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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: jaiinsect@gmail.com
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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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References

Abdusselam, A. (2007) Forecasting surface water level fluctuations of Lake Van by artificial neural networks. Water Resources Management 21, 399–408.Google Scholar
Affandi, K. A., Kunio, W. and Haryadi, T. (2007) Application of an artificial neural network to estimate groundwater level fluctuation. Journal of Spatial Hydrology 7, 17–32.Google Scholar
Ali, I., Ullah, F. and Khan, S. A. (1999) Efficacy of various insecticides and trap heights in methyl eugenol traps against fruit flies (Bactrocera spp.). Sarhad Journal of Agriculture 15, 589–594.Google Scholar
Anjum, S., Razag, M. and Yazadni, M. S. (2000) Studies on seasonal activity and control of fruit flies (Dacus spp.) on mango (Mangifera indica L.) at Faisalabad, Pakistan. Arab Journal of Plant Protection 18, 121–123.Google Scholar
Butani, D. K. (1979) Insects and Fruits. Periodical Expert Book Agency, Delhi, India. 415 pp.Google Scholar
Cornelius, M. L., Duan, J. J. and Messing, R. H. (1999) Capture of oriental fruit flies (Diptera: Tephritidae) by protein-baited traps and fruit-mimicking visual traps in a guava orchard. Environmental Entomology 28, 1140–1144.CrossRefGoogle Scholar
Cornelius, M. L., Nergel, L., Duan, J. J. and Messing, R. H. (2000) Responses of female oriental fruit flies (Diptera: Tephritidae) to protein and host fruit odors in field cage and open field tests. Environmental Entomology 29, 14–19.Google Scholar
Drew, R. A. I. and Hancock, D. L. (1994) The Bactrocera dorsalis complex of fruit flies (Diptera: Tephritidae: Dacinae) in Asia. Bulletin of Entomological Research Suppl. 2: 1–68.Google Scholar
Hashimoto, Y. (1997) Applications of artificial neural networks and genetic algorithms to agricultural systems. Computers and Electronics in Agriculture 18, 71–72.CrossRefGoogle Scholar
Hasyim, A., Muryati, and de Kogel, W. J. (2008) Population fluctuation of adult males of the fruit fly, Bactrocera tau Walker (Diptera: Tephritidae) in passion fruit orchards in relation to abiotic factors and sanitation. Indonesian Journal of Agricultural Sciences 9, 29–33.CrossRefGoogle Scholar
Haykin, S. (2001) Neural Networks: A Comprehensive Foundation. Prentice Hall, New Jersey, NJ. 842 pp.Google Scholar
Kamala Jayanthi, P. D. and Verghese, A. (2011) Host-plant phenology and weather based forecasting models for population prediction of the oriental fruit fly, Bactrocera dorsalis Hendel. Crop Protection 30, 1557–1562.Google Scholar
Lin-nan, Y., Lin, P., Li-min, Z., Li-lian, Z. and Shi-sheng, Y. (2009) A prediction model for population occurrence of paddy stem borer (Scirpophaga incertulas), based on Back Propagation Artificial Neural Network and Principal Components Analysis. Computers and Electronics in Agriculture 68, 200–206.Google Scholar
Little, T. M. and Hills, F. J. (1978) Agricultural Experimentation (Design and Analysis). John Wiley, New York, NY. 368 pp.Google Scholar
Madhura, H. S. and Viraktamath, C. A. (2001) Selective response of fruit flies (Diptera: Tephritidae) traps at different heights, pp. 29–30. In Integrated Pest Management in Horticultural Crops: Different New Molecules, Biopesticides and Environment. Proceedings of the Second National Symposium on Integrated Pest Management in Horticultural Crops, 17–19 October 2001, organized by the Association for Advancement of Pest Management in Horticultural Ecosystems (AAPMHE) (edited by Verghese, A. and Reddy, P. P.). Indian Institute of Horticultural Research, Bangalore.Google Scholar
Nash, J. E. and Sutcliffe, J. V. (1970) River flow forecasting through conceptual models. Part I – a discussion of principles. Journal of Hydrology 10, 282–290.CrossRefGoogle Scholar
Pinero, J. C., Mau, R. F. L. and Vargas, R. I. (2009) Managing oriental fruit fly, Bactrocera dorsalis (Diptera: Tephritidae), through spinosad-based protein bait sprays and sanitation in papaya orchards in Hawaii. Journal of Economic Entomology 102, 1123–1132.Google Scholar
Rajat, G., Narayana, B. V. L., Krishna Reddy, P., Ranga Rao, G. V., Gowda, C. L. L., Reddy, Y. V. R. and Rama Murthy, G. (2003) Understanding Helicoverpa armigera pest population dynamics related to chickpea crop using neural networks, pp. 723–726. In Proceedings of the Third IEEE International Conference on Data Mining (ICDM’03). The Institute of Electrical and Electronics Engineers (IEEE), New York, NY.CrossRefGoogle Scholar
Sharov, A. A. (1996) Modelling forest insect dynamics, pp. 293–303. In Caring for the Forest: Research in A Changing World (edited by Korpilahti, E., Mukkela, H. and Salonen, T.). Congress Report, Vol. II, IUFRO XX World Congress, 6–12 August 1995, Tampere, Finland. Gummerus Printing, Jyvaskyla.Google Scholar
Shukla, R. P. and Prasad, V. G. (1985) Population fluctuations of the oriental fruit fly, Dacus dorsalis, Hendel, in relation to hosts and abiotic factors. Tropical Pest Management 31, 273–275.Google Scholar
Sreekanth, P. D., Geethanjali, N., Sreedevi, P. D., Shakeel, A., Ravi Kumar, N and Jayanthi, P. D. K. (2009) Forecasting groundwater level using artificial neural networks. Current Science 96, 933–939.Google Scholar
Verghese, A. and Kamala Jayanthi, P. D. (2001) Integrated pest management in fruits, pp. 1–23. In Pest Management in Horticultural Ecosystems (Edited by Parvatha Reddy, P., Verghese, A. and Krishna Kumar, N. K.). Capital Publishing Company, New Delhi.Google Scholar
Verghese, A., Nagaraju, D. K. and Jayanthi Mala, B. R. (2006 a) A farmer friendly trap for the management of the fruit fly Bactrocera spp. (Tephritidae: Diptera). Pest Management in Horticultural Ecosystems 12, 164–167.Google Scholar
Verghese, A., Nagaraju, D. K., Madhura, H. S., Jayanthi, P. D. K. and Sreedevi, K. (2006 b) Wind speed as an independent variable to forecast the trap catch of the fruit fly (Bactrocera dorsalis). Indian Journal of Agricultural Sciences 76, 172–175.Google Scholar
Verghese, A. and Sudhadevi, K. (1998) Relation between trap catch of Bactrocera dorsalis Hendel and abiotic factors, pp. 15–18. In Advances in IPM for Horticultural Crops. Proceedings of the First National Symposium on Pest Management in Horticultural Crops: Environmenatl Implicatins and Thrusts (edited by Verghese, A., Kumar, N. K. K. and Parvatha Reddy, P.). Association for Advancement of Pest Management in Horticultural Ecosystems, Bangalore.Google Scholar
Verghese, A., Tandon, P. L. and Stonehouse, J. (2004) Economic evaluation of the integrated management of the oriental fruit fly Bactrocera dorsalis (Diptera: Tephritidae) in mango in India. Crop Protection 23, 61–63.Google Scholar