International Journal of Tropical Insect Science

, Volume 31, Issue 4, pp 205–211 | Cite as

Predicting the oriental fruit fly Bactrocera dorsalis (Diptera: Tephritidae) trap catch using artificial neural networks: a case study

  • P. D. Kamala JayanthiEmail author
  • Abraham Verghese
  • P. D. Sreekanth


The oriental fruit fly Bactrocera dorsalis (Hendel) is a very serious pest of fruit trees, causing enormous economic losses globally. The present study examines the capability of an artificial neural network (ANN) with a Quasi-Newton (QN) algorithm to predict a fruit fly trap catch and compare the results with those of a traditional regression model. MATLAB 7.0 was used to develop ANN programming and the fortnightly measurement of 14 input variables (abiotic along with biotic variables) provided the database for analysing the ANN model. An input model using a total of 14 identified input nodes with a selected QN-ANN structure (14-25-20-1) gave an optimum result. In general, the present study showed that an ANN could be used to estimate fruit fly trap catch with enhanced accuracy (R2 = 0.92; root mean square error (RMSE) = 23.75; Nash-Sutcliffe efficiencies = 0.99) over traditional regression models (R2 = 0.76; RMSE = 30.28; Nash-Sutcliffe efficiencies = 0.76). This finding helps the region-specific fruit fly monitoring and management programmes that lack long-term historic data.

Key words

oriental fruit fly Bactrocera dorsalis prediction model artificial neural networks 


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Copyright information

© ICIPE 2011

Authors and Affiliations

  • P. D. Kamala Jayanthi
    • 1
    Email author
  • Abraham Verghese
    • 1
  • P. D. Sreekanth
    • 2
  1. 1.Division of Entomology and NematologyIndian Institute of Horticultural ResearchBangaloreIndia
  2. 2.Division of Information and Communication ManagementNational Academy of Agricultural Research ManagementHyderabadIndia

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