Research of ordinal model fusion based apple grading

Abstract Near infrared spectrum has been applied for the rapid non-destructive prediction and classification of the internal soluble solids content (SSC) of apples, due to its rapid, non-destructive and non-polluting nature. However, current apple grading methods do not make full use of the orderliness relationship existing in the apple grading labels. Therefore, ordinal model is introduced in the issue of apples grading based on the internal SSC. In details, the orderliness in the classification model is considered and ordinal regression is combined with apple classification model to establish ordered partition neural network and ordinal regression extreme learning machine, respectively. Meanwhile, to address the problems of poor applicability of single prediction model and the grading uncertainty associated with compulsory segmentation of grading boundaries, a Gaussian mass function generating method is proposed based on the distance between the predicted ordinal class labels and the real grading boundaries, and the multiple models can be fused through the Dempster combination rule, making a fuller description of the uncertainty problem on ordinal class labels prediction, providing a research basis for online non destructive testing grading of apples. Finally, the multiple model fusion process is fully demonstrated by simulation examples, the grading accuracy of Yantai Red Fuji apples is also improved than single ordinal model.