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Establish seedling quality classification standard for Chrysanthemum efficiently with help of deep clustering algorithm

Yanzhi Jing, Hongguang Zhao, Shujun Yu

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.

Establish seedling quality classification standard for Chrysanthemum efficiently with help of deep clustering algorithm

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 for edible chrysanthemum seedlings. Through conducting extensive experiments, we validate the correctness and efficiency of the proposed SQCSEF framework.
Paper Structure (11 sections, 23 equations, 6 figures, 11 tables)

This paper contains 11 sections, 23 equations, 6 figures, 11 tables.

Table of Contents

  1. introduction
  2. Materials and methods
  3. Experimental materials
  4. Basic preprocessing and statistical analysis of dataset
  5. Framework of establishing seedling quality classification standard
  6. Traditional cluster analysis algorithm
  7. Deep Clustering
  8. Results
  9. Analysis of dataset
  10. Performance of SQCSEF using different clustering method
  11. Conclusions

Figures (6)

  • Figure 1: CVCL method architecture. Here we introduce weight to each view.
  • Figure 2: Scatterplot matrix of indices with kernel density estimation.
  • Figure 3: P-P plot of indices. If the points roughly follow a straight line, it suggests that the variable obey a normal distribution. However, if there are significant deviations from the linearity, it may indicate non-normality.
  • Figure 4: Correlation heat map of indices.
  • Figure 5: Scree plot of factor analysis. The Y-axis represents eigenvalue of each factor.
  • ...and 1 more figures