A Framework for Identifying and Analyzing Key Research Trends in the Smart Agricultural Supply Chain

Objective: The smart supply chain in the agricultural sector has emerged as a key driver for sustainable development and resource efficiency in the contemporary world. Increasing demand for agricultural products, limitations in natural resources, and the necessity to reduce waste have highlighted the need for the adoption of advanced technologies and the digitalization of supply chain processes. In this context, identifying the key and priority research trends in smart agriculture and smart supply chains can facilitate both scientific and practical development, as well as support data-driven managerial decision-making. The objective of this study is to identify and prioritize the most significant research trends related to the smart supply chain in the agricultural sector.

Methodology: To achieve this objective, the present study employed a multi-quantitative approach. Research data were collected through a systematic literature review and expert surveys. In the first step, research trends were extracted from published studies in the fields of smart agriculture and smart supply chains, resulting in the identification of 30 initial trends. Subsequently, with the participation of 10 academic and professional experts, these trends were refined and screened using the fuzzy Delphi technique, such that only trends with a fuzzy score higher than 0.7 progressed to the next stage, resulting in the selection of 9 key trends. In the following step, these trends were analyzed and ranked based on three critical criteria—frequency in review articles, technological impact, and future-orientedness—using the MARCOS method. The weights of the criteria were considered equal, and the data were linearly normalized and incorporated into a weighted normalized matrix to quantitatively determine the utility and priority of each trend.

Findings: The research findings indicated that five research trends hold the highest priority: the integration of explainable artificial intelligence (XAI) with blockchain for decision-making and security, the use of machine learning for optimizing product flow and reducing waste, the combination of sensor data and big data for production and consumption forecasting, the expansion of the Internet of Things (IoT) in agricultural supply chain management, and the development of traceability and transparency in the supply chain through blockchain. These trends not only reflect key technological advancements in smart agriculture but also delineate future research directions and development strategies aimed at enhancing the efficiency, transparency, and sustainability of agricultural supply chains.

Conclusion: The research concludes that focusing on data-driven technologies and the digitalization of processes can enhance efficiency, reduce waste, improve sustainability, and strengthen trust among supply chain stakeholders. The integration of explainable artificial intelligence (XAI) with blockchain, the use of machine learning and the analysis of sensor and big data, as well as the expansion of the Internet of Things (IoT) and supply chain transparency, serve as key tools to achieve these objectives. The findings can assist researchers, managers, and policymakers in scientifically and data-drivenly determining their research and development priorities. Despite limitations such as a small expert sample, reliance on published data, and the quantitative nature of the analysis, this study provides a valuable foundation for scientific policymaking, planning the development of emerging technologies, and conducting future studies with larger samples and mixed-method quantitative–qualitative analyses. Future research could investigate the environmental, social, and economic impacts of implementing smart technologies and develop sustainable, human-centered frameworks for agricultural supply chain management.