用深度聚类算法高效建立菊花苗质量分级标准
Establish seedling quality classification standard for Chrysanthemum efficiently with help of deep clustering algorithm
- 引入因子分析与先进聚类算法，从多指标中自动提取分级依据
- 新标准$S_{cvcl}$比传统方法更合理，实验验证其正确性与效率
- 框架通用性强，适用于多数植物种类，适合农业科研与生产
建立可食用菊花苗的合理质量分级标准有助于促进苗期发育，提升植株品质。然而，现有分级方法存在局限：仅支持少数指标导致信息丢失，评估指标适用性窄，部分方法误用数学公式。为此，本文提出一种简单、高效且通用的框架SQCSEF，采用灵活聚类模块，适用于大多数植物物种。研究引入先进的深度聚类算法CVCL，通过因子分析将指标划分为多个视角作为输入，生成更合理的聚类结果，最终获得食用菊花苗的分级标准$S_{cvcl}$。通过大量实验验证了所提SQCSEF框架的正确性与高效性。
原文摘要 · Abstract (English)
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.
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