Robust Spectral Classification Under Sample Type and Seasonal Variability: A Proximal Remote Sensing Approach for Grapevine Cultivar Discrimination

Abstract Purpose The discrimination of grapevine cultivars is an important yet underexploredtopic in precision viticulture. This study evaluated three aspects of grapevinecultivar classification and model training strategies: (1) the influence ofseasonal variability and data collection strategy; (2) model generalisabilityacross sample types; and (3) the combined impact of temporal and sample-typevariation on classification performance. Methods Spectral data from leaf and canopy samples were used