Rice varieties classification via two-stage weighted ELM method

Rice production is the key factor to ensure food security. Rice varieties classification is an important means to improve rice quality. Considering the problem that traditional machine learning methods lack analytic solutions and can not adapt parameters. This paper implements the ELM method based on two-stage weights. In the initial stage, the weights of the output layer can be calculated according to the Moore-Penrose generalized inverse. Then regular function balances the expectation error and model complexity. In the model optimized stage, we calculated the model weights of the new function according to the predictive performance. These strategies help to reduce the fitting error of sequence fragments. Numerical experiment results are proved at the University of California Irvine data set, the results show that the proposed method not only has better generalization performance, but also guarantees the prediction effect of contrast method for different scenes.