Bayesian network for decision-support on pest management of tomato fruit borer, H. armigera

  • Authors

    • Niranjan Singh Manav Rachna International University
    • Neha Gupta Manav Rachna International University
    2017-11-27
    https://doi.org/10.14419/ijet.v6i4.8583
  • Decision-Support, ETL, Fruit Borer (H. Armigera), Tomato, Pest Management.

  • Abstract

    Fruit borer (H. armigera) is the key pest of tomato, a prominent vegetable crop grown in northern plains of India. Timely availability of decision-support to the farmers on ‘whether and what management option is required’ is imperative for effective pest management. For decades, the pest economic threshold level (ETL) has been the basis to select an appropriate pest management option. This process requires quantitative information about pest activity, which needs to be scientifically observed in the farmers’ fields. However, a large section of the farming community is not able to scientifically obtain this kind of information. Moreover, in current pest management, decision-making depends upon a large range of agro-ecological information, besides pest activity. In this study, a Bayesian network-based method/model was devised for the selection of an appropriate management option for the effective management of fruit borer in tomato crop, based on tentative agro-ecological information, beside pest activity, that farmers provided. Thus, the resulting method can be used in decision support systems of agriculture with applies information and communication technology to automate and speed up the process of providing pest management decision-support to the farmers.

  • References

    1. [1] Bhagat S., Tripathi A. K., Ahmad I., Birah A., Sharma O. P. & Singh N (2015). Integrated Disease Management for Tomato in Island Ecosystem of Andaman. Indian Journal of Horticulture72 (1), 67-72. https://doi.org/10.5958/0974-0112.2015.00012.2.

      [2] Chen S. H. & Pollino C. A. (2012). Good Practice in Bayesian Network Modelling. Environmental Modelling & Software37,134-145. https://doi.org/10.1016/j.envsoft.2012.03.012.

      [3] Chunguang B. & Guifen, C.(2010). Bayesian Networks Modeling for Crop Diseases. Computer and Computing Technologies in Agriculture IV344, 312-320.

      [4] del Ãguila I. M. & del Sagrado J. (2012). Metamodeling of Bayesian Networks for Decision-Support Systems Development.Ä°n 8th Workshop on Knowledge Engineering and Software Engineering (KESE), Montpelier, USA: 949,12–19.

      [5] Jensen F. V. & Nielsen T. D. (2007). Bayesian Networks and Decision Graphs. 2nd ed.New York, NY, USA:Springer-Verlag. https://doi.org/10.1007/978-0-387-68282-2.

      [6] Kjrulff U. B. & Madsen A. L. (2008). Bayesian Networks and Influence Diagrams.New York, NY, USA:Springer-Verlag. https://doi.org/10.1007/978-0-387-74101-7.

      [7] Kragt M. E. (2009). A Beginners Guide to Bayesian Network Modelling for Integrated Catchment Management.(Technical Report No. 9, Landscape Logic). Department of Environment, Water Heritage and the Arts, Australia.

      [8] McCann R. K., Marcot B. G. & Ellis R. (2006). Bayesian Belief Networks: Applications in Ecology and Natural Resource Management. Canadian Journal of Forest Research36, 3053-3062. https://doi.org/10.1139/x06-238.

      [9] Meysam A. M. & Mahdi K. S. (2010) Bayesian Network and Pest Management: A Case Study of Trap Plants on Locust Population. Advances in Environmental Biology4(2), 147-151.

      [10] Pearl J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Francisco, CA: Morgan Kaufmann Publishers Inc.

      [11] Rossi V., Caffi T. & Salinari F. (2012). Helping Farmers Face the Increasing Complexity of Decision-Making For Crop Protection. Phytopathologia Mediterranea51(3), 457-479.

      [12] Satyagopal K., Sushil S. N., Jeyakumar P., Shankar G., Sharma O.P., Sain S. K., …Saha S. (2015). AESA Based IPM Package for Tomato.Hyderabad, AP: National Institute of Plant Health Management.

      [13] Singh K. (2013). Seasonal Abundance of Fruit Borer, Helicoverpa Armigera and Its Impact on Marketable Fruit Production in Tomato, Lycopersicon Esculentum (Mill.). Agriculture. Science Digest33(4), 247–252. https://doi.org/10.5958/j.0976-0547.33.4.019.

      [14] Singh N., Dotasara S. K., Jat S. M. & Naqvi A.R. (2017). Assessment of Crop Losses Due to Tomato Fruit Borer, Helicoverpa Armigera in Tomato. Journal of Entomology and Zoology Studies5(3), 595-597.

      [15] Singh N. & Gupta N. (2016). ICT Based Decision Support Systems for Integrated Pest Management (IPM) in India: A review. Agricultural Reviews37(4), 309-316.

      [16] Singh N. & Gupta N. (2017). Decision Making In Integrated Pest Management and Bayesian Network.International Journal of Computer Science & Information Technology9(2), 31-36. https://doi.org/10.5121/ijcsit.2017.9203.

      [17] Singh N., Sardana H. R. & Kumar S. S. (2012). Pest Management Information System (PMIS) for Tomato: A Database Tool for IPM. Indian Journal of Entomology74(2), 202–204.

      [18] Varis O. (1997). Bayesian Decision Analysis for Environmental and Resource Management. Environmental Modelling and Software 12,177–185. https://doi.org/10.1016/S1364-8152(97)00008-X.

  • Downloads

  • How to Cite

    Singh, N., & Gupta, N. (2017). Bayesian network for decision-support on pest management of tomato fruit borer, H. armigera. International Journal of Engineering & Technology, 6(4), 168-170. https://doi.org/10.14419/ijet.v6i4.8583

    Received date: 2017-10-31

    Accepted date: 2017-11-20

    Published date: 2017-11-27