Abstract:

Based on multiple dust retention indexes of 10 typical plant leaves in Changsha, the dust retention rank and pattern recognition model of plants are constructed by using the fuzzy mathematics theory. In addition, a comprehensive quantitative evaluation of dust retention of greening plants was performed. Firstly, after leaf samples were collected by conventional methods, leaf surface area parameters were measured by the drilling and weighing method; the densities of pores and fluff on plant leaf surface were calculated by the microscopic observation method; and the maximum dust retention per unit leaf area and natural dust retention were measured by the dry-cleaning method. Then, the dust retention rank and pattern recognition of 10 plant dust retention indexes were carried out by using the fuzzy clustering theory. The dust retention indexes of 9 plants were divided into 7 clusters with different λ levels by using fuzzy clustering. When it was 0.907, the F test value was 21.41, and the difference between dust retention indexes was more evident. The dust retention indexes of 9 plants were the best and 9 plants could be divided into 4 dust retention ranks: low, medium, higher and the highest, in that, Ligustrum×vicaryi has the highest dust retention rank, Loropetalum chinense has a higher dust retention rank, Buxus sinica, Euonymus japonicus and Photinia×fraseri have a medium dust retention rank, and Pittosporum tobira, Llex chinensis, Rosa chinensis and Rhododendron simsii have the lowest dust retention rank. The 10th plant wax was identified as a medium dust retention rank based on pattern recognition. The dust retention rank and pattern recognition conclusion are basically consistent with previous research. The pattern recognition method of dust retention rank of greening plant leaves constructed by fuzzy clustering is viable and can provide a new perspective for a comprehensive quantitative evaluation of dust retention of greening plant leaves.

Key words: garden plants, dust retention rank, fuzzy cluster, pattern recognition, close degree, unit leaf area

摘要：

基于长沙市10种典型植物叶片的多个滞尘指标,运用模糊数学理论构建植物的滞尘等级与识别模型,实现绿化植物滞尘综合量化评判方法的探究。首先,常规方法采集叶样后,运用打孔称质量法测量叶表面积参数,显微观察法计算植物叶表气孔与绒毛密度,干洗法测量单位叶面积最大滞尘量与自然滞尘量。然后,运用模糊聚类理论对10种植物滞尘指标进行滞尘等级划分与模式识别。模糊聚类将9种植物叶片的滞尘指标分为7个不同λ水平的聚类。其中λ为0.907时,F检验值为21.41,滞尘指标间差异最显著。9种植物滞尘指标聚为4种最优,9种植物可对应划分为低、中、较高与高4个滞尘等级,即金叶女贞（Ligustrum×vicaryi）滞尘等级最高,红花檵木（Loropetalum chinense）滞尘等级较高,瓜子黄杨（Buxus sinica）、金边黄杨（Euonymus japonicus）与红叶石楠（Photinia×fraseri）3种植物滞尘等级为中等,海桐（Pittosporum tobira）、冬青（Ilex chinensis）、月季（Rosa chinensis）、杜鹃花（Rhododendron simsii）滞尘等级最低。第10种植物小蜡在模式识别中辨识为中等滞尘等级。滞尘等级与模式识别结论与相关研究基本相符,模糊聚类构建的绿化植物叶片滞尘等级模式识别方法具有可操作性,可为绿化植物叶片滞尘效果综合量化评价提供新思路。

关键词: 园林植物, 滞尘等级, 模糊聚类, 模式识别, 贴近度, 单位叶面积