FUZZY CLUSTERS FOR CLASSIFIATION OF PLANTS AND SOIL
Fuzzy C-means clustering is a soft technique and has some advantages in ecological studies. Fuzzy excess red (ExR) and excess green (ExG) indices and clustering algorithms: fuzzy c-means (FCM) and Gustafson–Kessel (GK) were studied for unsupervised classification of hidden and prominent regions of interest (ROI) in color images. A hundred plots were classified by fuzzy C-means clustering. Each formation has its own composition, structure, distribution range and environment, and all of them should be protected effectively. The results suggest that fuzzy C-means clustering is a useful technique for classification of the plant community.
Reference Paper-1: Intensified fuzzy clusters for classifying plant, soil, and residue regions of interest from color images
Author’s Name: George. E. Meyer∗, Joao Camargo Neto, David D. Jones, and Timothy W. Hindman
Source: Elsevier-Computers and Electronics in Agriculture
Year: 2003
Reference Paper-2: Fuzzy C-means Clustering Applied to the Classification of Glycyrrhiza uralensis Communities in North China
Author’s Name: Naiqi Song and Jintun Zhang
Source: Science PG-Automation, Control and Intelligent Systems
Year: 2017
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