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From IoT to AIoT: Evolving Agricultural Systems Through Intelligent Connectivity in Low-Income Countries

Author

Listed:
  • Selain K. Kasereka

    (Department of Information Measurement Systems, Technical University of Sofia, 1000 Sofia, Bulgaria
    Department of Environmental and Urban Geology, Natural Hazard Management Section, Centre de Recherches Géologiques et Minières (CRGM), Kinshasa P.O. Box 13275, Democratic Republic of the Congo
    Department of Mathematics, Statistics and Computer Science, University of Kinshasa, Kinshasa XI, Kinshasa P.O. Box 190, Democratic Republic of the Congo)

  • Alidor M. Mbayandjambe

    (Department of Mathematics, Statistics and Computer Science, University of Kinshasa, Kinshasa XI, Kinshasa P.O. Box 190, Democratic Republic of the Congo)

  • Ibsen G. Bazie

    (ABIL Research Center, Kinshasa XI, Kinshasa P.O. Box 190, Democratic Republic of the Congo)

  • Heriol F. Zeufack

    (ABIL Research Center, Kinshasa XI, Kinshasa P.O. Box 190, Democratic Republic of the Congo)

  • Okurwoth V. Ocama

    (Department of Mathematics, Statistics and Computer Science, University of Kinshasa, Kinshasa XI, Kinshasa P.O. Box 190, Democratic Republic of the Congo)

  • Esteve Hassan

    (Jodrey School of Computer Science, Acadia University, Wolfville, NS B4P 2R6, Canada)

  • Kyandoghere Kyamakya

    (Institute of Smart Systems Technologies, University of Klagenfurt, 9220 Klagenfurt, Austria)

  • Tasho Tashev

    (Department of Information Measurement Systems, Technical University of Sofia, 1000 Sofia, Bulgaria)

Abstract

The convergence of Artificial Intelligence and the Internet of Things has given rise to the Artificial Intelligence of Things (AIoT), which enables connected systems to operate with greater autonomy, adaptability, and contextual awareness. In agriculture, this evolution supports precision farming, improves resource allocation, and strengthens climate resilience by enhancing the capacity of farming systems to anticipate, absorb, and recover from environmental shocks. This review provides a structured synthesis of the transition from IoT-based monitoring to AIoT-driven intelligent agriculture and examines key applications such as smart irrigation, pest and disease detection, soil and crop health assessment, yield prediction, and livestock management. To ensure methodological rigor and transparency, this study follows the PRISMA 2020 guidelines for systematic literature reviews. A comprehensive search and multi-stage screening procedure was conducted across major scholarly repositories, resulting in a curated selection of studies published between 2018 and 2025. These sources were analyzed thematically to identify technological enablers, implementation barriers, and contextual factors affecting adoption particularly within low-income countries where infrastructural constraints, limited digital capacity, and economic disparities shape AIoT deployment. Building on these insights, the article proposes an AIoT architecture tailored to resource-constrained agricultural environments. The architecture integrates sensing technologies, connectivity layers, edge intelligence, data processing pipelines, and decision-support mechanisms, and is supported by governance, data stewardship, and capacity-building frameworks. By combining systematic evidence with conceptual analysis, this review offers a comprehensive perspective on the transformative potential of AIoT in advancing sustainable, inclusive, and intelligent food production systems.

Suggested Citation

  • Selain K. Kasereka & Alidor M. Mbayandjambe & Ibsen G. Bazie & Heriol F. Zeufack & Okurwoth V. Ocama & Esteve Hassan & Kyandoghere Kyamakya & Tasho Tashev, 2026. "From IoT to AIoT: Evolving Agricultural Systems Through Intelligent Connectivity in Low-Income Countries," Future Internet, MDPI, vol. 18(2), pages 1-22, February.
    • Handle: RePEc:gam:jftint:v:18:y:2026:i:2:p:82-:d:1856159
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