1. [1] S. Sankaran, A. Mishra, R. Ehsani, and C. Davis, "A review of advanced techniques for detecting plant diseases," Computers and Electronics in Agriculture, vol. 72, pp. 1-13, 2010. [ DOI:10.1016/j.compag.2010.02.007] 2. [2] D. Ashourloo, "Developing an algorithme to estimate and detect wheat rust by field spectrometry", Ph.D Thesis, Geomatics Engineering Faculty, K.N.Toosi University of Technology, 2014. 3. [3] H. H. Muhammed, "Hyperspectral crop reflectance data for characterising and estimating fungal disease severity in wheat," Biosystems Engineering, vol. 91, pp. 9-20, 2005. [ DOI:10.1016/j.biosystemseng.2005.02.007] 4. [4] J. S. West, C. Bravo, R. Oberti, D. Lemaire, D. Moshou, and H. A. McCartney, "The potential of optical canopy measurement for targeted control of field crop diseases," Annual review of Phytopathology, vol. 41, pp. 593-614, 2003. [ DOI:10.1146/annurev.phyto.41.121702.103726] 5. [5] M. D. Bolton, J. A. Kolmer, and D. F. Garvin, "Wheat leaf rust caused by Puccinia triticina," Molecular plant pathology, vol. 9, pp. 563-575, 2008. [ DOI:10.1111/j.1364-3703.2008.00487.x] 6. [6] D. Ashourloo, M. R. Mobasheri, and A. Huete, "Evaluating the effect of different wheat rust disease symptoms on vegetation indices using hyperspectral measurements," Remote Sensing, vol. 6, pp. 5107-5123, 2014. [ DOI:10.3390/rs6065107] 7. [7] D. Ashourloo, M. R. Mobasheri, and A. Huete, "Developing two spectral disease indices for detection of wheat leaf rust (Pucciniatriticina)," Remote Sensing, vol. 6, pp. 4723-4740, 2014. [ DOI:10.3390/rs6064723] 8. [8] J. Franke, G. Menz, E.-C. Oerke, and U. Rascher, "Comparison of multi-and hyperspectral imaging data of leaf rust infected wheat plants," in Remote Sensing, 2005, pp. 59761D-59761D-11. [ DOI:10.1117/12.626531] 9. [9] T. Mewes, B. Waske, J. Franke, and G. Menz, "Derivation of stress severities in wheat from hyperspectral data using support vector regression," in 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2010, pp. 1-4. [ DOI:10.1109/WHISPERS.2010.5594921] 10. [10] R. Devadas, D. Lamb, S. Simpfendorfer, and D. Backhouse, "Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves," Precision Agriculture, vol. 10, pp. 459-470, 2009. [ DOI:10.1007/s11119-008-9100-2] 11. [11] H. Wang, F. Qin, Q. Liu, L. Ruan, R. Wang, Z. Ma, et al., "Identification and disease index inversion of wheat stripe rust and wheat leaf rust based on hyperspectral data at canopy level," Journal of Spectroscopy, vol. 2015, 2015. [ DOI:10.1155/2015/651810] 12. [12] 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. [ DOI:10.1109/JSTARS.2016.2575360] 13. [13] S. Naseri.Nasab, "Pixel-Based abundance estimation of minerals using the absolute minimum and maximum of reflectance curve", MSc Thesis, Geomatics Engineering Faculty, K.N.Toosi University of Technology, 2015. 14. [14] M. Ojaghloo, M. R. Mobasheri and Y. Rezaei, "Classification of hyperspectral images,Using derivative in the spectral space and coding methods," Iranian Journal of Remote Sensing and GIS, vol. 5, no. 1, pp. 13-28, 2013. 15. [15] A. S. Mazer, M. Martin, M. Lee, and J. E. Solomon, "Image processing software for imaging spectrometry data analysis," Remote Sensing of Environment, vol. 24, pp. 201-210, 1988. [ DOI:10.1016/0034-4257(88)90012-0] 16. [16] S. e. Qian, A. B. Hollinger, D. Williams, and D. Manak, "Fast three‐dimensional data compression of hyperspectral imagery using vector quantization with spectral‐feature‐based binary coding," Optical Engineering, vol. 35, pp. 3242-3249, 1996. [ DOI:10.1117/1.601062] 17. [17] C.-I. Chang, S. Chakravarty, H.-M. Chen, and Y.-C. Ouyang, "Spectral derivative feature coding for hyperspectral signature analysis," Pattern recognition, vol. 42, pp. 395-408, 2009. [ DOI:10.1016/j.patcog.2008.07.016] 18. [18] F. Tsai and W. D. Philpot, "A derivative-aided hyperspectral image analysis system for land-cover classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 40, pp. 416-425, 2002. [ DOI:10.1109/36.992805] 19. [19] T. H. Demetriades-Shah, M. D. Steven, and J. A. Clark, "High resolution derivative spectra in remote sensing," Remote Sensing of Environment, vol. 33, pp. 55-64, 1990. [ DOI:10.1016/0034-4257(90)90055-Q] 20. [20] W. D. Philpot, "The derivative ratio algorithm: avoiding atmospheric effects in remote sensing," IEEE Transactions on Geoscience and Remote Sensing, vol. 29, pp. 350-357, 1991. [ DOI:10.1109/36.79425] 21. [21] A. Martinez, A. Sawyer, J. Youmans and J. Buck, "Identification and Control of Leaf Rust of Wheat in Georgia," UGA Extension publications, Athens, Georgia, 2014. 22. [22] R. S. Kim, "Spectral Matching using Bitmap Indices of Spectral Derivatives for the Analysis of Hyperspectral Imagery", MSc Thesis, The Ohio State University, 2011. 23. [23] K. L. Castro-Esau, G. A. Sánchez-Azofeifa, B. Rivard, S. J. Wright, and M. Quesada, "Variability in leaf optical properties of Mesoamerican trees and the potential for species classification," American Journal of Botany, vol. 93, pp. 517-530, 2006. [ DOI:10.3732/ajb.93.4.517] 24. [24] A. Burkholder, T. A. Warner, M. Culp, and R. Landenberger, "Seasonal trends in separability of leaf reflectance spectra for Ailanthus altissima and four other tree species," Photogrammetric Engineering & Remote Sensing, vol. 77, pp. 793-804, 2011. [ DOI:10.14358/PERS.77.8.793]
|