Phytopathology Prediction in Dry Soil Using Artificial Neural Networks Modeling

Authors: F. Allag, S. Bouharati, M. Belmahdi, R. Zegadi

Abstract:

The rapid expansion of deserts in recent decades as a result of human actions combined with climatic changes has highlighted the necessity to understand biological processes in arid environments. Whereas physical processes and the biology of flora and fauna have been relatively well studied in marginally used arid areas, knowledge of desert soil micro-organisms remains fragmentary. The objective of this study is to conduct a diversity analysis of bacterial communities in unvegetated arid soils. Several biological phenomena in hot deserts related to microbial populations and the potential use of micro-organisms for restoring hot desert environments. Dry land ecosystems have a highly heterogeneous distribution of resources, with greater nutrient concentrations and microbial densities occurring in vegetated than in bare soils. In this work, we found it useful to use techniques of artificial intelligence in their treatment especially artificial neural networks (ANN). The use of the ANN model, demonstrate his capability for addressing the complex problems of uncertainty data.

Keywords: Desert soil, Climatic changes, Bacteria, Vegetation, Artificial neural networks.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1093868

References:

[1] Go´mez-Silva B, Rainey FA, Warren-Rhodes KA, McKay CP, Navarro-Gonza´lez R. Atacama Desert soil microbiology. In: Dion P, Nautiyal CS (eds) Microbiology of extreme soils. Springer, Berlin, pp 117–132. 2008.
[2] Bashan Y.;. de-Bashan L.E, Microbial Populations of Arid Lands and their Potential for Restoration of Deserts. P. Dion (ed.), Soil Biology and Agriculture in the Tropics, Soil Biology 21, DOI 10.1007/978-3-642-05076-3_6, # Springer-Verlag Berlin Heidelberg. 2010.
[3] Filar J. A. Mathematical models. In: Knowledge for Sustainable Systems. An Insight into the Encyclopedia of Life Support Systems, Vol., pp. 339–354, UNESCO Publishing-Eolss Publishers, Paris, France, Oxford, UK. 2002.
[4] Baxt WG, Skora J. 1996. Prospective validation of artificial neural network trained to identify acute myocardial infarction
[see comments]. Lancet; 347:12–15
[5] Buchman TG, Kubos KL, Seidler AJ, et al. A comparison of statistical and connectionist models for the prediction of chronicity in a surgical intensive care unit. Crit Care Med; 22:750–762. 1994.
[6] Ajith A. "Neuro Fuzzy Systems: state of Art Modelling Techniques”, In proceedings of the sixth international work conference on Artificial and Natural Neural Networks, IWANN 2001, Granada, Springer Verlag Germany, pp.269-276. 2001.
[7] Frize M, Solven FG, Stevenson M, et al. Computer-assisted decision support systems for patient management in an intensive care unit. Medinfo; 8:1009–1012. 1995.
[8] Doig GS, Inman KJ, Sibbald WJ, et al. Modeling mortality in the intensive care unit: comparing the performance of a backpropagation, associative-learning neural network with multivariate logistic regression. Proc Annu Symp Comput Applications Med Care; 361–365. 1993.
[9] Izenberg SD, Williams MD, Luterman A. Prediction of trauma mortality using a neural network. Am Surg; 63:275–281. 1997.
[10] William C., Hanson III, MD, FCCM, Bryan E., Marshall, MD, FRCP, FRCA. 2001. Artificial intelligence applications in the Ajith A. 2005. "Adaptation of Fuzzy Inference System Using Neural Learning”, Computer Science Department, Oklahoma State University, USA, springer verlag berlin Heidelberg, 2005.
[11] Ajith Abraham , Baikunth Nath. Hybrid Intelligent Systems Design -- A Review of a Decade of Research 2000.
[12] Ajith Abraham. Machine Intelligence. Journal of network and computer applications 28 (2), 167-182, 221, 2005.
[13] Nikam S.R.; Nikumbh P.J.; Kulkarni S.P. Fuzzy Logic And Neuro-Fuzzy Modeling. MPGI National Multi Conference 2012 (MPGINMC-2012) 7-8. 2012.
[14] Baxt WG. A neural network trained to identify the presence of myocardial infarction bases some decisions on clinical associations that differ from accepted clinical teaching. Med Decis Making; 14:217–222. 1994.