Prediction model for rice stem borer based on L-M optimized algorithm and its preliminary application

Rice stem borer is a pest, pest emergence volume is related to different climatic factors. The prediction of pest emergence volume is a problem of multi-variable and non-linearity. The artificial neural network is a nonlinear optimization tool. However, the training of neural network by conventional back-propagation(BP) method has intrinsic vulnerable weakness in slow convergence and local minima, L-M optimized algorithm not only possesses the advantages of artificial neural network, but also offsets disadvantages caused by the BP- neural network. On the basis of this, the project selected L-M optimized algorithm as modeling method for prediction of pest emergence volume in rice planting area. The experimental results by MATLAB show that this method is effective and more accurate than BP neural network, and it can be applied for prediction of pest emergence.