Predicting pre-planting risk of Stagonospora nodorum blotch in winter wheat using machine learning models

Frontiers in Plant Science

Volumn 7, Issue MAR2016, 2016, Pages

  Mehra, Lucky K ^a   Cowger, Christina ^a,b   Gross, Kevin ^a   Ojiambo, Peter S ^a  

^a NORTH CAROLINA STATE UNIVERSITY   (United States)

^b NORTH CAROLINA STATE UNIVERSITY   (United States)

Author keywords

Disease risk; Machine learning; Random forest; Stagonospora nodorum blotch; Variable importance; Wheat

Indexed keywords

EID: 84964233163     PISSN: None     EISSN: 1664462X     Source Type: Journal    
DOI: 10.3389/fpls.2016.00390     Document Type: Article

References (68)

1. Anonymous (1995). Losses in Agriculture (Agriculture Handbook No. 291). Washington, DC: United States Department ofAgriculture, 120.
2. Anonymous. (2011). USDA, Agricultural Research Service -Eastern United States Septoria Nursery. 2011. Available at: http://www.ars.usda.gov/Main/docs.htm?docid = 8419&page=8.
3. Batchelor, W. D., Yang, X. B., and Tschanz, A. T. (1997). Development of a neural network for soybean rust epidemics. Trans. ASAE 40, 247-252. doi:10.13031/2013.21237
4. Bhathal, J. S., Loughman, R., and Speijers, J. (2003). Yield reduction in wheat in relation to leaf disease from yellow (tan) spot and septoria nodorum blotch. Eur. J. PlantPathol. 109, 435-443. doi:10.1023/A:1024277420773
5. Bishop, C. (1995). Neural Networks for Pattern Recognition. Oxford: Oxford University Press.
6. Breiman, L. (2001). Random forest. Mach. Learn. 45, 5-32. doi:10.1023/A:1017934522171
7. Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1984). Classification and Regression Trees. Monterey, CA: Wadsworth Inc.
8. Cerrito, P. B. (2006). Introduction to Data Mining Using SAS Enterprise Miner. Cary, NC: SAS Institute Inc.
9. Chakraborty, S., Ghosh, R., Ghosh, M., Fernandes, C. D., Charchar, M. J., and Kelemu, S. (2004). Weather-based prediction of anthracnose severity using artificial neural networkmodels. PlantPathol. 53, 375-386. doi:10.1111/j.1365-3059.2004.01044.x
10. Copes, W. E., and Scherm, H. (2010). Rhizoctonia web blight development on container-grown azalea in relation to time and environmental factors. Plant Dis. 94,891-897. doi:10.1094/PDIS-94-7-0891
11. Cowger, C., and Silva-Rojas, H. V. (2006). Frequency of Phaeosphaeria nodorum, the sexual stage of Stagonospora nodorum, on winter wheat in North Carolina. Phytopathology 96, 860-866. doi:10.1094/PHYTO-96-0860
12. De Wolf, E. D., and Francl, L. J. (2000). Neural network classification of tan spot and Stagonospora nodorum blotch infection periods in a wheat field environment. Phytopathology 90, 108-113. doi:10.1094/PHYTO.2000.90.2.108
13. De Wolf, E. D., Madden, L. V., and Lipps, P. E. (2003). Risk assessment models for wheat Fusarium head blight epidemics based on within-season weather data. Phytopathology 93, 428-435. doi:10.1094/PHYTO.2003.93.4.428
14. De'ath, G., and Fabricius, K. E. (2000). Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 81, 31783192. doi:10.1890/0012-9658(2000)081
15. Del Ponte, E. M., Godoy, C. V., Li, X., and Yang, X. B. (2006). Predicting severity ofAsian soybean rust epidemics with empirical rainfall models. Phytopathology 96, 797-803. doi:10.1094/PHYTO-96-0797
16. Eyal, Z. (1981). Integrated control of Septoria diseases of wheat. Plant Dis. 65, 763-768. doi:10.1094/PD-65-763
17. Fan, Z., Kabrick, J. M., and Shifley, S. R. (2006). Classification and regression tree based survival analysis in oak-dominated forests of Missouri’s Ozark highlands. Can.J. For. Res. 36, 1740-1748. doi:10.1139/x06-068
18. Fischer, A. (2015). How to determine the unique contributions of input-variables to the nonlinear regression function of a multilayer perceptron. Ecol. Model. 309-310, 60-63. doi:10.1016/j.ecolmodel.2015.04.015
19. Francl, L. J. (2004). Squeezing the turnip with artificial neural nets. Phytopathology 94, 1007-1012. doi:10.1094/PHYTO.2004.94.9.1007
20. Frasconi, P., Gori, M., and Tesi, A. (1993). “Successes and failures of back-propagation: a theoretical investigation" in Progress in Neural Networks, ed. O. Omidvar (Toronto: Ablex Publishing), 205-242.
21. Garson, G. D. (1991). Interpreting neural-network connection weights. AI Expert 6, 46-51.
22. Garzon, M. B., Blazek, R., Neteler, M., de Dios, R. S., Ollero, H. S., and Furlanello, C. (2006). Predicting habitat suitability with machine learning models: the potential area of Pinus sylvestris L. in the Iberian Peninsula. Ecol. Model. 197, 383-393. doi:10.1016/j.ecolmodel.2006.03.015
23. Gent, D. H., and Ocamb, C. M. (2009). Predicting infection risk of hop by Pseudoperonspora humuli. Phytopathology 99, 1190-1198. doi:10.1094/PHYTO-99-10-1190
24. Gent, G. H., Mahaffee, W. F., McRoberts, N., and Pfender, W. F. (2013). The use and role of predictive systems in disease management. Annu. Rev. Phytopathol. 51,267-289. doi:10.1146/annurev-phyto-082712-102356
25. Gutierrez, D. D. (2015). Machine Learning and Data Science: An Introduction to StatisticalLearningMethods with R. Basking Ridge, NJ: Technics Publications.
26. Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning, Second Edn. New York, NY: Springer.
27. Holmes, S. J. I., and Colhoun, J. (1975). Straw-borne inoculum of Septoria nodorum and S. tritici in relation to incidence of disease on wheat plants. Plant Pathol. 24, 63-66. doi:10.1111/j.1365-3059.1975.tb01863.x
28. Horowitz, J., Ebel, R., and Uda, K. (2010). No-Till’ Farming is a Growing Practice (Economic Information Bulletin Number 70). Washington, DC: United States Department of Agriculture Economic Research Service.
29. Jeger, M. J., and Xu, X. M. (2015). Modelling the dynamics of a plant pathogen and a biological control agent in relation to flowering pattern and populations present on leaves. Ecol Model. 313, 13-28. doi:10.1016/j.ecolmodel.2015.06.015
30. Kelly, M., Guo, Q., Liu, D., and Shaari, D. (2007). Modeling the risk for a new invasive forest disease in the United States: an evaluation of five environmental niche models. Comput. Environ. Urban. Syst. 31, 689-710. doi:10.1016/j.compenvurbsys.2006.10.002
31. Kim, K. S., Beresford, R. M., and Walter, M. (2014). Development of a disease risk prediction model for downy mildew (Peronospora sparsa) in boysenberry. Phytopathology 104, 50-56. doi:10.1094/PHYTO-02-13-0058.
32. King, J. E., Cook, R. J., and Melville, S. C. (1983). A review of Septoria diseases of wheat and barley. Ann. Appl. Biol. 103, 345-373. doi:10.1111/j.1744-7348.1983.tb02773.x
33. Krupinsky, J. M., Tanaka, D. L., Merrill, S. D., Liebig, M. A., Lares, M. T., and Hanson, J. D. (2007). Crop sequence effects on leaf spot diseases of no-till spring wheat. Agron.J. 99, 912-920. doi:10.2134/agronj2006.0130
34. Leath, S., Scharen, A. L., Lund, R. E., and Dietz-Holmes, M. E. (1993). Factors associated with global occurrences of Septoria nodorum blotch and Septoria tritici blotch of wheat. PlantDis. 77, 1266-1270. doi:10.1094/PD-77-1266
35. Liaw, A., and Wiener, M. (2002). Classification and Regression by random Forest. R News 2, 18-22.
36. Luke, H. H., Pfahler, P. L., and Barnett, R. D. (1983). Control of Septoria nodorum on wheat with crop rotation and seed treatment. Plant Dis. 67, 949-951. doi:10.1094/PD-67-949
37. Madden, L. V. (2006). Botanical epidemiology: some key advances and its continuing role in disease management. Eur. J. Plant Pathol. 115, 3-23. doi:10.1007/1-4020-5020-8_1
38. Madden, L. V., Hughes, G., and van den Bosch, F. (2007). The Study ofPlant Disease Epidemics. St. Paul, MN: American Physiological Society.
39. McPherson, J. M., Jetz, W., and Rogers, D. J. (2004). The effects of species’ range sizes on the accuracy of distribution models: ecological phenomenon or statistical artefact? J. Appl. Ecol. 41, 811-823. doi:10.1111/j.0021-8901.2004.00943.x
40. Mehra, L. K., Cowger, C., Weisz, R., and Ojiambo, P. S. (2015). Quantifying the effects of wheat residue on severity of Stagonospora nodorum blotch and yield in winter wheat. Phytopathology 105, 1417-1426. doi:10.1094/PHYTO-03-15-0080-R
41. Milus, E. A., and Chalkley, D. B. (1997). Effect of previous crop, seedborne inoculum, and fungicides on development of Stagonospora blotch. Plant Dis. 81, 1279-1283. doi:10.1094/PDIS.1997.81.11.1279
42. Monserud, R. A., and Leemans, R. (1992). Comparing global vegetation maps with the Kappa statistics. Ecol. Model. 62, 275-293. doi:10.1016/0304-3800(92)90003-W
43. Nelson, B. D. (2001). “Variable reduction for modeling using PROC VARCLUS,” in Proceedings of the Twenty-Sixth Annual SAS Users Group International Conference, Cary, NC, Available at: http://www2.sas.com/proceedings/sugi26/p261-26.pdf [Accessed on 10th September, 2015]
44. Olatinwo, R. O., Paz, J. O., Brown, S. L., Kemerait, R. C., Culbreath, A. K., Beasley, J. P., et al. (2008). A predictive model for spotted wilt epidemics in peanut based on local weather conditions and the Tomato spotted wilt virus risk. Phytopathology 98, 1066-1074. doi:10.1094/PHYTO-98-10-1066
45. Ottenbacher, K., Linn, R. T., Smith, P. M., Illig, S. B., Mancuso, M., and Granger, C. V. (2004). Comparison of logistic regression and neural network analysis applied to predicting living setting after hip fracture. Ann. Epidemiol. 14, 551-559. doi:10.1016/j.annepidem.2003.10.005
46. Paul, P. A., and Munkvold, G. P. (2004). A model-based approach to preplanting risk assessment for gray leaf spot of maize. Phytopathology 94, 1350-1357. doi:10.1094/PHYTO.2004.94.12.1350
47. Paul, P. A., and Munkvold, G. P. (2005). Regression and artificial neural network modeling for the prediction of gray leaf spot of maize. Phytopathology 95, 388-396. doi:10.1094/PHYT0-95-0388
48. Philibert, A., Desprez-Loustau, M., Fabre, B., Frey, P., Halket, F., Husson, C., et al. (2011). Predicting invasion success of forest pathogenic fungi from species traits. J. Appl. Ecol. 48, 1381-1390. doi:10.1111/j.1365-2664.2011.02039.x
49. Recknagel, F. (2001). Applications of machine learning to ecological modelling. Ecol. Model 146, 303-310. doi:10.1016/S0304-3800(01)00316-7
50. Ripley, B. D. (1996). Pattern Recognition andNeuralNetworks. London: Cambridge UniversityPress.
51. Rojas, R. (1996). Neural Networks: A Systematic Introduction. New York, NY: Springer-Verlag.
52. Rosso, P. H., and Hansen, E. M. (2003). Predicting Swiss needle cast disease distribution and severity in young Douglas-fir plantations in coastal Oregon. Phytopathology93, 790-798. doi:10.1094/PHYT0.2003.93.7.790
53. Sarle, W. S. (2002). Neural Network FAQ, part 1 of 7: Introduction. Documented online as FAQ 1473. Cary, NC: SAS Institute. Available at: ftp://ftp.sas.com/pu b/neural/FAQ.html.
54. Scherm, H., Ngugi, H. K., and Ojiambo, P. S. (2006). Trends in theoretical plant epidemiology. Eur. J. PlantPathol. 115, 61-73. doi:10.1007/1-4020-5020-8_5
55. Schielzeth, H. (2010). Simple means to improve the interpretability of regression coefficients. Methods Ecol. Evol 1, 103-113. doi:10.1111/j.2041-210X.2010.00012.x
56. Shaner, G., and Buechley, G. (1995). Epidemiology of leaf spot of soft red winter wheat caused by Septoria tritici and Stagonospora nodorum. Plant Dis. 79, 928-938. doi:10.1094/PD-79-0928
57. Solomon, P. S., Lowe, R. G. T., Tan, K.-C., Waters, O. D. C., and Oliver, R. P. (2006). Stagonospora nodorum: cause of Stagonospora nodorum blotch of wheat. Mol. PlantPathol. 7, 147-156. doi:10.1111/j.1364-3703.2006.00326.x
58. Stover, R. W., Francl, L. J., and Jordahl, J. G. (1996). Tillage and fungicide management of foliar diseases in a spring wheat monoculture. J. Prod. Agric. 9, 261-265. doi:10.2134/jpa1996.0261
59. Stubbs, T. L., Kennedy, A. C., and Schillinger, W. F. (2004). Soil ecosystem changes during the transition to no-till cropping. J. Crop Improv. 11, 105-135. doi:10.1300/J411v11n01_06
60. Su, X., Tsai, C., Wang, H., Nickerson, D. M., and Li, B. (2009). Subgroup analysis via recursive partitioning. J. Mach. Learn. Res. 10, 141-158.
61. Svetnik, V., Liaw, A., Tong, C., Culberson, J. C., Sheridan, R. P., and Feuston, B. P. (2003). Random forest: a classification and regression tool for compound classification and QSAR modeling. J. Chem. Inform. Comput. Sci. 43, 19471958. doi:10.1021/ci034160g
62. Uddin, W., Serlemitsos, K., and Viji, G. (2003). A temperature and leaf wetness duration-based model for prediction of gray leaf spot of perennial ryegrass turf. Phytopathology 93, 336-343. doi:10.1094/PHYTO.2003.93.3.336
63. Venables, W. N., and Ripley, B. D. (1999). Modern Applied Statistics with S-Plus, 3rd Edn. NewYork, NY: Springer.
64. Verreet, J. A., and Hoffmann, G. M. (1990). A biologically oriented threshold decision model for control of epidemics of Septoria nodorum in wheat. Plant Dis. 74, 731-738. doi:10.1094/PD-74-0731
65. Weisz, R. (2013). Small Grain Production Guide. North Carolina Cooperative Extension Service, Publication No. AG-580. Raleigh, NC: North Carolina State University.
66. Weisz, R., Cowger, C., Ambrose, G., and Gardner, A. (2011). Multiple mid-Atlantic field experiments show no economic benefit to fungicide application when fungal disease is absent in winter wheat. Phytopathology 101, 323-333. doi:10.1094/PHYTO-03-10-0096
67. Wollenhaupt, N. C., and Pingry, J. (1991). Estimating Residue using the Line-transectMethod. PublicationNo. A3553. Madison,WI: University ofWisconsin.
68. Zadoks, J. C., Chang, T. T., and Konzak, C. F. (1974). A decimal code for the growth stages of cereals. Weed Res. 14, 415-421. doi:10.1111/j.1365-3180.1974.tb01084.x