Bayesian Methods for Predicting the Shape of Chinese Yam in Terms of Key Diameters

Abstract EN

This paper proposes Bayesian methods for the shape estimation of Chinese yam (Dioscorea opposita) using a few key diameters of yam.

Shape prediction of yam is applicable to determining optimal cutoff positions of a yam for producing seed yams.

Our Bayesian method, which is a combination of Bayesian estimation model and predictive model, enables automatic, rapid, and low-cost processing of yam.

After the construction of the proposed models using a sample data set in Japan, the models provide whole shape prediction of yam based on only a few key diameters.

The Bayesian method performed well on the shape prediction in terms of minimizing the mean squared error between measured shape and the prediction.

In particular, a multiple regression method with key diameters at two fixed positions attained the highest performance for shape prediction.

We have developed automatic, rapid, and low-cost yam-processing machines based on the Bayesian estimation model and predictive model.

Development of such shape prediction approaches, including our Bayesian method, can be a valuable aid in reducing the cost and time in food processing.