Using Support Vector Machines classification to differentiate spectral signatures of potato plants infected with Potato Virus Y

Computers and Electronics in Agriculture

Volumn 153, Issue , 2018, Pages 318-324

  Griffel, L M ^a   Delparte, D ^a   Edwards, J ^a  

^a IDAHO STATE UNIVERSITY   (United States)

Author keywords

Machine learning; Potato; Potyviridae; PVY; Remote sensing

Indexed keywords

ANTENNAS; INFRARED DEVICES; REFLECTION; REMOTE SENSING; SEED; UNMANNED AERIAL VEHICLES (UAV); VIRUSES;

LEARNING CLASSIFIERS; MACHINE-LEARNING; POTATO; POTATO PLANTS; POTATO VIRUS; POTYVIRIDA; PVY; REMOTE-SENSING; SPECTRAL SIGNATURE; SUPPORT VECTOR MACHINE CLASSIFICATION;

SUPPORT VECTOR MACHINES;

ACCURACY ASSESSMENT; COMMERCIAL SPECIES; DISEASE SEVERITY; FOOD PROCESSING; LABORATORY METHOD; MACHINE LEARNING; POTATO; REMOTE SENSING; SEED PRODUCTION; SPECTRAL REFLECTANCE; STAKEHOLDER; SUPPORT VECTOR MACHINE; VEGETATION CLASSIFICATION; VIRAL DISEASE;

HEXAPODA; POTATO VIRUS Y; POTYVIRIDAE; SOLANUM TUBEROSUM;

EID: 85052638924     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2018.08.027     Document Type: Article

References (41)

1. Behmann, J., Mahlein, A.-K., Rumpf, T., Römer, C., Plümer, L., A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. Precis. Agric 16 (2015), 239–260, 10.1007/s11119-014-9372-7.
2. Bhandari, S., Raheja, A., Green, R.L., Do, D., Towards collaboration between unmanned aerial and ground vehicles for precision agriculture. Auton.Air Gr Sens. Syst. Agric. Optim. Phenotyping II, 10218, 10218, 2017, 06, 10.1117/12.2262049.
3. Bradley, R.H.E., Studies of the mechanism of transmission of Potato virus Y by the green peach aphid, Myzus persicae (Sulz.)(Homoptera: Aphidae). Can. J. Zool. 32 (1954), 64–73.
4. Chávez, P., Yarlequé C., Piro, O., Posadas, A., Mares, V., Loayza, H., Chuquillanqui, C., Zorogastúa, P., Flexas, J., Quiroz, R., Applying multifractal analysis to remotely sensed data for assessing PYVV infection in potato (Solanum tuberosum L.) crops. Remote Sens. 2 (2010), 1197–1216, 10.3390/rs2051197.
5. Chávez, P., Zorogastúa, P., Chuquillanqui, C., Salazar, L.F., Mares, V., Quiroz, R., Assessing Potato Yellow Vein Virus (PYVV) infection using remotely sensed data. Int. J. Pest Manag. 55 (2009), 251–256, 10.1080/09670870902862685.
6. Cortes, C., Vapnik, V., Support vector networks. Mach. Learn. 20 (1995), 273–297, 10.1007/BF00994018.
7. Döring, T.F., Schrader, J., Schüler, C., Representation of potato virus Y control strategies in current and past extension literature. Potato Res. 49 (2007), 225–239, 10.1007/s11540-007-9019-4.
8. Godwin, R.J., Miller, P.C.H., A review of the technologies for mapping within-field variability. Biosyst. Eng. 84 (2003), 393–407.
9. Gopal, J., Khurana, S.M., Handbook of potato production, improvement, and postharvest management. 2006, Food Products Press.
10. Halterman, D., Charkowski, A., Verchot, J., Potato, viruses, and seed certification in the USA to provide healthy propagated tubers. Pest Technol. 6 (2012), 1–14.
11. Harrington, R., Katis, N., Gibson, R.W., Field assessment of the relative importance of different aphid species in the transmission of potato virus Y. Potato Res. 29 (1986), 67–76, 10.1007/BF02361982.
12. Hillnhütter, C., Mahlein, A.K., Sikora, R.A., Oerke, E.C., Remote sensing to detect plant stress induced by Heterodera schachtii and Rhizoctonia solani in sugar beet fields. F. Crop. Res. 122 (2011), 70–77, 10.1016/j.fcr.2011.02.007.
13. Karasev, A.V., Gray, S.M., Continuous and emerging challenges of potato virus Y in potato. Annu. Rev. Phytopathol. 51 (2013), 571–586.
14. Kerlan, C., Potato viruses. Encycl. Virol., 296–309, 2008, 10.1016/B978-012374410-4.00646-4.
15. Mahlein, A.-K., Plant disease detection by imaging sensors – parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis. 100 (2016), 241–251, 10.1094/PDIS-03-15-0340-FE.
16. Mallik, I., Anderson, N.R., Gudmestad, N.C., Detection and differentiation of potato virus Y strains from potato using immunocapture multiplex RT-PCR. Am. J. Potato Res. 89 (2012), 184–191, 10.1007/s12230-012-9241-8.
17. Milton, E.J., Schaepman, M.E., Anderson, K., Kneubuhler, M., Fox, N., Progress in field spectroscopy. Remote Sens. Environ. 113 (2009), S92–S109.
18. Moriya, A.S., Imai, N.N., Maria, A., Tommaselli, G., Miyoshi, G.T., Mapping Mosaic Virus in Sugarcane Based on Hyperspectral Images. IEEE J. Selected Topics Appl. Earth Observ. Remote Sens. 10 (2017), 740–748, 10.1109/JSTARS.2016.2635482.
19. Mountrakis, G., Im, J., Ogole, C., Support vector machines in remote sensing: a review. ISPRS J. Photogramm. Remote Sens. – ISPRS J. Photogramm. Remote Sens. J. homepage, 2010, 10.1016/j.isprsjprs.2010.11.001.
20. Mulder, A., Turkensteen, L.J., Potato diseases: diseases, pests and defects. 2005, NIVAP.
21. Naidu, R.A., Perry, E.M., Pierce, F.J., Mekuria, T., The potential of spectral reflectance technique for the detection of Grapevine leafroll-associated virus-3 in two red-berried wine grape cultivars. Comput. Electron. Agric. 66 (2009), 38–45.
22. Nolte, P., Alvarez, J.M., Whitworth, J.L., Potato Virus Y Management for the seed potato producer. 2002, University of Idaho Extension.
23. Nolte, P., Falls, I., Whitworth, J.L., States, U., Thornton, M.K., Effect of seedborne potato virus Y on performance of russet burbank, russet norkotah, and shepody potato. Plant Dis. 88 (2004), 248–252.
24. Otulak, K., Garbaczewska, G., Cytopathological potato virus Y structures during solanaceous plants infection. Micron 43 (2012), 839–850, 10.1016/j.micron.2012.02.015.
25. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, É., Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12 (2012), 2825–2830, 10.1007/s13398-014-0173-7.2.
26. Perring, T.M., Gruenhagen, N.M., Farrar, C.A., Management of plant viral diseases through chemical control of insect vectors. Annu. Rev. Entomol. 44 (1999), 457–481, 10.1146/annurev.ento.44.1.457.
27. Pilli, S.K., Nallathambi, B., George, S.J., Diwanji, V., EAGROBOT - A robot for early crop disease detection using image processing. 2nd International Conference on Electronics and Communication Systems, ICECS 2015, 2015, IEEE1689, 1684, 10.1109/ECS.2015.7124873.
28. Polder, G., van der Heijden, G.W.A.M., van Doorn, J., Baltissen, T.A., Automatic detection of tulip breaking virus (TBV) in tulip fields using machine vision. Biosyst. Eng. 117 (2014), 35–42.
29. Pompe-Novak, M., Gruden, K., Baebler, Š., Krečič-Stres, H., Kovač M., Jongsma, M., Ravnikar, M., Potato virus Y induced changes in the gene expression of potato (Solanum tuberosum L.). Physiol. Mol. Plant Pathol. 67 (2006), 237–247, 10.1016/j.pmpp.2006.02.005.
30. Pujari, D., Yakkundimath, R., Byadgi, A.S., SVM and ANN based classification of plant diseases using feature reduction technique. IJIMAI 3 (2016), 6–14.
31. Rahnemoonfar, M., Sheppard, C., Real-time yield estimation based on deep learning. Auton. Air Gr Sens. Syst. Agric. Optim. Phenotyping II, 10218, 2017, 10218 09, 10.1117/12.2263097.
32. Rumpf, T., Mahlein, A., Steiner, U., Oerke, E.-C., Dehne, H.-W., Plümer, L., Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance. Comput. Electron. Agric. 74 (2010), 91–99, 10.1016/j.compag.2010.06.009.
33. Schmitt, D.P., Embracing the emerging precision agriculture technologies for site-specific management of yield-limiting factors. J. Nematol. 34 (2002), 185–188.
34. Scholthof, K.B.G., Adkins, S., Czosnek, H., Palukaitis, P., Jacquot, E., Hohn, T., Hohn, B., Saunders, K., Candresse, T., Ahlquist, P., Hemenway, C., Foster, G.D., Top 10 plant viruses in molecular plant pathology. Mol. Plant Pathol. 12 (2011), 938–954, 10.1111/j.1364-3703.2011.00752.x.
35. Slack, S.A., German, T.L., 2001. Diseases caused by viruses and viroids. Compend. Potato Dis., second ed. W. R. Stevenson, R. Loria, GD Fr. DP Weingartner, eds. Am. Phytopathol. Soc. St. Paul, MN 57–62.
36. Soil Survey Staff, Soil Survey Geographic (SSURGO) Database [WWW Document]. 2018, Nat. Resour. Conserv. Serv. United States Dep Agric https://sdmdataaccess.sc.egov.usda.gov (accessed 2.18.18).
37. Stark, J.C., Love, S.L., Potato production systems. 2003, Univ Idaho, Extension., Idaho, USA.
38. Strand, L.L., Rude, P.A., Integrated pest management for potatoes in the Western United States. second. ed., 2006, University of California Agriculture and Natural Resources Communication Services.
39. Tilman, D., Balzer, C., Hill, J., Befort, B.L., Global food demand and the sustainable intensification of agriculture. Proc. Natl. Acad. Sci. USA. 108 (2011), 20260–20264, 10.1073/pnas.1116437108.
40. United States Department of Agriculture National Agricultural Statistics Service [WWW Document], 2017. URL http://usda.mannlib.cornell.edu/usda/current/Pota/Pota-09-14-2017.pdf (accessed 2.19.18).
41. Whitworth, J., Nolte, P., Singh, R., Boucher, A., Xu, H., Potato virus Y: an evolving concern for potato crops in the United States and Canada. Plant Dis. 94 (2010), 1384–1397, 10.1094/PDIS-02-10-0124.