Establish seedling quality classification standard for Chrysanthemum efficiently with help of deep clustering algorithm (2409.08867v1)

Abstract: Establishing reasonable standards for edible chrysanthemum seedlings helps promote seedling development, thereby improving plant quality. However, current grading methods have the several issues. The limitation that only support a few indicators causes information loss, and indicators selected to evaluate seedling level have a narrow applicability. Meanwhile, some methods misuse mathematical formulas. Therefore, we propose a simple, efficient, and generic framework, SQCSEF, for establishing seedling quality classification standards with flexible clustering modules, applicable to most plant species. In this study, we introduce the state-of-the-art deep clustering algorithm CVCL, using factor analysis to divide indicators into several perspectives as inputs for the CVCL method, resulting in more reasonable clusters and ultimately a grading standard $S_{cvcl}$ for edible chrysanthemum seedlings. Through conducting extensive experiments, we validate the correctness and efficiency of the proposed SQCSEF framework.