Research Information System
Engineering Applications of Artificial Intelligence, vol.164, 2026 (SCI-Expanded, Scopus)
Farm-level evaluation is considered an essential element that assists farmers in managing their farms in the wake of rising emphasis on agriculture. However, it is difficult for farmers to choose appropriate and effective farm-level agricultural decision support tools (ADSTs) due to multiple assessment criteria, data fluctuation, and a myriad of ADSTs. Previous studies have implemented the Multi-Attribute Decision-Making (MCDM) technique to evaluate the ADSTs. However, due to some inherent MCDM issues, it is not recommended to be an appropriate technique that aligns with ADSTs evaluation needed for agriculture 4.0. Accordingly, this study addresses this gap and proposes a novel decision-matrix ADSTs evaluation technique by integrating fuzzy-weighted zero-inconsistency (FWZIC) and fuzzy decision-making opinion score method (FDOSM) approaches. To this end, the methodology of the study contains two steps: first, an initial decision matrix is established based on “9 main criteria” and 16 ADSTs; second, the steps of Z-Cloud (ZC) FWZIC to get the weight of every criterion were done, while ZC-FDOSM steps are defined to rank ADSTs. The results of the analysis show that Farm Sustainability Assessment (FSA), DairySAT, and FieldPrint Calculator were found to be the best tools for the 1st, 2nd, and 3rd groups in a sequence, while COMER-FARM, Dairy GEM, and Ofoot were found to be the worst in the first, second, and third groups, respectively. This study provides essential implications for farmers and policymakers to increase productivity and reduce the use of resources.