Weed Identification using K-Means Clustering with Color Spaces Features in Multi-Spectral Images Taken by UAV

Abstract

Food security is a pertinent global challenge that has plagued the nations over time immemorial. Maize is one of the world's most significant consumption crop, based on production volume. Maize supply can vary according to the cultivation area, climatic condition, and disease. Modern precision weed management relies on site-specific management tactics to maximize resource use efficiency and yield, while reducing unintended environmental impacts caused by herbicides. Recent advancements in Unmanned Aircraft Systems (UAS)-based tools and geospatial information technology have created enormous applications for efficient and economical assessment of weed infestations as well as site-specific weed management. This paper explores the possibility of extracting features from color spaces and combining them with vegetation indices (NDVI, VARI, and TGI) to be clustered using K-means classifier to identify the weed population from a multispectral imagery. The results give a clear indication that the NDVI performance is better than VARI; It also shows that TGI is not acceptable for the classification.