• Jiake Lv
  • Xuan Wang
  • Deti Xie
  • Chaofu Wei
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 294)


Forecasting pests emergence levels plays a significant role in regional crop planting and management. The accuracy, which is derived from the accuracy of the forecasting approach used, will determine the economics of the operation of the pests prediction. Conventional methods including time series, regression analysis or ARMA model entail exogenous input together with a number of assumptions. The use of neural networks has been shown to be a cost-effective technique. But their training, usually with back-propagation algorithm or other gradient algorithms, is featured with some drawbacks such as very slow convergence and easy entrapment in a local minimum. This paper presents a hybrid approach of neural network with particle swarm optimization for developing the accuracy of predictions. The approach is applied to forecast Alternaria alternate Keissl emergence level of the WuLong Country, one of the most important tobacco planting areas in Chongqing. Traditional ARMA model and BP neural network are investigated as comparison basis. The experimental results show that the proposed approach can achieve better prediction performance.


Root Mean Square Error Particle Swarm Optimization Hybrid Approach Hide Node Back Propagation Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Ahn, B. S. and S. S. Cho, et al.. (2000). “The integrated methodology of rough set theory and artificial neural network for business failure prediction.” Expert Systems with Applications 18 (2): 65–74.CrossRefGoogle Scholar
  2. Carpinteiro and A. S. Otavio, et al.. (2004). “A hierarchical neural model in short-term load forecasting.” Applied soft computing 4 (4): 405–412.CrossRefGoogle Scholar
  3. Clerc, M. (1999). The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. Proceeding Congress on Evolutionary Computation, Washington DC.Google Scholar
  4. Drake, A. (2001). “Use of remote sensing and ANN in prediction of pests in Queensland.” Remote sensing of environment 12 (4): 32–35.MathSciNetGoogle Scholar
  5. Eberhart, R. C. and Y. Shi (2000). Comparing Inertia Weights and Constriction Factors in Particle swarm optimization. 2000 congress on Evolutionary Computing.Google Scholar
  6. Hippert, H. and C. Pedreira, et al.. (2001). “Neural networks for short-term load forecasting: a review and evaluation.” IEEE Trans Power System 16 (1): 45–55.Google Scholar
  7. Kennedy, J. (1997). The particle swarm: Social adaptation of knowledge. Proceedings of the 1997 International Conference on Evolutionary Computation, Indianapolis.Google Scholar
  8. Nabney, I. T. (2002). NETLAB:Algorithms for pattern recognition. London, Springer.MATHGoogle Scholar
  9. Park, Y. and Y. Chung (2006). “Hazard rating of pine trees from a forest insect pest using artificial neural networks.” Forest ecology and management 222: 222–233.CrossRefGoogle Scholar
  10. Reichert, P. and M. Omlin (1997). “On the usefulness of over parameterized ecological models.” Ecology modeling 95: 289–299.CrossRefGoogle Scholar
  11. Roditakis, N. E. and M. G. Karandinons (2001). “Effects of photoperiod and temperature on pupil diapause induction of grape berry moth lobelia botrana.” Physiol Entomol 26: 329–340.CrossRefGoogle Scholar
  12. Russell, S. and P. Norvig (2003). Artificial intelligence: A modern approach, Prentice-Hall International Inc.Google Scholar
  13. Satake, A. and T. Ohgushi, et al.. (2006). “Modeling population dynamics of a tea pest with temperature-dependent development: predicting emergence timing and potential damage.” Ecology research 21: 107–116. CrossRefGoogle Scholar
  14. Shaffer, P. L. and H. J. Gold (1985). “A simulation model of population dynamics of the coding moth cydia pomonella.” Ecology modeling 30: 247–274.CrossRefGoogle Scholar
  15. Shi, Y. and R. C. Eberhart (1998). A modified particle sarm optimizer. IEEE World Conf on Computation Intelligence.Google Scholar
  16. Shi, Y. and R. C. Eberhart (1999). Empirical study of Particle Swarm Optimization. IEEE World Conference on Evolutionary Computation.Google Scholar
  17. Tourenq, C. and S. Aulaginer, et al. (1999). “Use of artificial neural networks for predicting rice crop damage by greater flamingos in the Camargue, France.” 120 (2–3): 349–358.Google Scholar
  18. WANG, N. (1999). “The research of hybrid optimization strategy in neural networks.” The Journal of Tsinghua University: 66–70.Google Scholar
  19. Wang, G. (2004). “Advances and outlook for forecast work of tobacco disease and insect pests in China.” Journal of China tobacco Science 1: 44–46.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Chongqing Key laboratory of digital agricultureChongqingChina
  2. 2.Southwest UniversityChongqingChina
  3. 3.Southwest UniversityChongqingChina

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