Cloud Model-Based Feature Extraction Algorithm for Flower Petals

This cloud model-based feature extraction algorithm for flower petals utilizes the reverse normal cloud generator to extract three digital characteristics (expected value, entropy, and hyper-entropy) for each of the four feature parameters across three flower categories from sample point data. The implementation involves statistical analysis of sample distributions and cloud transformation techniques to quantify feature uncertainty.