Image-based phenotyping of plant disease symptoms

Frontiers in Plant Science

Volumn 5, Issue JAN, 2015, Pages

  Mutka, Andrew M ^a   Bart, Rebecca S ^a  

^a DONALD DANFORTH PLANT SCIENCE CENTER   (United States)

Author keywords

Host; Imaging; Pathogen; Phenotyping; Plant disease

Indexed keywords

EID: 84922240808     PISSN: None     EISSN: 1664462X     Source Type: Journal    
DOI: 10.3389/fpls.2014.00734     Document Type: Review

References (55)

1. Araus, J. L., and Cairns, J. E. (2014). Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci. 19, 52-61. doi: 10.1016/j.tplants.2013.09.008
2. Baker, N. R. (2008). Chlorophyll fluorescence: a probe of photosynthesis in vivo. Annu. Rev. Plant Biol. 59, 89-113. doi: 10.1146/annurev.arplant.59.032607.092759
3. Bart, R., Cohn, M., Kassen, A., McCallum, E. J., Shybut, M., Petriello, A., et al.. High-throughput genomic sequencing of cassavabacterial blight strains identifies conserved effectors to target for durable resistance. Proc. Natl. Acad. Sci. U.S.A. 109, E1972-E1979. doi: 10.1073/pnas.1208003109
4. Bauriegel, E., Giebel, A., and Herppich, W. B. (2011). Hyperspectral and chlorophyll fluorescence imaging to analyse the impact of Fusarium culmorum on the photosynthetic integrity of infected wheat ears. Sensors (Basel) 11, 3765-3779. doi: 10.3390/s110403765
5. Bauriegel, E., and Herppich, W. (2014). Hyperspectral and chlorophyll fluorescence imaging for early detection of plant diseases, with special reference to Fusar- ium spec. infections on wheat. Agriculture 4, 32-57. doi: 10.3390/agriculture 4010032
6. Becker, E. M., Herrfurth, C., Irmisch, S., Kollner, T. G., Feussner, I., Karlovsky, P., et al. (2014). Infection of corn ears by Fusarium spp. induces the emission of volatile sesquiterpenes. J. Agric. Food Chem. 62, 5226-5236. doi: 10.1021/jf500560f
7. Berger, S., Benediktyova, Z., Matous, K., Bonfig, K., Mueller, M. J., Nedbal, L., et al. (2007). Visualization of dynamics of plant-pathogen interaction by novel combination of chlorophyll fluorescence imaging and statistical analysis: differential effects of virulent and avirulent strains of P. syringae and of oxylipins on A. thaliana. J. Exp. Bot. 58, 797-806. doi: 10.1093/jxb/erl208
8. Berkman, P. J., Lai, K., Lorenc, M. T., and Edwards, D. (2012). Next-generation sequencing applications for wheat crop improvement. Am. J. Bot. 99, 365-371. doi: 10.3732/ajb.1100309
9. Bock, C. H., Parker, P. E., Cook, A. Z., and Gottwald, T. R. (2008). Visual rating and the use of image analysis for assessing different symptoms of citrus canker on grapefruit leaves. Plant Dis. 92, 530-541.doi: 10.1094/PDIS-92-4-0530
10. Bock, C. H., Poole, G. H., Parker, P. E., and Gottwald, T. R. (2010). Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. Crit. Rev. Plant Sci. 29, 59-107. doi: 10.1080/07352681003617285
11. Bonfig, K. B., Schreiber, U., Gabler, A., Roitsch, T., and Berger, S. (2006). Infection with virulent and avirulent P. syringae strains differentially affects photosynthesis and sink metabolism in Arabidopsis leaves. Planta 225, 1-12. doi: 10.1007/s00425-006-0303-3
12. Bravo, C., Moshou, D., West, J., Mccartney, A., and Ramon, H. (2003). Early disease detection in wheat fields using spectral reflectance. Biosyst. Eng. 84, 137-145. doi: 10.1016/S1537-5110(02)00269-6
13. Busemeyer, L., Mentrup, D., Moller, K., Wunder, E., Alheit, K., Hahn, V., et al. (2013). BreedVision-a multi-sensor platform for non-destructive field- based phenotyping in plant breeding. Sensors (Basel) 13, 2830-2847. doi: 10.3390/s130302830
14. Calvino-Cancela, M., Mendez-Rial, R., Reguera-Salgado, J., and Martin-Herrero, J.. Alien plant monitoring with ultralight airborne imaging spectroscopy. PLoS ONE 9:e102381. doi: 10.1371/journal.pone.0102381
15. Chaerle, L., De Boever, D., Van Montagu, M., and Van Der Straeten, D. (2001). Thermographic visualization of cell death in tobacco and Arabidopsis. Plant Cell Environ. 24, 15-25. doi: 10.1046/j.1365-3040.2001.00654.x
16. Chaerle, L., Hagenbeek, D., De Bruyne, E., Valcke, R., and Van Der Straeten, (2004). Thermal and chlorophyll-fluorescence imaging distinguish plant- pathogen interactions at an early stage. Plant Cell Physiol. 45, 887-896. doi: 10.1093/pcp/pch097
17. Chaerle, L., Van Caeneghem, W., Messens, E., Lambers, H., Van Montagu, M., and Van Der Straeten, M. (1999). Presymptomatic visualization of plant- virus interactions by thermography. Nat. Biotechnol. 17, 813-816. doi: 10.1038/ 11765
18. Dangl, J. L., Horvath, D. M., and Staskawicz, B. J. (2013). Pivoting the plant immune system from dissection to deployment. Science 341, 746-751. doi: 10.1126/science.1236011
19. Dhondt, S., Wuyts, N., and Inze, D. (2013). Cell to whole-plant pheno- typing: the best is yet to come. Trends Plant Sci. 18, 428-439. doi: 10.1016/j.tplants.2013.04.008
20. Fiorani, F., and Schurr, U. (2013). Future scenarios for plant phenotyping. Annu. Rev. Plant Biol. 64, 267-291. doi: 10.1146/annurev-arplant-050312-120137
21. Furbank, R. T., and Tester, M. (2011). Phenomics-technologies to relieve the phenotyping bottleneck. Trends Plant Sci. 16, 635-644. doi: 10.1016/j.tplants.2011.09.005
22. Granier, C., and Vile, D. (2014). Phenotyping and beyond: modelling the relationships between traits. Curr. Opin. Plant Biol. 18C, 96-102. doi: 10.1016/j.pbi.2014.02.009
23. Gudesblat, G. E., Torres, P. S., and Vojnov, A. A. (2009). Xanthomonas campestris overcomes Arabidopsis stomatal innate immunity through a DSF cell-to- cell signal-regulated virulence factor. Plant Physiol. 149, 1017-1027. doi: 10.1104/pp.108.126870
24. Hartmann, A., Czauderna, T., Hoffmann, R., Stein, N., and Schreiber, F. (2011). HTPheno: an image analysis pipeline for high-throughput plant phenotyping. BMC Bioinformatics 12:148. doi: 10.1186/1471-2105-12-148
25. Ilic, K., Kellogg, E. A., Jaiswal, P., Zapata, F., Stevens, P. F., Vincent, L. P., et al. (2007). The plant structure ontology, a unified vocabulary of anatomy and morphology of a flowering plant. Plant Physiol. 143, 587-599. doi: 10.1104/pp.106.092825
26. Jackson, E. W., Avant, J. B., Overturf, K. E., and Bonman, J. M. (2006). A quantitative assay of Puccinia coronata f.sp. avenae DNA in Avena sativa. Plant Dis. 90, 629-636. doi: 10.1094/PD-90-0629
27. Jansen, M., Gilmer, F., Biskup, B., Nagel, K. A., Rascher, U., Fischback, A., et al. (2009). Simultaneous phenotyping of leaf growth and chlorophyll fluorescence via GROWSCREEN FLUORO allows detection of stress tolerance in Arabidopsis thaliana and other rosette plants. Funct. PlantBiol. 36, 902-914. doi: 10.1071/FP09095
28. Jones, H. G., Serraj, R., Loveys, B. R., Xiong, L., Wheaton, A., and Price, A. H.. Thermal infrared imaging ofcrop canopies for the remote diagnosis and quantification of plant responses to water stress in the field. Funct. Plant Biol. 36, 978-989. doi: 10.1071/FP09123
29. Klukas, C., Chen, D., and Pape, J. M. (2014). Integrated Analysis Platform: an open-source information system for high-throughput plant phenotyping. Plant Physiol. 165, 506-518. doi: 10.1104/pp.113.233932
30. Kunkeaw, S., Tan, S., and Coaker, G. (2010). Molecular and evolutionary analyses of Pseudomonas syringae pv. tomato race 1. Mol. Plant Microbe Interact. 23, 415-424. doi: 10.1094/MPMI-23-4-0415
31. Leprevost, F. V., Barbosa, V. C., Francisco, E. L., Perez-Riverol, Y., and Carvalho, P. C. (2014). On best practices in the development of bioinformatics software. Front. Genet. 5:199. doi: 10.3389/fgene.2014.00199
32. Lindenthal, M., Steiner, U., Dehne, H. W., and Oerke, E. C. (2005). Effect of downy mildew development on transpiration of cucumber leaves visualized by digital infrared thermography. Phytopathology 95, 233-240. doi: 10.1094/PHYTO-95-0233
33. Lobet, G., Draye, X., and Perilleux, C. (2013). An online database for plant image analysis software tools. Plant Methods 9, 38. doi: 10.1186/1746-4811-9-38
34. Mahlein, A. K., Steiner, U., Hillnhutter, C., Dehne, H. W., and Oerke, E. C. (2012). Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases. Plant Methods 8, 3. doi: 10.1186/1746-4811-8-3
35. Marr, D. (1982). Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. San Francisco: Freeman.
36. Matous, K., Benediktyova, Z., Berger, S., Roitsch, T., and Nedbal, L. (2006). Case study of combinatorial imaging: what protocol and what chlorophyll fluorescence image to use when visualizing infection of Arabidopsis thaliana by Pseudomonas syringae Photosynth. Res. 90, 243-253. doi: 10.1007/s11120-006-9120-6
37. Melotto, M., Underwood, W., and He, S. Y. (2008). Role of stomata in plant innate immunityandfoliar bacterial diseases. Annu. Rev. Phytopathol. 46,101-122. doi: 10.1146/annurev.phyto.121107.104959
38. Mesterhazy, A., Lemmens, M., and Reid, L. M. (2012). Breeding for resistance to ear rots caused by Fusarium spp. in maize-a review. Plant Breed. 131, 1-19. doi: 10.1111/j.1439-0523.2011.01936.x
39. Munns, R., James, R. A., Sirault, X. R., Furbank, R. T., and Jones, H. G. (2010). New phenotyping methods for screening wheat and barley for beneficial responses to water deficit. J. Exp. Bot. 61, 3499-3507. doi: 10.1093/jxb/erq199
40. Murillo-Williams, A., and Munkvold, G. P. (2008). Systemic infection by Fusarium verticillioides in maize plants grown under three temperature regimes. PlantDis. 92, 1695-1700. doi: 10.1094/PDIS-92-12-1695
41. Nutter, F. W. Jr., Gleason, M. L., Jenco, J. H., and Christians, N. C. (1993). Assessing the accuracy, intra-rater repeatability, and inter-rater reliability of disease assessment systems. Phytopathology 83, 806-812. doi: 10.1094/Phyto-83-806
42. Olmstead, J. W., Lang, G. A., and Grove, G. G. (2001). Assessment of severity of powdery mildew infection of sweet cherry leaves by digital image analysis. HortScience 36, 107-111.
43. Prashar, A., Yildiz, J., Mcnicol, J. W., Bryan, G. J., and Jones, H. G. (2013). Infra-red thermography for high throughput field phenotyping in Solanum tuberosum. PLoS ONE 8:e65816. doi: 10.1371/journal.pone.0065816
44. Pridmore, T. P., French, A. P., and Pound, M. P. (2012). What lies beneath: underlyingassumptions in bioimage analysis. Trends Plant Sci. 17, 688-692. doi: 10.1016/j.tplants.2012.07.003
45. Prlic, A., and Procter, J. B. (2012). Ten simple rules for the open development of scientific software. PLoS Comput. Biol. 8:e1002802. doi: 10.1371/journal.pcbi.1002802
46. Rodriguez Iglesias, A., Egana Aranguren, M., Rodriguez Gonzalez, A., and Wilkinson, M. D. (2013). “Plant-pathogen interactions ontology (PPIO)," in Proceedings of the International Work-Conference on Bioinformatics and Biomedical Engineering, 695-702.
47. Rodriguez-Moreno, L., Pineda, M., Soukupova, J., Macho, A. P., Beuzon, C. R., Baron, M., et al. (2008). Early detection of bean infection by Pseudomonas syringae in asymptomatic leaf areas using chlorophyll fluorescence imaging. Photosynth. Res. 96, 27-35. doi: 10.1007/s11120-007-9278-6
48. Rolfe, S. A., and Scholes, J. D. (2010). Chlorophyll fluorescence imaging of plant- pathogen interactions. Protoplasma 247, 163-175. doi: 10.1007/s00709-010-0203-z
49. Rousseau, C., Belin, E., Bove, E., Rousseau, D., Fabre, F., Berruyer, R., et al. (2013). High throughput quantitative phenotyping of plant resistance using chlorophyll fluorescence image analysis. Plant Methods 9, 17. doi: 10.1186/1746-48119-17
50. Scholes, J. D., and Rolfe, S. A. (2009). Chlorophyll fluorescence imaging as tool for understanding the impact of fungal diseases on plant performance: a phenomics perspective. Funct. Plant Biol. 36, 880-892. doi: 10.1071/FP09145
51. Spalding, E. P., and Miller, N. D. (2013). Image analysis is driving a renaissance in growth measurement. Curr. Opin. Plant Biol. 16, 100-104. doi: 10.1016/j.pbi.2013.01.001
52. Stewart, E. L., and McDonald, B. A. (2014). Measuring quantitative virulence in the wheat pathogen Zymoseptoria tritici using high-throughput automated image analysis. Phytopathology 104, 985-992. doi: 10.1094/PHYTO-11-13-0328-R
53. Strange, R. N., and Scott, P. R. (2005). Plant disease: a threat to global food security. Annu. Rev. Phytopathol. 43, 83-116. doi: 10.1146/annurev.phyto.43.113004.133839
54. Walls, R., Smith, B., Elsar, J., Goldfain, A., Stevenson, D. W., and Jaiswal, P. (2012). “A plant disease extension of the Infectious Disease Ontology,” in International Conference on Biomedical Ontology (ICBO-2012), 1-5.
55. Xie, W., Yu, K., Pauls, K. P., and Navabi, A. (2012). Application of image analysis in studies of quantitative disease resistance, exemplified using common bacterial blight-common bean pathosystem. Phytopathology 102, 434-442. doi: 10.1094/PHYTO-06-11-0175