Open Access   Article Go Back

Hyperspectral Analysis of Wheat Leaf Rust (WLR) Disease: A Review

M.K. Maid1 , R.R. Deshmukh2

  1. Department of CS ans IT, Dr. B. A. M. U, Aurangabad, India.
  2. Department of CS ans IT, Dr. B. A. M. U, Aurangabad, India.

Correspondence should be addressed to: mm915monali@gmail.com .

Section:Review Paper, Product Type: Journal Paper
Volume-6 , Issue-1 , Page no. 215-219, Jan-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i1.215219

Online published on Jan 31, 2018

Copyright © M.K. Maid, R.R. Deshmukh . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: M.K. Maid, R.R. Deshmukh, “Hyperspectral Analysis of Wheat Leaf Rust (WLR) Disease: A Review,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.215-219, 2018.

MLA Style Citation: M.K. Maid, R.R. Deshmukh "Hyperspectral Analysis of Wheat Leaf Rust (WLR) Disease: A Review." International Journal of Computer Sciences and Engineering 6.1 (2018): 215-219.

APA Style Citation: M.K. Maid, R.R. Deshmukh, (2018). Hyperspectral Analysis of Wheat Leaf Rust (WLR) Disease: A Review. International Journal of Computer Sciences and Engineering, 6(1), 215-219.

BibTex Style Citation:
@article{Maid_2018,
author = {M.K. Maid, R.R. Deshmukh},
title = {Hyperspectral Analysis of Wheat Leaf Rust (WLR) Disease: A Review},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {1 2018},
volume = {6},
Issue = {1},
month = {1},
year = {2018},
issn = {2347-2693},
pages = {215-219},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1661},
doi = {https://doi.org/10.26438/ijcse/v6i1.215219}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i1.215219}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1661
TI - Hyperspectral Analysis of Wheat Leaf Rust (WLR) Disease: A Review
T2 - International Journal of Computer Sciences and Engineering
AU - M.K. Maid, R.R. Deshmukh
PY - 2018
DA - 2018/01/31
PB - IJCSE, Indore, INDIA
SP - 215-219
IS - 1
VL - 6
SN - 2347-2693
ER -

VIEWS PDF XML
633 359 downloads 262 downloads
  
  
           

Abstract

Remote Sensing has wide range of applications in many different fields. Remote Sensing has been found to be a valuable tool in evaluation, monitoring, and management of land, water and crop resources. The applications of remote sensing techniques in the field of agriculture are wide and varied ranging from crop identification, detection of disease on different crops & predicting grain yield of crops. Many remote sensing applications are devoted to the agricultural sector. The selected applications are put in the context of the global challenges the agricultural sector is facing: minimizing the environmental impact, while increasing production and productivity. The application of remote sensing in agriculture typically involves measuring reflectance of electromagnetic radiation in the visible (390 to 770 nm), near-infrared (NIR, 770 to 1,300 nm), or middle-infrared (1,300 to 2,500 nm) ranges using spectrometers. This paper reviews the concept of hyperspectral remote sensing, use of remote sensing in terms of agriculture field, study of diseased wheat leaves using hyperspectral remote sensing.

Key-Words / Index Term

Remote Sensing, Wheat Leaf Rust, Vegetation Indices, ASD Fieldspec4 Spectroradiometer

References

[1] A. Chitradevi, S. Vijayalakshmi, “Random Forest for Multitemporal and Multiscale Classification of Remote Sensing Satellite Imagery”, International Journal of Computer Sciences and Engineering, Vol. 4, Issue.2, pp.59-65, 2016.
[2] D. Souza, “Growth Complementarity Between Agriculture and Industry: Evidence from a Panel of Developing Countries”, 2014.
[3] G. Boyle, “The Winter Wheat Guide”, Teagasc, pp. 21-40, 2016.
[4] S. N. Wegulo, “Rust Diseases of Wheat”, NebGuide, 2012.
[5] S. Markell, G. Milus, R. Cartwright, J. Hedge, “Rust Diseases of Wheat”, Agriculture and natural resources.
[6] L. Chang, S. Peng-Sen, and Liu Shi-Rong, “A review of plant spectral reflectance response to water physiological changes,” Chinese Journal of Plant Ecology, vol. 40, no. 1, pp. 80–91, 2016.
[7] C. Zhang and J. M. Kovacs, “The application of small unmanned aerial systems for precision agriculture: a review,” Precision Agriculture, vol. 13, no. 6, pp. 693–712, 2012.
[8] J. B Campbell, “Introduction to Remote Sensing”, Taylor and Francis, London, 1996.
[9] H. R. Bin Abdul Rahim, M. Q. Bin Lokman, S. W. Harun, “Applied light-side coupling with optimized spiral-patterned zinc oxide nanorod coatings for multiple optical channel alcohol vapor sensing,” Journal of Nanophotonics, vol. 10, no. 3, Article ID 036009, 2016.
[10] B. A. Cruden, D. Prabhu, and R. Martinez, “Absolute radiation measurement in venus and mars entry conditions,” Journal of Spacecraft and Rockets, vol. 49, no. 6, pp. 1069–1079, 2012.
[11] S. Sankaran, A. Mishra, R. Ehsani, and C. Davis, “A review of advanced techniques for detecting plant diseases,” Comput. Electron. Agriculture, vol. 72, no. 1, pp. 1–13, 2010.
[12] C. Buschmann and E. Nagel, “In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation,” Int. J. Remote Sens, vol. 14, no. 4, pp. 711–722, 1993.
[13] N. K. Poona and R. Ismail, “Using Boruta-selected spectroscopic wavebands for the asymptomatic detection of Fusarium circinatum stress,” IEEE J. Select. Topics Appl. Earth Observations Remote Sens., vol. 7, no. 9, pp. 3764–3772, 2014.
[14] W. Huang, “New optimized spectral indices for identifying and monitoring winter wheat diseases,” IEEE J. Select. Topics Appl. Earth Observations Remote Sens., vol. 7, no. 6, pp. 2516–2524, 2014.
[15] M. D. Bolton, J. A. Kolmer, and D. F. Garvin, “Wheat leaf rust caused by Puccinia triticina,” Molecular Plant Pathology, vol. 9, no. 5, pp. 563–575, 2008.
[16] C. Robert, M.-O. Bancal, B. Ney, and C. Lannou, “Wheat leaf photosynthesis loss due to leaf rust, with respect to lesion development and leaf nitrogen status,” New Phytologist, vol. 165, no. 1, pp. 227–241, 2005.
[17] D. Ashourloo, H. Aghighi, A. A. Matkan, M. R. Mobasheri, and A. M. Rad, “An Investigation Into Machine Learning Regression Techniques For The Leaf Rust Disease Detection Using Hyperspectral Measurement”, IEEE journal of selected topics in applied earth observations and remote sensing, vol. 9, pp. 4344 – 4351, 2016.
[18] J.C. Zhang, R.L. Pu, J.H.Wang, W.J. Huang, L.Yuan, J.H. Luo, “Detecting powdery mildew of winter wheat using leaf level hyperspectral measurements”, Comput. Electron. Agric, pp. 13–23, 2012.
[19] R. N. Strange, P. R. Scott, “Plant Disease: A threat to global food security”, Annual reviews phytopathol, vol. 43, pp. 83-116, 2005.
[20] R. M. Misal, R. R. Deshmukh, “Application of Near-Infrared Spectrometer in Agro-Food Analysis: A Review”, International Journal of Computer Applications, Vol. 141 No.7, pp. 0975 – 8887, 2016.
[21] H.D Roelofsen, P. M. van Bodegom, L. Kooistra, , J. P.M. Witte, “Trait estimation in herbaceous plant assemblages from in situ canopy spectra” Remote Sens., Vol. 5, pp. 6323–6345, 2013.
[22] S. Delalieux, A. Auwerkerken, V.W. Verstraeten, B. Somers, R.Valcke, S.Lhermitte, J. Keulemanss, P. Coppin, “Hyperspectral reflectance and fluorescence imaging to detect scab induced stress in Apple leaves”, Remote Sens, Vol. 1, pp. 858–874, 2009.
[23] U. Steiner, K. Bürling, E.C. Oerke, “Sensor use in plant protection”, Gesunde Pflanz, Vol. 60, pp. 131–141, 2008.
[24] J.C. Zhang, R.L. Pu, J.H.Wang, W.J. Huang, L.Yuan, J .Wang, “Using in-situ hyperspectral data for detecting and discriminating yellow rust disease from nutrient stresse”,Field Crops Res., Vol. 134, pp.165–174, 2012.
[25] C.Hillnhütter, A.K. Mahlein, R.A. Sikora, E.C. Oerke, “Remote sensing to detect plant stress induced by Heterodera schachtii and Rhizoctonia solani in sugar beet fields”, Field Crops Res., Vol. 122, pp. 70–77, 2011.
[26] D. Moshou, C. Bravo, J. West, S. Wahlen, A. McCartney, H. Ramon, “Automatic detection of ―yellow rust‖ in wheat using reflectance measurements and neural networks”, Comput. Electron. Agric, Vol. 44, pp. 173–188, 2004.
[27] A.K. Mahlein, T. Rumpf, P. Welke, H.W. Dehne, L. Plümer, U. Steiner, E.C. Oerke, “Development of spectral indices for detecting and identifying plant diseases”, Remote Sens. Environ, Vol. 128, pp. 21–30, 2013.
[28] J.C. Zhang, R.L. Pu, J.H.Wang, W.J. Huang, L. Yuan, J.H. Luo, “Detecting powdery mildew of winter wheat using leaf level hyperspectral measurements”, Comput. Electron. Agric, Vol. 85, pp. 13–23, 2012.
[29] A.A. Gitelson, Y.J. Kaufman, R. Stark, D. Rundquist, “Novel algorithms for remote estimation of vegetation fraction”, Remote Sens. Environ, Vol.80, pp. 76–87, 2002.
[30] J. Penuelas, F. Baret, I. Filella, “Semiempirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance”, Photosynthetica, Vol. 31, pp. 221–230, 1995.
[31] P. V. Janse, R. R. Deshmukh, “Hyperspectal Remote Sensing for Agriculture: A Review”, International Journal of Computer Applications,Vol.172 No.7, pp. 0975 – 8887, 2017.
[32] A. R. Huete, B. K. Liu, L. Van, “A comparison of vegetation indices over a global set of TM images for EOS-MODIS”, Remote Sensing of Environment, Vol. 59, pp. 440-451, 1997.
[33] J.W. Rouse, R.H. Haas, J.A. Schell, D.W. Deering, “Monitoring vegetation systems in the great plains with ERTS, Third ERTS symposium”, NASA SP-351, NASA Washington, DC, Vol. 1, pp. 309-317, 1973.
[34] C.F. Jorden, “Leaf area index from quality of light on the forest floor”, Ecology, Vol. 50(4), pp. 663-666, 1969.
[35] B. Gao, “NDWI: A normalized difference water index for remote sensing of vegetation liquid water from space”, Remote Sensing of Environment, Vol. 58, pp. 257-266, 1996.
[36] J. Penuelas, J. Pinol, R. Ogaya, I. Lilella, “Estimation of plant water content by the reflectance water index WI (R900/ R970)”, International journal of remote sensing, Vol. 18, pp. 2869-2875, 1997.
[37] Y. J. Kaufman, D. Tanier, “Atmospherically resistant vegetation index (ARVI) for EOS-MODIS”, IEEE Transaction on Geoscience and Remote Sensing, Vol. 30(2), pp. 261-270, 1992.
[38] A.R. Huete, “A soil adjusted vegetation index (SAVI)”, Remote Sensing of Environment, Vol. 71, pp. 158-182, 2000.
[39] A.A. Gitelson, Y. J. Kaufman, R. Stark, D. Rundquist, “Novel algorithm for remote estimation of vegetation fraction”, Remote Sensing of Environment, vol. 80, pp. 76-87, 2002.
[40] J. Penuelas, F. Baret, I. Filella, “Semi empirical indices to assess carotenoids/ chlorophyll a ratio from leaf spectral reflectance”, Photosynthetica, Vol. 31, pp. 221-230, 1995.
[41] G. A. Blackburn, “Spectral indices for estimating photosynthetic pigment concentration: A test using senescent tree leaves”, International journal of remote sensing, Vol. 19, pp. 657-675, 1998.
[42] G. A. Blackburn, “Quantifying chlorophyll and carotenoids from leaf to canopy scale: An evaluation of some hyperspectral approaches”, Remote Sensing of Environment, Vol. 66, pp. 273-285, 1998.
[43] M. N. Merzlyak, A. A. Gitelson, O. B. Chivkunova, Y. Ratikin, “Non-destructive optical detection of pigment changes during leaf senescent and fruit ripening”, Physiologia Plantarum, Vol. 105, pp. 135-141, 1999.
[44] M. S. Kim, “The use of narrow spectral bands for improving remote sensing estimation of fractionally absorbed photosynthetically active radiation (fAPAR)”, Master Thesis, Department of Geography, University of Maryland, College Park, 1994.
[45] C. S. T. Daughtry, C. L. Walthall, M. S. Kim, E. B. de Colstoun, J. E. McMurtrey, “Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance”, Remote Sensing of Environment, Vol. 74, pp. 229-239, 2000.
[46] A. A. Gitelson, G. P. Keydan, M. N. Merzlyak, “Three band model for noninvasive estimation of chlorophyll, carotenoids and anthocyanin contents in higher plant leaves”, Geophysical Research Letters, Vol. 33, L11402, 2006.
[47] A. A. Gitelson, M. N. Merzlyak, O. B. Chivkunova, “Optical properties and non-destructive estimation of anthocyanin content in plant leaves”, Photochemistry and Photobiology, Vol. 74(1), pp. 38-45, 2001.
[48] J. A. Gaman, J. S. Surfus, “Assessing leaf pigment content and activity with a reflectometer”, New Phytologist, Vol. 143, pp. 105-117, 1999.
[49] A. K. Van Den Berg, T. D. Perkins, “Non-destructive estimation of anthocyanin content in autumn auger maple leaves”, Horticultural Science, vol. 40(3), pp. 685-685, 2005.
[50] A. A. Gitelson, Y. Zur, O. B. Chivkunova, M. N. Merzlyak, “Assessing carotenoid content in plant leaves with reflectance spectroscopy, Photochemistry and Photobiology, Vol. 75(3), pp. 272-281, 2002.
[51] A. R. Hunt, B. N. Rock, “Detection of changes in leaf water content using near- and middle-infrared reflectance”, Remote Sensing of Environment, Vol. 30, pp. 43-54, 1989.
[52] B. N. Rock, J. E. Vogelmann, D. L. Williams, A. F. Vogelmann, T. Hoshizaki, “Detection of forest damage”, BioScience, Vol. 36(7), pp. 439-445, 1986.
[53] J. A. Gamon, L. Serrano, J. S. Surfus, “The photochemical reflectance index: An optical indicator of photosynthetic radiation-use efficiency across species, functional types, and nutrient level”, Oecologia, Vol. 112, pp. 492-501, 1997.
[54] D. N. H. Horler, M. Dockray, J. Barber, “The red-edge of plant leaf reflectance”, International journal of remote sensing, Vol. 4, pp. 273-288, 1983.