Neutrosophic–Fuzzy Evidence Fusion for Uncertainty-Aware

Cultivar Identification from Viticultural Chemical Profiles

Amine Saddik1 Ika Hesti Agustin2,*

1 Faculty of Applied Sciences, Ibn Zohr University, Ait Melloul, Morocco

2 Department of Mathematics, University of Jember, Jember, East Java, Indonesia

Received: September 14, 2025 Revised: November 20, 2025 Accepted: January 15, 2026 ⋆ Corresponding author

ABSTRACT

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 =

GkLk, Fk =

(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.

Keywords: Neutrosophic sets Fuzzy evidence Viticultural analytics Cultivar identification Agricultural decision

support Uncertainty quantification Chemical profiling

1. INTRODUCTION

Agricultural analytics increasingly relies on measurements

whose value is not only predictive but also evidential: a chemical

or sensor profile should support an agronomic decision

while indicating when the supporting information is internally

ambiguous. Fuzzy methods are well suited to this setting because

a sample need not belong to a linguistic or numerical