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Decision Making in Integrated Pest Management and Bayesian Network


Affiliations
1 ManavRachna International University, Faridabad-121004, India
 

Timely availability of expert support to the farmers for appropriate decision-making on 'whether and what pest management option is required' is imperative for effective Integrated Pest Management (IPM). For several decades Economic Threshold Level (ETL) has been the basis for decision-making but in modern IPM emphasis is given on agro-ecological situation wherein IPM decisions are based on large range of pest relevant information such as crop health, natural enemies, weather etc. beside pest incidence scientifically obtained through farmers' field scouting. But large farming community in India can rather obtain the tentative information of this kind, consisting uncertainties. Bayesian Network (BN), anartificial intelligence approach could help in developing technique/model to deal with tentative pest relevant information which can be used in field scouting based IPM Decision Support Systems (DSSs) to automate the process of advising appropriate pest management option to the farmers on the basis of tentative agro-ecological situation of their fields.

Keywords

IPM, Field Scouting, Decision-Making, ETL, Agro-Ecological Situation.
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  • Chunguang Bi and Guifen Chen (2010). Bayesian Networks Modeling for Crop Diseases. Computer and Computing Technologies in Agriculture IV, Vol.344; 312-32.

  • Fabre, F., Pierre J.S., Dedryver C.A. and Plantegenest M. (2006). Barley yellow low dwarf disease risk assessment based on Bayesian modelling of aphid population dynamics. Ecological Modelling, 193: 457-466.

  • Isabel M. delAguila and Jos e del Sagrado (2012). Metamodeling of Bayesian networks for decision-support systems development, presented in workshop Knowledge Engineering and Software Engineering (KESE8) in ECAI 2012, At Montpelier.

  • March J.G. Primer on Decision Making: How Decisions Happen(1994). The Free Press, New York, NY, USA; 289:

  • Mead R., J. Paxton and R. Sojda (2006). Applications of Bayesian Networks in ecological modelling, The Second IASTED International Conference on Environmental Modelling and Simulation. St. Thomas, Virgin Islands

  • PetrosDamos (2015). Modular structure of web-based decision support systems for integrated pest management. A review. Agronomy for Sustainable Development, 35:1347–1372

  • Pan Heping, McMichael Daniel (1998). Fuzzy Causal Probabilistic Networks - A New Ideal and Practical Inference Engine. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1. 1.39.1482 rep=rep1&type=pdf.

  • Rivot, E., Prevost E., Parent E. and Bagliniere J.L. (2004). A Bayesian state-space modelling framework for fitting a salmon stage-structured population dynamic model to multiple time series of field data. Ecological Modelling, 179(4): 463-485.

  • Rossi V., Caffi T. and Salinari F. (2012). Helping farmers face the increasing complexity of decision-making for crop protection. PhytopathologiaMediterranea 51(3), pp. 457-479.

  • Satyagopal, K., S.N. Sushil, P. Jeyakumar, G. Shankar, O.P. Sharma, S.K. Sain, D.R. Boina, D. Chattopadhyay, B.S. Sunanda, Ram Asre, K.S. Kapoor, Sanjay Arya, Subhash Kumar, C.S. Patni, Dhanapal, A.N. Sabalpara, S.K. Beura, R.K. Mesta, Biju, B.G. Naik, J. Halder, S. Saha. (2014). AESA based IPM package for Watermelon, pp 40.

  • Serena H. Chen and Carmel A. Pollino(2012). Good Practice in Bayesian Network Modelling. Environmental Modelling & Software, 37: 134-145.


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  • Decision Making in Integrated Pest Management and Bayesian Network

Abstract Views: 405  |  PDF Views: 157

Authors

Niranjan Singh
ManavRachna International University, Faridabad-121004, India
Neha Gupta
ManavRachna International University, Faridabad-121004, India

Abstract


Timely availability of expert support to the farmers for appropriate decision-making on 'whether and what pest management option is required' is imperative for effective Integrated Pest Management (IPM). For several decades Economic Threshold Level (ETL) has been the basis for decision-making but in modern IPM emphasis is given on agro-ecological situation wherein IPM decisions are based on large range of pest relevant information such as crop health, natural enemies, weather etc. beside pest incidence scientifically obtained through farmers' field scouting. But large farming community in India can rather obtain the tentative information of this kind, consisting uncertainties. Bayesian Network (BN), anartificial intelligence approach could help in developing technique/model to deal with tentative pest relevant information which can be used in field scouting based IPM Decision Support Systems (DSSs) to automate the process of advising appropriate pest management option to the farmers on the basis of tentative agro-ecological situation of their fields.

Keywords


IPM, Field Scouting, Decision-Making, ETL, Agro-Ecological Situation.

References