Evaluation of water-use efficiency in foxtail millet (Setaria italica) using visible-near infrared and thermal spectral sensing techniques

Talanta

Volumn 152, Issue , 2016, Pages 531-539

  Wang, Meng ^a   Ellsworth, Patrick Z ^a   Zhou, Jianfeng ^a   Cousins, Asaph B ^a   Sankaran, Sindhuja ^a  

^a WASHINGTON STATE UNIVERSITY   (United States)

Author keywords

Drought; High throughput sensing; Leaf temperature; Partial least square regression; Visible near infrared spectroscopy

Indexed keywords

DROUGHT; EFFICIENCY; INFRARED IMAGING; ISOTOPES; LEAST SQUARES APPROXIMATIONS; NEAR INFRARED SPECTROSCOPY; PATIENT MONITORING; PHYSIOLOGICAL MODELS; PHYSIOLOGY; PLANTS (BOTANY); PRINCIPAL COMPONENT ANALYSIS; REFLECTION; THROUGHPUT;

COEFFICIENT OF DETERMINATION; HIGH THROUGHPUT; LEAF TEMPERATURE; PARTIAL LEAST SQUARE REGRESSION; PARTIAL LEAST SQUARES REGRESSIONS (PLSR); PHYSIOLOGICAL MONITORING; PHYSIOLOGICAL PARAMETERS; VISIBLE-NEAR INFRARED SPECTROSCOPY;

INFRARED DEVICES;

WATER;

METABOLISM; NEAR INFRARED SPECTROSCOPY; PROCEDURES; SETARIA (PLANT); STATISTICAL MODEL; TEMPERATURE;

MODELS, STATISTICAL; SETARIA PLANT; SPECTROSCOPY, NEAR-INFRARED; TEMPERATURE; WATER;

EID: 84959330673     PISSN: 00399140     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.talanta.2016.01.062     Document Type: Article

References (65)

1. J.S. Boyer Plant productivity and environment Science 218 1982 443 448
2. L. Cattivelli, F. Rizza, F. Badeck, E. Mazzucotelli, A.M. Mastrangelo, E. Francia, C. Mare, A. Tondelli, and A.M. Stanca Drought tolerance improvement in crop plants: an integrated view from breeding to genomics Field Crops Res. 105 2008 1 14
3. Z. Chen, J. Tang, and F. Zhang Advances and developing tendency of water use efficiency in plant biology Agric. Sci. China 8 2009 855 863
4. T.A. Howell Enhancing water use efficiency in irrigated agriculture Agron. J. 93 2001 281 289
5. J. Laraus The problems of sustainable water use in the Mediterranean and research requirements for agriculture Ann. Appl. Biol. 144 2004 259 272
6. R.T. Furbank Plant phenomics: from gene to form and function Funct. Plant Biol. 36 2009 5 6
7. L. Cabrera-Bosquet, J. Crossa, J. von Zitzewitz, M.D. Serret, and J. Luis Araus High throughput phenotyping and genomic selection: the frontiers of crop breeding converge J. Integr. Plant Biol. 54 2012 312 320
8. J.L. Araus, A. Elazab, O. Vergara, L. Cabrera-Bosquet, M.D. Serret, M. Zaman-Allah, and J.E. Cairns New Technologies for Phenotyping, Anonymous Phenomics 2015 Springer International Publishing Switzerland 1 14
9. G. Romano, S. Zia, W. Spreer, C. Sanchez, J. Cairns, J.L. Araus, and J. Müller Use of thermography for high throughput phenotyping of tropical maize adaptation in water stress Comput. Electron. Agric. 79 2011 67 74
10. F. Fiorani, and U. Schurr Future scenarios for plant phenotyping Ann. Rev. Plant Biol. 64 2013 267 291
11. R.T. Furbank, and M. Tester Phenomics-technologies to relieve the phenotyping bottleneck Trends Plant Sci. 16 2011 635 644
12. A. Condon, R. Richards, and G. Farquhar Carbon isotope discrimination is positively correlated with grain yield and dry matter production in field-grown wheat Crop Sci. 27 1987 996 1001
13. R. Fischer, D. Rees, K. Sayre, Z. Lu, A. Condon, and A.L. Saavedra Wheat yield progress associated with higher stomatal conductance and photosynthetic rate, and cooler canopies Crop Sci. 38 1998 1467 1475
14. 4 species Sorghum bicolor in the glasshouse and the field Funct. Plant Biol. 25 1998 111 123
15. L.B. Flanagan, and G.D. Farquhar Variation in the carbon and oxygen isotope composition of plant biomass and its relationship to water use efficiency at the leaf and ecosystem scales in a northern Great Plains grassland Plant Cell Environ. 37 2014 425 438
16. Y. Xu, D. This, R.C. Pausch, W.M. Vonhof, J.R. Coburn, J.P. Comstock, and S.R. McCouch Leaf-level water use efficiency determined by carbon isotope discrimination in rice seedlings: genetic variation associated with population structure and QTL mapping Theor. Appl. Genet. 118 2009 1065 1081
17. P. Monneveux, M.S. Sheshshayee, J. Akhter, and J. Ribaut Using carbon isotope discrimination to select maize (Zea mays L.) inbred lines and hybrids for drought tolerance Plant Sci. 173 2007 390 396
18. H.G. Jones Use of thermography for quantitative studies of spatial and temporal variation of stomatal conductance over leaf surfaces Plant Cell Environ. 22 9 1999 1043 1055
19. M. Shibayama, W. Takahashi, S. Morinaga, and T. Akiyama Canopy water deficit detection in paddy rice using a high resolution field spectroradiometer Remote Sens. Environ. 45 2 1993 117 126
20. J. Eitel, P.E. Gesseler, A. Smith, and R. Robberecht Suitability of existing and novel spectral indices to remote detect water stress in Populus spp For. Ecol. Manage. 220 2006 170 182
21. Q. Zhang, Q. Li, and G. Zhang Rapid determination of leaf water content using VIS/NIR spectroscopy analysis with wavelength selection Spectroscopy 27 2 2012 93 105
22. K. Johnson, S. Sankaran, and R. Ehsani Identification of water stress in citrus leaves using sensing technologies Agronomy 3 4 2013 747 756
23. J. Peñuelas, and I. Filella Visible and near-infrared reflectance techniques for diagnosing plant physiological status Trends Plant Sci. 3 4 1998 151 156
24. 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 2001 677 684
25. H.W. Gausman, W.A. Allen, R. Cardenas, and A.J. Richardson Relation of light reflectance to histological and physical evaluations of cotton leaf maturity Appl Optics 9 3 1970 545 552
26. T. Rapaport, U. Hochberg, M. Shoshany, A. Karnieli, and S. Rachmilevitch Combining leaf physiology, hyperspectral imaging and partial least squares-regression (PLS-R) for grapevine water status assessment ISPRS J Photogramm. 109 2015 88 97
27. M. Govender, P.J. Dye, I.M. Weiersbye, E.T. Witkowski, and F. Ahmed Review of commonly used remote sensing and ground-based technologies to measure plant water stress Water SA 35 5 2009 741 752
28. D. Clark, D. Johnson, K. Kephart, and N. Jackson Near infrared reflectance spectroscopy estimation of 13C discrimination in forages J. Range Manage. 48 1995 132 136
29. G.A. Lobos, I. Matus, A. Rodriguez, S. Romero-Bravo, J.L. Araus, and A. del Pozo Wheat genotypic variability in grain yield and carbon isotope discrimination under Mediterranean conditions assessed by spectral reflectance J. Integr. Plant Biol. 56 2014 470 479
30. 4 photosynthesis Plant Cell 22 2010 2537 2544
31. P. Li, and T.P. Brutnell Setaria viridis and Setaria italica, model genetic systems for the Panicoid grasses J. Exp. Bot. 62 2011 3031 3037
32. A.N. Doust, E.A. Kellogg, K.M. Devos, and J.L. Bennetzen Foxtail millet: a sequence-driven grass model system Plant Physiol. 149 2009 137 141
33. J.L. Bennetzen, J. Schmutz, H. Wang, R. Percifield, J. Hawkins, A.C. Pontaroli, M. Estep, L. Feng, J.N. Vaughn, and J. Grimwood Reference genome sequence of the model plant Setaria Nat. Biotechnol. 30 2012 555 561
34. 4 grasses Setaria viridis and Setaria italica Plant Cell Environ. 2015 (Submitted for publication)
35. S. Araki, H. Nomura, and N. Wakami Segmentation of thermal images using the fuzzy C-means algorithm Fuzzy Sys. 2 1993 719 724
36. S. Sankaran, A. Mishra, J.M. Maja, and R. Ehsani Visible-near infrared spectroscopy for detection of Huanglongbing in citrus orchards Comput. Electron. Agric. 77 2 2011 127 134
37. R.D. Tobias, An introduction to partial least squares regression, in: Proc. Ann. SAS Users Group Int. Conf., 20th, Orlando, FL, 1995, pp. 2-5.
38. H. Abdi, Partial least square regression (PLS regression), Encyclopedia for Research Methods for the Social Sciences, 2003, pp. 792-795.
39. J.A. Gamon, and J. Penuelas C.B. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency Field Remote Sens. Environ. 41 1 1992 35 44
40. J.A. Gamon, L. Serrano, and J.S. Surfus The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels Oecologia 112 4 1997 492 501
41. B. Gao NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space Remote Sens. Environ. 58 1996 257 266
42. J. Peñuelas, I. Filella, C. Biel, L. Serrano, and R. Save The reflectance at the 950-970 nm region as an indicator of plant water status Int. J. Remote Sens. 14 10 1993 1887 1905
43. R. Core Team, R: A Language and Environment for Statistical Computing, 2013.
44. R.R. Sokal, and F.J. Rohlf Biometry: The Principles and Practice of Statistics in Biological Research third ed. 1995 W. H. Freeman and Co. New York
45. O.M. Grant, M.M. Chaves, and H.G. Jones Optimizing thermal imaging as a technique for detecting stomatal closure induced by drought stress under greenhouse conditions. Physiol Physiol. Plant. 127 2006 507 518
46. J.E. Pallas, B.E. Michel, and D.G. Harris Photosynthesis, transpiration, leaf temperature, and stomatal activity of cotton plants under varying water potentials Plant Physiol. 42 1 1967 76 88
47. M. Reynolds, S. Rajaram, and K. Sayre Physiological and genetic changes of irrigated wheat in the post-green revolution period and approaches for meeting projected global demand Crop Sci. 39 1999 1611 1621
48. M. Reynolds, R. Singh, A. Lbrahim, O. Ageeb, A. Larque-Saavedra, and J. Quick Evaluating physiological traits to complement empirical selection for wheat in warm environments Euphytica 100 1998 85 94
49. H.G. Jones, M. Stoll, T. Santos, C. de Sousa, M.M. Chaves, and O.M. Grant Use of infrared thermography for monitoring stomatal closure in the field: application to grapevine J. Exp. Bot. 53 2002 2249 2260
50. B. Gao, Normalized difference water index for remote sensing of vegetation liquid water from space, in: SPIE's Symposium on OE/Aerospace Sensing and Dual Use Photonics, International Society for Optics and Photonics, 1995, pp. 225-236.
51. L. Wang, J.J. Qu, and X. Hao Forest fire detection using the normalized multi-band drought index (NMDI) with satellite measurements Agric. For. Meteorol. 148 11 2008 1767 1776
52. A. Prashar, and H.G. Jones Infra-red thermography as a high-throughput tool for field phenotyping Agronomy 4 2014 397 417
53. J.W. Rouse, R.H. Haas, J.A. Schell, D.W. Deering, Monitoring vegetation systems in the great plains with ERTS, in: Proceedings of Third ERTS Symposium, NASA SP-351, NASA, Washington, DC, 1 (1973), pp. 309-317.
54. M. Hardisky, V. Klemas, and R. Smart The Influences of soil salinity, growth form, and leaf moisture on the spectral reflectance of Spartina Alterniflora canopies Photogramm. Eng. Remote Sens. 49 1983 77 83
55. E.R. Hunt, and B.N. Rock Detection of changes in leaf water content using near-and middle-infrared reflectances Remote Sens. Environ. 30 1 1989 43 54
56. P. Ceccato, S. Flasse, S. Tarantola, S. Jacquemoud, and J.M. Grégoire Detecting vegetation leaf water content using reflectance in the optical domain Remote Sens. Environ. 77 1 2001 22 33
57. T.J. Jackson, D. Chen, M. Cosh, F. Li, M. Anderson, C. Walthall, and E.R. Hunt Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans Remote Sens. Environ. 92 4 2004 475 482
58. E.B. Knipling Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation Remote Sens. Environ. 1 3 1970 155 159
59. E.R. Hunt, B.N. Rock, and P.S. Nobel Measurement of leaf relative water content by infrared reflectance Remote Sens. Environ. 22 3 1987 429 435
60. J. Penuelas, I. Filella, C. Biel, L. Serrano, and R. Save The reflectance at the 950-970 nm region as an indicator of plant water status Int. J. Remote Sens. 14 10 1993 1887 1905
61. S. Sankaran, J.M. Maja, S. Buchanon, and R. Ehsani Huanglongbing (citrus greening) detection using visible, near infrared and thermal imaging techniques Sensors 13 2 2013 2117 2130
62. S. Sankaran, L.R. Khot, and A.H. Carter Field-based crop phenotyping: Multispectral aerial imaging for evaluation of winter wheat emergence and spring stand Comput. Electron. Agric. 118 2015 372 379
63. F. Garcia-Ruiz, S. Sankaran, J.M. Maja, W.S. Lee, J. Rasmussen, and R. Ehsani Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees Comput. Electron. Agric. 91 2013 106 115
64. S. Sankaran, L.R. Khot, C.Z. Espinoza, S. Jarolmasjed, V.R. Sathuvalli, G.J. Vandemark, P.N. Miklas, A.H. Carter, M.O. Pumphrey, N.R. Knowles, and M.J. Pavek Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: a review Eur. J. Agron. 70 2015 112 123
65. H.G. Jones, R. Serraj, B.R. Loveys, L. Xiong, A. Wheaton, and A.H. Price Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field Funct. Plant Biol. 36 11 2009 978 989