A Fuzzy Decision-Making Framework for Enhancing Hydroponic Agriculture: Aligning LED Lighting Strategies with Global Food Security

Hydroponic farming offers a sustainable alternative to conventional agriculture, yet selecting optimal Light-Emitting Diode (LED) grow lights remains a complex decision due to conflicting criteria and uncertainty in expert judgments. Existing Multi-Criteria Decision-Making (MCDM) approaches often assume crisp or fuzzy data and fail to capture probabilistic uncertainty, limiting decision reliability. To address this gap, this study proposes a novel hybrid framework that integrates Plithogenic Z-Numbers (PZNs) for uncertainty modeling, Analytic Hierarchy Process-Gaussian (AHP-Gaussian) for statistically robust weight derivation, and Priority Observed from the Presumption of Gaussian Attitude of Alternatives (PrOPPAGA) for probabilistic ranking. The proposed methodology effectively treats expert uncertainty and produces precise rankings of the alternatives. The results, based on the data collected from three leading hydroponic farm operators (including 11 LED lighting options against 10 evaluation criteria), demonstrate that cost, power consumption, and timer settings consistently emerge as the most influential factors, with rank differences of up to ten positions observed across the applied methods. Unlike conventional methods, our model combines linguistic expert input with statistical normalization, enabling more accurate prioritization under real-world variability. Sensitivity analysis represents stability ranges of ±2 ranks for the top-performing alternatives, confirming the robustness of the proposed model. The outcomes reveal that the integrated framework enhances decision-making reliability and informs the design of sustainable lighting systems in hydroponic settings. This research contributes a decision-support tool that improves reliability, reduces bias, and supports sustainable hydroponic lighting design, advancing the application of decision science in agriculture. Moreover, the methodological application to agricultural technology highlights new opportunities for utilizing advanced decision science in complex and data-limited contexts.