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 
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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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