Big Data Approaches for Agricultural and Health Systems Monitoring: Lessons for Sub-Saharan Africa from Global Experiences

Abstract

Big data enabled technologies are increasingly recognized as important tools for strengthening agricultural productivity and health systems monitoring, particularly in resource-constrained settings. This study presents a scoping review of existing evidence on the application of big data approaches including artificial intelligence, machine learning, Internet of Things (IoT), remote sensing, and digital surveillance platforms for agricultural and health systems monitoring, with a focus on Sub-Saharan Africa (SSA). The review was conducted in accordance with the PRISMA-ScR guidelines. Peer-reviewed studies published between 2020 and 2025 were identified from major scientific databases and screened based on predefined eligibility criteria. Six studies met the inclusion criteria and were synthesized narratively. The findings indicate that big data applications in SSA are predominantly implemented at pilot or early operational stages, with limited large-scale integration across sectors. Common data sources included routine administrative records, sensor-generated data, remote sensing imagery, and digital reporting platforms. Reported benefits included improved timeliness, situational awareness, and decision support; however, implementation was constrained by infrastructure limitations, data quality challenges, limited analytical capacity, governance issues, and sustainability concerns. Evidence of integrated, multi-source surveillance particularly within One Health frameworks remains limited. Overall, this scoping review highlights both the potential and the current gaps in the use of big data approaches for agricultural and health systems monitoring in SSA. Context-sensitive implementation, capacity building, data governance, and institutional coordination are essential to translating technological innovation into sustainable improvements in food security and public health outcomes.