Distinguishing between pest and pollinator butterfly species is a major challenge in precision agriculture. However, traditional RGB cameras, capturing only shape and surface color, are insufficient for detailed insect analysis. This work explores the rich spectral information provided by hyperspectral imaging for effective butterfly species identification. For this purpose, we use a single spatio-spectral image that provides partial spectral information to identify the butterfly species. The proposed classification approach consists of a convex combination of the probabilistic decisions obtained by the Gaussian Naive Bayes and Z-score methods for each butterfly reflectance. Compared to traditional classification models, this approach showed higher robustness and performance.