Blackburn, G.A., and J.G. Ferwerda. 2008. Retrieval of chlorophyll concentration from leaf reflectance spectra using wavelet analysis. Remote Sens. Environ. 112:614– 1632.
Chaerle, L., D. Hagenbeek, X. Vanrobaeys, and D. Van Der Straeten. 2007. Early detection of nutrient and biotic stress in Phaseolus vulgaris. Int. J. Remote Sens. 28:3479–3492.
Chávez, P., C. Yarlequé, O. Piro, A. Posadas, V. Mares, H. Loayza, C. Chuquillanqui, P. Zorogastúa, J. Flexas, and R. Quiroz. 2010. Applying Multifractal Analysis to Remotely Sensed Data for Assessing PYVV Infection in Potato (Solanum tuberosum L.) Crops. Remote Sens. 2:1197-1216
Chavez, P., P. Zorogastua, C. Chuquillanqui, L.F Salazar, V. Mares, and R. Quiroz. 2011. Assessing potato yellow vein virus (PYVV) infection using remotely sensed data. Int. J. Pest Manage. 55:251–6.
Clark, M. F., and A. N. Adams. 1977. Characteristics of the microplate method of enzyme-linked immunosorbent assay for the detection of plant viruses. J. Gen. Virol. 34(3): 475-483.
Du, Z., J. Chen, and C. Hiruki. 2006. Optimization and application of a multiplex RT-PCR system for simultaneous detection of five potato viruses using 18S rRNA as an internal control. Plant Dis. 90:185–189.
Gazala, I.F.S., R.N. Sahoo, P. Pandey, B. Mandal, V.K. Gupta, R. Singh, and P. Sinha. 2013. Spectral reflectance pattern in soybean for assessing yellow mosaic disease. Indian J. Virol. 2:242-249
Griffel, L.M., D. Delparte, and J. Edwards. 2018. Using Support Vector Machines classification to differentiate spectral signatures of potato plants infected with Potato Virus Y. Comput. Electron. Agric. 153:318-324.
Grishama, M.P., R.M. Johnsona, and P.V. Zimba. 2010. Detecting Sugarcane yellow leaf virus infection in asymptomatic leaves with hyperspectral remote sensing and associated leaf pigment changes. J. Virol. Methods. 167:140–145
Guo, T.T., L. Guo, X.H. Wang, and M. Li. 2009. Application of NIR spectroscopy in classification of plant species. In: International Workshop on Education Technology and Computer Science, Wuhan, Hubei, China. 3: 879–883.
Huang, J.F., and A. Apan. 2006. Detection of Sclerotinia rot disease on celery using hyperspectral data and partial least squares regression. J. Spat. Sci. 51: 129–142.
Jamshidi, B., S. Minaei, E. Mohajerani, and H. Ghassemian. 2014. Effect of Spectral Pre-Processing Methods on Non-Destructive Quality Assessment of Oranges Using NIRS. J. Agric. Eng. Res. 2:27-44
Jinendra, B., K. Tamaki, S. Kuroki, M. Vassileva, S. Yoshida, and R. Tsenkova. 2010. Near infrared spectroscopy and aquaphotomics: Novel approach for rapid in vivo diagnosis of virus infected soybean. Biochem. Biophys. Res. Commun. 397: 685–690
Krezhova, D., D. Hristovsa, and T. Yanev. 2010. Spectral Remote Sensing of Tomato Plants (Lycopersicon esculentum L.) Infected with Tomato Mosaic Virus (ToMV). Pp 715–722. In R. Reuter (Ed). Proc. 30th EARSeL Symp. Remote Sensing Sci. Educ. Nat. Cult. Heritage, 31 May-3 June 2010, France
Mahlein, A.K., T. Rumpf, P. Welke, H.W. Dehne, L. Plümer, U. Steiner, and E.C. Oerke. 2013. Development of spectral indices for detecting and identifying plant diseases. Remote Sens. Environ. 128: 21–30.
Moshou, D., C. Bravo, R. Oberti, J. West, L. Bodria, A. McCartney, and Ramon. H. 2005. Plant disease detection basedondata fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps. Real-Time Image. 11:75– 83.
Muhammad, H.H. 2002. Using Hyperspectral Reflectance Data for Discrimination between Healthy and Diseased Plants, and Determination of Damage-level in Diseased Plants. Pp. 49–54. In Proc. 31st Appl. Imagery Pattern Recognition Workshop, 16–18 October. Washington, DC, USA. IEEE.
Naidu, R.A., E.M. Perry, F.J. Pierce, and T. Mekuria. 2009. The potential of spectral reflectance technique for the detection of Grapevine leaf roll-associated virus-3 in two red- berried wine grape cultivars. Comput. Electron. Agric. 66:38-45
Polder, G.. P.M. Blok, H. de Villiers, J.M. van der Wolf, and J. Kamp. 2019. Potato virus Y detection in seed potatoes using deep learning on hyperspectral images. Front. Plant Sci. 10: p.209.
Prabhakar, M., Y. Prasad, and M. Rao. 2012. Remote sensing of biotic stress. Pp 517-545. In B. Venkateswarlu et al. (Ed). Crop plants and its applications for pest management Crop stress and its management. Perspectives and strategies. Springer Science, Dordrecht.
Rahoutei, J., I. García‐Luque, and M. Barón. 2000. Inhibition of photosynthesis by viral infection: effect on PSII structure and function. Physiol. Plant. 110: 286-292.
Saiz‐Abajo, M. J., J. M. Gonzalez‐Saiz, and C. J. Pizarro. 2004. Near infrared spectroscopy and pattern recognition methods applied to the classification of vinegar according to raw material and elaboration process. J. Near Infrared Spectrosc. 12: 207‐219.
Singh, R.P., J. Kurz, and G. Boiteau. 1996. Detection of stylet-borne and circulative potato viruses in aphids by duplex reverse transcription polymerase chain reaction. J. Virol. Methods. 59:189–196.
Sirisomboon, P., Y. Hashimoto, and M. Tanaka. 2009. Study on non-destructive evaluation methods for defect pods for green soybean processing by near-infrared spectroscopy. J. Food Eng. 93:502–512.
Woo, Y.A., H.J. Kim, K.R. Ze, and H. Chung. 2005. Near‐infrared (NIR) spectroscopy for the non‐destructive and fast determination of geographical origin of Angelicae gigantis Radix. J Pharm Biomed Anal. 36: 955‐959.
Zhang, C., Y Shen, J. Chen, P. Xiao, and J. Bao. 2008. Nondestructive prediction of total phenolics, flavonoid contents, and antioxidant capacity of rice grain using near-infrared spectroscopy. J. Agric. Food Chem. 56:8268–8272.
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