Enhancing Predictive Modeling of Chinese Yam Shape Through Bayesian Linear Modeling and Key Diameter Modification

Abstract

In the development of devices for cutting Chinese yams into chunks for use as seeds, accurately measuring the yam's shape with a simple mechanism is crucial. In our prior study, we introduced a statistical approach for predicting the shape of a Chinese yam based on its key diameters. This method involves organizing sample data, estimating diameters at discrete points along the central axis, and constructing a predictive model based on these estimated diameters. However, the initial predictive model relied on separate regression models for each point, potentially leading to instability. In this article, we enhance our previous approach by incorporating a new step that refines the estimation of regression coefficients through Bayesian linear modeling methods. This modification allows for the simultaneous estimation of regression coefficients, ensuring greater stability in the reconstructed model. Additionally, we modify the method for locating key diameters. To validate the performance of the enhanced approach, we apply it to a set of samples and compare the output of the reconstructed model with that of our initial method. The results demonstrate improved stability and performance, highlighting the efficacy of the refined modeling technique.