Evaluating agriculture 4.0 decision support systems based on hyperbolic fuzzy-weighted zero-inconsistency combined with combinative distance-based assessment

Agriculture 4.0 plays a crucial role in shaping sustainable cities and societies by revolutionizing urban food systems. By incorporating advanced technologies like precision farming, vertical gardening, and data analytics, Agriculture 4.0 improves local food production, reduces food transportation, and optimizes resource utilization. This paper introduces an innovative approach using Multi-Criteria Decision Making (MCDM) to assess Agriculture 4.0 Decision Support Systems (ADSS), contributing significantly to the selection of optimal systems that can drive sustainability in smart agriculture. The novelty of this research lies in developing a comprehensive evaluation framework that extends the hyperbolic fuzzy-weighted zero-inconsistency method for criteria weighting, combined with the combinative distance-based assessment method for benchmarking ADSS. The assessment matrix evaluates 13 ADSS across eight key criteria, including “accessibility,” “re-planning,” “expert knowledge,” “interoperability,” “scalability,” “uncertainty and dynamic factors,” “prediction and forecast,” and “historical data analysis”. Results from the hyperbolic fuzzy-weighted zero-inconsistency approach highlight “re-planning” (0.143) and “prediction and forecast” (0.140) as the most significant criteria, while “expert knowledge” ranked lowest (0.113). In the combinative distance-based assessment, the system labelled “OCCASION” achieved the highest score (3.843), positioning it as the most favourable ADSS, whereas the “MOLP-based beef supply chain” system scored lowest (−3.519). Sensitivity analysis, conducted using varying sets of weights, confirms the robustness and reliability of the proposed approach. This research provides a powerful decision-making tool that can guide stakeholders in selecting the best ADSS, ultimately promoting sustainability and resource optimization in Agriculture 4.0. The findings have important implications for farmers, agribusiness, and smart agriculture, demonstrating the potential of the methodology to enhance decision-making processes in a critical sector.