossRef]

Yang, C.

K. Peleg, G. L. Anderson, and C. Yang, “Repeatability of hyperspectral imaging systems – quantification and improvement,” Int. J. Remote Sens. 26(1), 115–139 (2005).
[CrossRef]

Yang, T.

A. M. Vargas, M. S. Kim, T. Yang, A. M. Lefcourt, Y.-R. Chen, Y. Luo, Y. Song, and R. Buchanan, “Detection of fecal contamination on cantaloupes using hyperspectral fluorescence imagery,” J. Food Sci. 70(8), 471–476 (2005).
[CrossRef]

Agron. J.

A. Masoni, L. Ercoli, and M. Mariotti, “Spectral properties of leaves deficient in iron, sulfur, magnesium, and manganese,” Agron. J. 88(6), 937–943 (1996).
[CrossRef]

Am. J. Bot.

G. A. Carter and A. K. Knapp, “Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration,” Am. J. Bot. 88(4), 677–684 (2001).
[CrossRef] [PubMed]

Appl. Opt.

Appl. Spectrosc.

Aust. J. Agric. Res.

D. I. Givens and E. R. Deaville, “The current and future role of near infrared reflectance spectroscopy in animal nutrition,” Aust. J. Agric. Res. 50(7), 1131–1145 (1999).
[CrossRef]

Comp. Elec. Agricult.

A. M. Lefcout and M. S. Kim, “Technique for normalizing intensity histograms of images when the approximate size of the target is known: Detection of feces on apples using fluorescence imaging,” Comp. Elec. Agricult. 50(2), 135–147 (2006).
[CrossRef]

Eur. J. Pharm. Sci.

J. M. Amigo and C. Ravn; “Direct quantification and distribution assessment of major and minor components in pharmaceutical tablets by NIR-chemical imaging,” Eur. J. Pharm. Sci. 37(2), 76–82 (2009).
[CrossRef] [PubMed]

Int. J. Remote Sens.

K. Peleg, G. L. Anderson, and C. Yang, “Repeatability of hyperspectral imaging systems – quantification and improvement,” Int. J. Remote Sens. 26(1), 115–139 (2005).
[CrossRef]

M. Baghzouz, D. A. Devitt, and R. L. Morris, “Evaluating temporal variability in the spectral reflectance response of annual ryegrass to changes in nitrogen applications and leaching fractions,” Int. J. Remote Sens. 27(19), 4137–4157 (2006).
[CrossRef]

C. Nansen, T. Macedo, R. Swanson, and D. K. Weaver, “Use of spatial structure analysis of hyperspectral data cubes for detection of insect-induced stress in wheat plants,” Int. J. Remote Sens. 30(10), 2447–2464 (2009).
[CrossRef]

J. Agric. Food Chem.

C. Nansen, M. Kolomiets, and X. Gao, “Considerations regarding the use of hyperspectral imaging data in classifications of food products, exemplified by analysis of maize kernels,” J. Agric. Food Chem. 56(9), 2933–2938 (2008).
[CrossRef] [PubMed]

J. Cereal Sci.

S. R. Delwiche, “Protein content of single kernels of wheat by near-infrared reflectance spectroscopy,” J. Cereal Sci. 27(3), 241–254 (1998).
[CrossRef]

J. Food Eng.

B. Park, K. C. Lawrence, R. W. Windham, and D. P. Smith, “Performance of hyperspectral imaging system for poultry surface fecal contaminant detection,” J. Food Eng. 75(3), 340–348 (2006).
[CrossRef]

P. M. Mehl, Y. Chen, M. S. Kim, and D. E. Chan, “Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations,” J. Food Eng. 61(1), 67–81 (2004).
[CrossRef]

J. Food Sci.

A. M. Vargas, M. S. Kim, T. Yang, A. M. Lefcourt, Y.-R. Chen, Y. Luo, Y. Song, and R. Buchanan, “Detection of fecal contamination on cantaloupes using hyperspectral fluorescence imagery,” J. Food Sci. 70(8), 471–476 (2005).
[CrossRef]

J. Near Infrared Spectrosc.

H. Ma and C. A. Anderson, “Optimisation of magnification levels for near infrared chemical imaging of blending of pharmaceutical powders,” J. Near Infrared Spectrosc. 15(2), 137–151 (2007).
[CrossRef]

J. Pharm. Biomed. Anal.

C. Ravn, E. Skibsted, and R. Bro, “Near-infrared chemical imaging (NIR-CI) on pharmaceutical solid dosage forms-comparing common calibration approaches,” J. Pharm. Biomed. Anal. 48(3), 554–561 (2008).
[CrossRef] [PubMed]

Remote Sens.

C. Nansen, N. Abidi, A. J. Sidumo, and A. H. Gharalari, “Using spatial structure analysis of hyperspectral imaging data and fourier transformed infrared analysis to determine bioactivity of surface pesticide treatment,” Remote Sens. 2(4), 908–925 (2010).
[CrossRef]

Trans. ASAE

R. Cogdill, C. Hurburgh, and G. Rippke, “Single-kernel maize analysis by near-infrared hyperspectral imaging,” Trans. ASAE 47, 311–320 (2004).

Other

M. Armstrong, Basics of semi-variogram Analysis (Springer 1998).

E. H. Isaaks and R. M. Srivastava, Applied Geostatistics (Oxford University Press, New York 1989).

S. A. Krajewski and B. L. Gibbs, “Understanding contouring. A practical guide to spatial estimation using a computer and semi-variogram interpretation,” Gibbs Associates (2001).

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Figures (6)

Fig. 1
Fig. 1

Reflectance data acquisition from maize individual leaves.

Fig. 2
Fig. 2

Average variogram parameters [nugget (a), sill (b), and range (c)] obtained from the most robust variogram analysis (spherical regression to semi-variance data with lag distance = 2 and 15 lag distance intervals) for all combinations of date of data collection and maize hybrid.

Fig. 3
Fig. 3

: Average reflectance values in six spectral bands [532 nm (a), 570 nm (b), 693 nm (c), 706 nm (d), 759 nm (e), 786 nm (f)] for all combinations of date of data collection and maize hybrid.

Fig. 4
Fig. 4

: Average vegetation indices [Normalized Difference Vegetation Index (a), NDVI = (R750 - R705) / (R750 + R705), Stress index (b), SI = (R693 / R759), and Photochemical reflectance index (c), PRI = (R531 – R570) / (R531 – R570)] for all combinations of date of data collection and maize hybrid.

Fig. 5
Fig. 5

Principal component analyses (PCA) were conducted individually with all 40 hyperspectral images, and PCA scores for the first three components [PCA1 (a), PCA2 (b), and PCA3 (c)] were calculated for each of the 160 spectral bands. These PCA scores were subsequently analyzed for treatment effects [year (difference between the two data sets), days within year, or hybrids]. Vertical lines represent parts of the examined spectrum with no significant treatment effect (P>0.05). Horizontal dotted lines denote spectral ranges with high level of robustness (all three treatment effects being non-significant).

Fig. 6
Fig. 6

Average range values at 706 nm obtained from the most robust variogram analysis (spherical regression to semi-variance data with lag distance = 2 and 15 lag distance intervals) and based on input data sets ranging from 1,000 – 10,000 data points.

Tables (2)

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Table 1 Robustness of variogram parameters and vegetation indices

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Table 2 Robustness of six spectral bands

Equations (2)

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F ( v ) = a + b ( 1 e ( 3 × D c ) )
F ( v ) = a + b ( 3 D 2 c D 3 2 c 3 )

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