Proceedings of International Conference on Applied Innovation in IT  ·  2025/12/22  ·  Vol. 13  ·  Issue 5  ·  pp. 827–839
Artificial Intelligence and Logistics Management in the Agricultural Sector: Bibliometric Analysis and Conceptual Model
Nataliia Trushkina, Rahayu Relawati, Oleh Harmash, Oksana Prokopyshyn, Diana Chernukh and Yuliya Shkrygun
📄 Download PDF DOI: 10.25673/123112
Abstract
The article presents a comprehensive bibliometric analysis of scientific publications indexed in the Scopus database, devoted to the application of artificial intelligence (AI) technologies in the logistics management of the agricultural sector. The study covers the full chronological period of observation and provides a quantitative and qualitative assessment of the dynamics of publications, the evolution of thematic domains and structural characteristics of the scientific field. The methodology includes the analysis of co-authorship, co-occurrence of keywords and visualization of terminological, geographical and temporal clusters using modern scientometric tools (VOSviewer, Bibliometrix). The results indicate a sharp increase in research interest after 2018, driven by the digital transformation of agri-food supply chains, the progress of machine learning and the strengthening of requirements for transparency, efficiency and environmental sustainability of logistics processes. Leading scientific contributions in the analyzed Scopus sample were made by researchers from Italy, China and India, along with active participation from the UK, Ukraine, the UAE, Jordan, Egypt, Oman and Indonesia. Key research areas focus on demand and yield forecasting, optimization of transport routes and inventory management, cold chain automation, risk management and increased traceability using blockchain and AI-driven decision support systems. Based on the results, a conceptual model of AI-driven agri-logistics management was formed, which integrates three interrelated dimensions: technological (AI, ML, IoT, Big Data), organizational (processes, MLOps, digital governance) and sustainable (energy efficiency, waste reduction, CO₂ reduction). The model ensures the cyclicity of the Data–Decision–Sustainability loop and supports the development of practical solutions to increase the resilience, productivity and environmental responsibility of agricultural logistics systems. The research results can be used by agricultural enterprises, logistics operators and public policy bodies to implement intelligent supply management systems and develop sustainable food chains.
Keywords
Artificial Intelligence Logistics Management Organization and Optimization of Logistics Processes Agricultural Sector Agri-Food Supply Chain Supply Chain Machine Learning Digital Agriculture Digital Transformation Digital Culture Bibliometric Analysis Conceptual Model Food Security Sustainable Development Resilience.
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