Journal of Neutrosophic and Fuzzy Systems

Cultivar identification from viticultural chemical profiles is a multiclass recognition problem in which a hard label alone does not reveal whether global chemometric evidence agrees with the local structure of previously observed samples. This paper proposes Neutrosophic–Fuzzy Viticultural Evidence Fusion (NFVEF), an uncertainty-aware classifier that combines a global discriminant probability vector G(x) ∈ ΔK−1 with a Gaussian fuzzy-neighborhood vector L(x) ∈ ΔK−1. For every cultivar k, the two evidence views are converted into Tk = p GkLk, Fk = p (1−Gk)(1−Lk), Ik = 1−Tk −Fk. where Ik is exactly the squared Hellinger disagreement between the Bernoulli support views Gk and Lk. A logarithmic fuzzy opinion pool Hk ∝ Gηk L1−η k is then attenuated by neutrosophic disagreement, Rk ∝ Hk exp(−κIk), before classification. The winning class is accompanied by an uncertainty score U = 1−Tˆk (1−Iˆk)(1−Fˆk ), enabling uncertain chemical profiles to be flagged rather than reported with unqualified confidence. The method is evaluated on the UCI Wine cultivar dataset using 60 repeated stratified splits at four synthetic analytical-perturbation levels δ ∈ {0,0.1,0.2,0.3} measured relative to training-feature standard deviations. NFVEF obtains mean accuracies of 0.9858, 0.9836, 0.9744, and 0.9728, respectively. At δ = 0.3, its paired accuracy advantage over linear discriminant analysis is 0.00278 with a 95% bootstrap interval [0.00123,0.00463], while RBF-SVM remains slightly better in raw accuracy. The uncertainty score detects NFVEF errors with mean AUC 0.9520 at the strongest perturbation, and retaining the lowest-uncertainty 90% of cases yields 0.9922 accuracy. The contribution is therefore not universal classifier dominance, but a mathematically interpretable fuzzy–neutrosophic evidence layer for cultivar identification from ambiguous chemical measurements.