Development and Validation of Bayesian Network Method for Decision-Support System of Insect-Pest Management in Tomato

Authors

  • Singh N Dept. of Computer Application, Manav Rachna International Institute of Research and Studies, Faridabad-121004, India
  • Gupta N Dept. of Computer Application, Manav Rachna International Institute of Research and Studies, Faridabad-121004, India

DOI:

https://doi.org/10.26438/ijcse/v7i7.320325

Keywords:

Fruit borer, Leaf minor, Tomato, Decision-making, ICT, DSS, Pest Management

Abstract

Bayesian Network (BN), a probabilistic reasoning approach have been widely used in ecological decision-making to deal with uncertain information nevertheless, very few instances of its usage in crop pest management. This paper focuses on how to deal with uncertain agro-ecological information for decision–making in pest management. In the study, a Bayesian network was developed for selecting appropriate management option of fruit borer (Helicoverpa armigera) and leaf minor (Liriomyza trifolii), key insect-pest of tomato based on the tentative agro-ecological information besides crop condition that farmers provided. Validation of the method resulted in 76% accuracy for fruit borer and 82% for leaf minor. Application of the method thus developed in Decision Support Systems (DSSs) of agriculture with applies Information and Communication Technology (ICT) would automate and speed up the process of providing insect-pest management decision support to the farmers. Thus, it will not only save the crop worth crores of rupees but also help in reduction of excessive and irrational usage of pesticides thus saving the environment and human health.

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Published

2019-07-31
CITATION
DOI: 10.26438/ijcse/v7i7.320325
Published: 2019-07-31

How to Cite

[1]
N. Singh and N. Gupta, “Development and Validation of Bayesian Network Method for Decision-Support System of Insect-Pest Management in Tomato”, Int. J. Comp. Sci. Eng., vol. 7, no. 7, pp. 320–325, Jul. 2019.

Issue

Vol. 7 No. 7 (2019): IJCSE July Edition

Section

Research Article

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