Gaussian Mass Function Based Multiple Model Fusion for Apple Classification

Near-infrared spectra can be used to predict the internal quality of apple non-destructively, such as Soluble Solids Content (SSC), acidity and so on. However, it needs to establish a prediction model. And for improving the predictive accuracy, some pre-processing methods should be adopted. In this paper, Apples’ SSC is considered as a representative index, the Probabilistic Neural Network (PNN) and Extreme Learning Machine (ELM) models are established. After carrying out the Multiple Scattering Correction (MSC), which is to reduce the baseline drift, the classification accuracies of both models are 81.8182 \(\%\) and 77.2727 \(\%\) respectively. For avoiding the limitation of single classification model, and dealing with the uncertainty introduced by hard partition of the instance space, an evidence theory based multiple model fusion is proposed. Especially, the mass function generation is considered. A Gaussian mass function is proposed so as to realize the fusion of PNN and ELM models by combining the mass function based on Dempster’s combination rules of evidence theory. The experimental results show that the accuracy of fusion model is 86.3636 \(\%\) , which demonstrate that Gaussian mass function is suitable for apples’ multi-model fusion.