Volume 9 • Issue 2 • PP: 15–25 • 2024
Contradiction-Aware Neutrosophic–Fuzzy Sensor Fusion for Reliable Irrigation Scheduling under Missing and Conflicting Evidence
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© 2024 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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Smart irrigation decisions are often computed from multiple sensor and model channels whose evidence may be gradual, missing, or mutually contradictory. A conventional fuzzy controller compresses these conditions into a scalar membership µ ∈ [0, 1], whereas a single-valued neutrosophic representation can retain support, indeterminacy, and opposition as (T, I, F ). This paper proposes a contradiction-aware neutrosophic–fuzzy irrigation system (CNFIS) in which three evidence channels for criterion j are summarized by an availability ratio qj , a robust fuzzy center m j , and a contradiction index cj . The resulting state is(Tj , Ij , Fj) = qj emj , 1 − qj + qjcj , qj(1 − emj), which satisfies Tj + Ij + Fj = 1 + qjcj and therefore separates missingness from contradiction without interpreting the triple as a probability vector. A neutral-prior shrinkage rule sj = (1 − αIj)rj + αIj/2 is then fused through b d = P j wjsj to estimate normalized irrigation demand, while U = P j wjIj supplies an explicit re-sensing or abstention signal. The method is evaluated in a fully reproducible Monte Carlo benchmark containing 30 independent runs of N = 5000 cases under five evidence regimes; no field measurements are claimed. At moderate missingness/corruption (pm, pc) = (0.10, 0.10), CNFIS obtains RMSE 0.03318 ± 0.00086 compared with 0.04192 ± 0.00079 for mean fuzzy fusion, a reduction of 20.84%. The reductions remain 12.63% and 10.57% under severe and extreme regimes, respectively, while the ordinary fuzzy mean retains a small advantage in clean evidence. At 80% selective coverage, the uncertainty score decreases CNFIS RMSE from 0.03320 to 0.02825 in the moderate regime. These results support the proposed representation as a mathematically transparent robustness layer for irrigation systems in which evidence quality, not only fuzzy demand, must be modeled explicitly.
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References
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