DSM-CoCoSo Advanced Technique for Intuitionistic Fuzzy MAGDM and its Applications to the High-Quality Development Level Evaluation of Digital Agriculture

Introduction: The Rural Revitalization Strategy (RRS) aims to address China's three major rural problems, enhance the vitality of agricultural development, and promote agricultural modernization. Within the RRS framework, “digital agriculture” emerges as a critical application of modern science and technology, involving the informatization and standardization of agricultural development. This progression not only supports agricultural modernization but also influences the broader implementation of the RRS. Evaluating the High-Quality Development Level (HQDL) of digital agriculture from the RRS perspective presents a significant challenge in the field of Multiple-Attribute Group Decision Making (MAGDM). Methods: To address the challenges of HQDL evaluation, this study proposes the Intuitionistic Fuzzy CoCoSo approach, based on the Dice similarity measure (IF-DSM-CoCoSo). This method integrates the CoCoSo technique and Dice Similarity Measure (DSM) within the framework of Intuitionistic Fuzzy Sets (IFSs), which are designed to capture and effectively handle fuzzy information in MAGDM scenarios. By combining these techniques, the IF-DSM-CoCoSo model provides a comprehensive and robust solution for evaluating HQDL in digital agriculture. Results: The proposed IF-DSM-CoCoSo approach was validated through a numerical study focused on the HQDL evaluation of digital agriculture from the perspective of the RRS. The results demonstrate the effectiveness and practicality of the model in addressing MAGDM problems, offering reliable insights and solutions for decision-makers. Discussion: The findings highlight the importance of integrating advanced decision-making techniques, such as the IF-DSM-CoCoSo model, to address the complexities of evaluating HQDL in digital agriculture. By leveraging Intuitionistic Fuzzy Sets, the approach effectively handles uncertainty and provides a robust framework for decision-making. However, further research is needed to refine the model and explore its application across diverse agricultural contexts and decision-- making scenarios. result: Through comparative analysis, we validate the effectiveness and superiority of the IF-DSM-CoCoSo model in handling MAGDM with IFSs. Conclusion: This study contributes to the understanding and practice of HQDL evaluation for digital agriculture within the RRS framework. The proposed IF-DSM-CoCoSo model offers valuable insights and practical guidance for decision-makers, advancing the development and implementation of high-quality digital agriculture and supporting the broader goals of the RRS.