Abstract
The assessment of vegetation health stands as a fundamental operation which precision agriculture and ecology and environmental risk management require. The Normalized Difference Vegetation Index (NDVI) uses red and near-infrared (NIR) reflectance data to function as the primary index which enables vegetation health assessment and stress detection. The implementation of these systems faces challenges when trying to deploy them in budget-friendly operations because they need multispectral sensors which include NIR channels. The research evaluates three algorithmic approaches through a complete analysis which includes (i) NDVI method and (ii) RGB-only channel transformation method and (iii) a synthesized hybrid algorithm that applies NDVI normalization to green-dominance for standard RGB cameras. The research includes mathematical derivations together with radiometric interpretation methods and calibration needs and error propagation calculations for all investigated approaches. The research develops a system which performs UAV image data preprocessing through geometric correction and radiometric normalization and noise filtering and illumination compensation. The system becomes more useful through discussions about UAV implementation scenarios and flight planning constraints and geospatial post-processing workflows. The text includes forest-fire-related use-cases as examples of fast assessment situations which vegetation stress maps help with post-event choices yet these examples do not represent the main focus. The research findings show that NDVI based on classical methods delivers better results for quantitative biophysical analysis but RGB-based methods work well for qualitative segmentation when using limited hardware resources. The developed algorithm delivers superior vegetation identification results than RGB transformation methods and functions as a useful solution for organizations which lack NIR-equipped systems. The document presents recommendations which help users choose methods based on their specific requirements regarding accuracy levels and budget constraints and time needs for deployment and system operational difficulties.