Weather-based prediction of anthracnose severity using artificial neural network models

Plant Pathology

Volumn 53, Issue 4, 2004, Pages 375-386

  Chakraborty, S ^a   Ghosh, R ^b   Ghosh, M ^b   Fernandes, C D ^c   Charchar, M J ^c   Kelemu, S ^d  

^a UNIVERSITY OF QUEENSLAND   (Australia)

^b UNIVERSITY OF BALLARAT   (Australia)

^c EMBRAPA CERRADOS   (Brazil)

^d Ctro Internacional de Agric Trop   (Colombia)

Author keywords

Anthracnose; Colletotrichum gloeosporioides; Disease risk and severity; Multiple linear regression analysis; Stylosanthes scabra

Indexed keywords

ANTHRACNOSE; ARTIFICIAL NEURAL NETWORK; DISEASE SEVERITY; PASTURE; WEATHER;

AUSTRALASIA; AUSTRALIA; BRAZIL; COLOMBIA; SOUTH AMERICA;

COLLETOTRICHUM; FUNGI; GLOMERELLA CINGULATA; PLANALTINA; STYLOSANTHES; STYLOSANTHES SCABRA;

EID: 4444333654     PISSN: 00320862     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.1365-3059.2004.01044.x     Document Type: Article

References (35)

1. Battiti R, 1992. First and second order methods for learning: between steepest descent and Newton's method. Neural Computation 4, 141-66.
2. Bishop CM, 1995. Neural Networks for Pattern Recognition. Oxford, UK: Oxford University Press.
3. Cambell CL, Madden LV, 1990. Introduction to Plant Disease Epidemiology. New York, USA: John Wiley & Sons.
4. Chakraborty S, 1990. Expression of quantitative resistance to Colletotrichum gloeosporioides in Stylosanthes scabra at different inoculum concentrations and day-night temperatures. Australian Journal of Agricultural Research 41, 89-100.
5. Chakraborty S, Billard L, 1995. Quantitative relationships between Colletotrichum gloeosporioides infection of Stylosanthes scabra and weather factors under field conditions. Plant Pathology 44, 63-72.
6. 2 under a changing climate? New Phytologist 159, 733-42.
7. Chakraborty S, Smyth GKS, 1995. A stochastic model incorporating the effect of weather conditions on anthracnose development in Stylosanthes scabra. Journal of Phytopathology 143, 495-9.
8. Chakraborty S, Cameron DF, Irwin JAG, Edye LA, 1988. Quantitatively expressed resistance to anthracnose (Colletotrichum gloeosporioides) in Stylosanthes scabra. Plant Pathology 37, 529-37.
9. Chakraborty S, McKay FJ, Ratcliff D, 1990. Anthracnose of Stylosanthes scabra: effect of leaf surface wetness on disease severity. Plant Disease 74, 379-84.
10. Chakraborty S, Fernandes CD, Charchar M, Jd'A, Thomas M, 2002. Pathogenic variation in Colletotrichum gloeosporioides infecting Stylosanthes spp. at its centre of diversity in Brazil. Phytopathology 92, 553-62.
11. Chtioui Y, Panigrahi S, Francl L, 1999. A generalized linear regression neural network and its application for leaf wetness prediction to forecast plant disease. Chemometrics and Intelligent Laboratory Systems 48, 47-58.
12. Crisci A, Moriondo M, Orlandini S, 1998. Analysis of downy and powdery mildew infection: modelling and neural network approach. In: Proceedings of the 7th ICCTA - International Congress for Computer Technology in Agriculture, 1998. Florence, Italy.
13. Davis RD, Irwin JAG, Cameron DF, Shepherd RK, 1987a. Epidemiological studies on the anthracnose diseases of Stylosanthes spp. caused by Colletotrichum gloeosporioides in north Queensland and pathogenic specialization within the natural fungal populations. Australian Journal of Agricultural Research 38, 1019-32.
14. Davis RD, Irwin JAG, Shepherd RK, Cameron DF, 1987b. Yield losses caused by Colletotrichum gloeosporioides in three species of Stylosanthes. Australian Journal of Agricultural Research 27, 67-72.
15. De Wolf ED, Francl LJ, 1997. Neural networks that distinguish infection periods of wheat tan spot in an outdoor environment. Phytopathology 87, 83-7.
16. Fitt BDL, McCartney HA, 1985. Spore dispersal in splash droplets. In: Ayres PG, Boddy L, eds. Water, Fungi and Plants. Cambridge, UK: Cambridge University Press, 87-104.
17. Francl LJ, Panigrahi S, 1997. Artificial neural network models of wheat leaf wetness. Agricultural and Forest Meteorology. 88, 57-65.
18. Frasconi P, Gori M, Tesi A, 1993. Successes and failures of back-propagation: a theoretical investigation. In: Omidvar O, ed. Progress in Neural Networks. Toronto, Canada: Ablex Publishing.
19. Friedman JH, 1991. Multivariate adaptive regression spline (with discussion). Annals of Statistics 19, 1-141.
20. Hajmeer M, Basheer I, 2003. Comparison of logistic regression and neural network-based classifiers for bacterial growth. Food Microbiology 20, 53-5.
21. Hardwick NV, 1998. Disease forecasting. In: Jones DG, ed. The Epidemiology of Plant Diseases. Dordrecht, the Netherlands: Kluwer Academic, 207-30.
22. Hertz J, Krogh A, Palmer RG, 1995. Introduction to the Theory of Neural Computation. Reading, UK: Addison-Wesley.
23. Irwin JAG, Cameron DF, Ratcliff D, 1984. Influence of environmental factors on the development of anthracnose diseases of Stylosanthes spp. Australian Journal of Agricultural Research 35, 473-8.
24. Lenné JM, Brown AE, 1991. Factors influencing the germination of pathogenic isolates of Colletotrichum gloeosporioides on leaf surfaces of Stylosanthes guianensis. Mycological Research 95, 227-32.
25. Louis I, Cooke RC, 1985. Conidial matrix and spore germination in some plant pathogens. Transactions of the British Mycological Society 84, 661-7.
26. Miles JW, Grof B, 1997. Recent advances in studies of anthracnose of Stylosanthes. III. Stylosanthes breeding approaches in South America. Tropical Grasslands 31, 430-4.
27. Pangga IB, 2002. Influence of climatic factors on plant architecture of Stylosanthes scabra and the epidemiology of the anthracnose disease. Brisbane, Australia: University of Queensland, PhD Thesis.
28. 2. Phytopathology 94, 221-7.
29. Scherm H, 2000. Simulating uncertainty in climate change models with fuzzy numbers. Environmental Pollution 108, 373-9.
30. Shaw MW, 1994. Modelling stochastic processes in plant pathology. Annual Review of Phytopathology 32, 523-44.
31. Specht DF, 1991. A general regression neural network. IEEE Transactions on Neural Networks 2, 568-72.
32. Taylor MC, Hardwick NV, Bradshaw NJ, Hall AM, 2003. Relative performance of five forecasting schemes for potato late blight (Phytophthora infestans) I. Accuracy of infection warnings and reduction of unnecessary, theoretical, fungicide applications. Crop Protection 22, 275-83.
33. Weeds PL, Chakraborty S, Fernandes CD, Charchar M, Jd'A, Ramesh CR, Kexian Y, Kelemu S, 2003. Genetic diversity in Colletotrichum gloeosporioides from Stylosanthes spp. at centres of origin and utilization. Phytopathology 93, 176-85.
34. Yang XB, Batchelor WD, Tshanz AT, 1995. A neural network model to predict soybean rust. Phytopathology 85, 1172.
35. Yang CC, Prasher SO, Sreekanth S, Patni NK, Masse L, 1997. An artificial neural network model for simulating pesticide concentrations in soil. Transactions of the ASAE 40, 1285-94.