Advancing digital agri-food systems through integrated AI, robotics, IoT, and blockchain with a bibliometric analysis
- 1Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC, United States of America
Abstract
Digital Agri-Food Systems (DAFS) are transforming agriculture by combining digital and intelligent technologies to enhance automation, traceability, and data-driven decisions. This study evaluates technical advancements in DAFS by examining 332 peer-reviewed articles retrieved from Scopus. Using a hybrid evidence-synthesis approach that integrates Systematic Mapping and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), it enables wide theme coverage requiring thorough screening, explicit inclusion criteria, and repeatability. The study identifies dominant research trajectories, methodological trends, and cross-domain technological interactions spanning crop production, livestock systems, food safety, and supply chain management. The findings show a marked transition toward hybrid intelligent frameworks that merge multimodal sensing with advanced analytical models for real-time crop monitoring, early disease detection, yield forecasting, autonomous robotics, and verifiable supply chain traceability. Internet of Things (IoT) sensor networks and unmanned aerial vehicle (UAV)-based imaging continue to strengthen precision agriculture practices, while blockchain-enabled systems support transparency, trust, and fraud prevention. Additionally, bibliometric analysis reveals strong international collaboration clusters and rapidly expanding research in precision agriculture, agricultural robotics, and intelligent supply chains. Key challenges persist, such as limited datasets, inconsistent validation, high computational costs, and insufficient focus on cybersecurity. The study shows that advancing DAFS requires scalable, unified architectures linking artificial intelligence (AI)-driven decision tools, interoperable IoT systems, and secure blockchain technologies. The integration is essential for building resilient, efficient, and sustainability-driven agri-food systems.
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BibTeX
@article{afrifa2026advancing,
title = {{Advancing digital agri-food systems through integrated AI, robotics, IoT, and blockchain with a bibliometric analysis}},
author = {Stephen Afrifa and Kapalik Khanal and Biswash Khatiwada and Ramesh Bahadur Bist},
journal = {Computers and Electronics in Agriculture},
year = {2026},
month = jun,
doi = {10.1016/j.compag.2026.112044},
url = {https://doi.org/10.1016/j.compag.2026.112044},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Advancing digital agri-food systems through integrated AI, robotics, IoT, and blockchain with a bibliometric analysis
AU - Stephen Afrifa
AU - Kapalik Khanal
AU - Biswash Khatiwada
AU - Ramesh Bahadur Bist
PY - 2026
DA - 2026/06/16
JO - Computers and Electronics in Agriculture
DO - 10.1016/j.compag.2026.112044
UR - https://doi.org/10.1016/j.compag.2026.112044
AB - Digital Agri-Food Systems (DAFS) are transforming agriculture by combining digital and intelligent technologies to enhance automation, traceability, and data-driven decisions. This study evaluates technical advancements in DAFS by examining 332 peer-reviewed articles retrieved from Scopus. Using a hybrid evidence-synthesis approach that integrates Systematic Mapping and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), it enables wide theme coverage requiring thorough screening, explicit inclusion criteria, and repeatability. The study identifies dominant research trajectories, methodological trends, and cross-domain technological interactions spanning crop production, livestock systems, food safety, and supply chain management. The findings show a marked transition toward hybrid intelligent frameworks that merge multimodal sensing with advanced analytical models for real-time crop monitoring, early disease detection, yield forecasting, autonomous robotics, and verifiable supply chain traceability. Internet of Things (IoT) sensor networks and unmanned aerial vehicle (UAV)-based imaging continue to strengthen precision agriculture practices, while blockchain-enabled systems support transparency, trust, and fraud prevention. Additionally, bibliometric analysis reveals strong international collaboration clusters and rapidly expanding research in precision agriculture, agricultural robotics, and intelligent supply chains. Key challenges persist, such as limited datasets, inconsistent validation, high computational costs, and insufficient focus on cybersecurity. The study shows that advancing DAFS requires scalable, unified architectures linking artificial intelligence (AI)-driven decision tools, interoperable IoT systems, and secure blockchain technologies. The integration is essential for building resilient, efficient, and sustainability-driven agri-food systems.
SN - 0168-1699
SN - 1872-7107
ER -