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

Published online by Cambridge University Press:  12 December 2011

P.D. Kamala Jayanthi ,
Abraham Verghese  and
P.D. Sreekanth
P.D. Kamala Jayanthi*
Affiliation:
Division of Entomology and Nematology, Indian Institute of Horticultural Research, Hesseraghatta Lake PO, Bangalore560089, Karnataka, India
Abraham Verghese
Affiliation:
Division of Entomology and Nematology, Indian Institute of Horticultural Research, Hesseraghatta Lake PO, Bangalore560089, Karnataka, India
P.D. Sreekanth
Affiliation:
Division of Information and Communication Management, National Academy of Agricultural Research Management, Rajendranagar, Hyderabad500407, India
*
*E-mail: [email protected]
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Abstract

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.

Keywords

oriental fruit flyBactrocera dorsalisprediction modelartificial neural networks
Type
Research Paper
Information
International Journal of Tropical Insect Science , Volume 31 , Issue 4 , December 2011 , pp. 205 - 211
Copyright
Copyright © ICIPE 2011

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