Establish seedling quality classification standard for Chrysanthemum efficiently with help of deep clustering algorithm
This addresses the need for efficient and generic seedling classification standards for plant species like chrysanthemums, but it is incremental as it applies existing deep clustering methods to a specific domain.
The paper tackled the problem of establishing seedling quality classification standards for edible chrysanthemums by proposing a framework (SQCSEF) that uses a deep clustering algorithm (CVCL) with factor analysis, resulting in a more reasonable grading standard S_cvcl, validated through extensive experiments.
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.