Estimation of Leaf Weight Based on Snowflake Theory

Abstract

There are two parts in this paper: The first part is the classification of leaf shapes. Leaf shapes are classified from the macro and micro perspectives respectively. In the macro perspective, influential factors on leaf shapes, such as ground diameter, breast diameter, etc., are used as variables to carry out K-means clustering analysis for classification on leaf shapes. In the micro perspective, 40 influential leaf structure factors on leaf shapes are extracted and analyzed by factor analysis and a second K-means clustering. After comparing clustering result with actual classification result, misjudgment probability is found to be very low. In the second part, snowflake model theory is proposed; the growth process of a tree is simulated. Through statistics, the number of leaves growing on smallest branches is not so different to each other. After calculating the number of smallest branches through programming, the total number of leaves could be calculated out.