Modelling Intelligent Agriculture Decision Support Tools to Boost Sustainable Digitalization: Evidence from MCDM Methods

Intelligent agriculture decision support tools (IADSTs) are essential tools that help farmers manage their farms under sustainable conditions. However, the assessment of IADSTs is rather complicated; it is difficult for farmers to select suitable and efficient farm-level DSTs because of the many assessment criteria, the fluctuation of data, and the large number of available IADSTs. Thus, this study evaluates and discusses different IADSTs to identify the best one that satisfies the given criteria and assessment at the farm level. MCDM is an approach used in expert systems and artificial intelligence, and many past and current researchers have recommended it for use in selecting the best IADSTs from a group of IADSTs. This study is considered distinct because it succeeded in integrating the Opinion Weight Criteria Method (OWCM) and TODIM to extract the weights of the criteria and rank IADSTs, respectively. The methodology is outlined in three phases: 1 - building the IADST decision matrix, 2 - weighting criteria, and 3 - evaluating the IADSTs. The analysis results showed that the best tool was “FSA,” which had the highest IADST score of (1), followed by “RISE 3.0,” with a score of 0.84936. This study also provides important recommendations for farmers and policymakers to improve productivity under sustainable conditions.