Vegetation Health Assessment Through a Novel Index Approximation Filter and K-Means Clustering

Vegetation health assessment is crucial for sustainable agricultural practices, particularly in managing rotational grazing strategies effectively. Remote sensing technology has been used increasingly for earth observation, which includes monitoring extensive agricultural landscapes. This study introduces a novel Vegetation Index Approximation Filter, combined with K-means clustering, to advance pasture health analysis techniques. Our method can accurately classify Sentinel-2 multispectral image regions into five vegetation health levels. By integrating unsupervised K-means clustering, our approach is able to increase assessment accuracy, and provide actionable insights for optimizing pasture usage and sustainability in diverse landscapes.