Adoption of Internet of Things (IoT) in Agriculture: A Systematic Review of Challenges and Opportunities in Developing Countries
DOI:
https://doi.org/10.34145/ivas.v1i1.3881Keywords:
Internet of Things, Agriculture, Technology Adoption, Developing Countries, Systematic ReviewAbstract
Agriculture in developing countries plays a crucial role in global food security, yet the adoption of digital technology remains limited due to infrastructure and literacy gaps. The Internet of Things (IoT) has the potential to enhance resource efficiency, productivity, and sustainability; however, its implementation among smallholder farmers faces multiple barriers. Previous literature remains fragmented and restricted to specific contexts, highlighting the need for a systematic review to provide a comprehensive picture of IoT adoption in agriculture. This study aims to explain the level of IoT adoption in developing countries, identify key barriers, and analyse the factors influencing farmers’ decisions to adopt it. The research employed a systematic literature review (SLR) design, following the PRISMA 2020 protocol. Databases used include Scopus, Web of Science, ScienceDirect, SpringerLink, Taylor & Francis Online, Wiley, PubMed, and DOAJ. Inclusion criteria covered empirical articles in English published between 2015 and 2025 focusing on IoT in agriculture in developing countries. The quality of studies was assessed using the Mixed Methods Appraisal Tool (MMAT), while data were thematically analysed using NVivo 14. Synthesised studies reveal that the adoption of IoT among farmers in developing countries generally ranges from low to moderate, with significant variations across regions. Sub- Saharan Africa is still dominated by pilot projects, India and Southeast Asia demonstrate limited positive trends in high-value commodities, while Brazil shows relatively greater progress on a large scale. Major barriers include high initial costs, limited digital infrastructure, low literacy levels, and minimal policy support. Determinants of adoption include perceptions of relative advantage, technological compatibility, social influence, digital literacy, access to finance, and observability of results. IoT adoption in agriculture in developing countries still faces multidimensional challenges. Accelerating adoption requires holistic interventions such as subsidies, digital literacy programmes, strengthened policies, and innovative business models. Future research is recommended to focus on the livestock subsector, cross-regional studies, and the socio-cultural role in adoption decisions.
Downloads
References
Hundal, G. S., Singh, S., Singh, R., & Singh, P. (2023). Exploring barriers to the adoption of IoT-based precision agriculture practices. Agriculture 13(1), 163.https://doi.org/10.3390/agriculture13010163
International Telecommunication Union. (2023). Facts and figures 2023: The state of digital connectivity. ITU Publications. https://www.itu.int/facts-and-figures
Li, M., Guo, Y., & Wu, Y. (2024). The influence mechanism analysis on the farmers’ intention to adopt IoT (UTAUT-TOE). Scientific Reports, 14, 65415. https://doi.org/10.1038/s41598-024-65415-4
Lowder, S. K., Sánchez, M. V., & Bertini,R. (2019). Farms, family farms, farmland distribution and farm labour: What do we know today? FAO Agricultural Development Economics Working Paper,
19-08. https://doi.org/10.22004/ag.econ.303709
Ronaghi, M. H., & Forouharfar, A. (2020). A contextualized study of IoT usage in smart farming.Information Systems Frontiers, 22(1), 189–206. https://doi.org/10.1007/s10796-018-9864-4
Ruslan, A., Rahim, M., & Hamzah, M. (2024). Determinants of adoption of Internet of Things (IoT) in rice farming: Evidence from major granary areas. Pakistan Journal of Life and Social Sciences, 22(2), 403–414. http://www.pjlss.edu.pk/pdf_files/2024_2/403-414.pdf
Shi, S., Alam, M., & Zeng, X. (2022). The antecedents of willingness to adopt and pay for the IoT in the agricultural industry. Sustainability, 14(11), 6640. https://doi.org/10.3390/su14116640
World Bank. (2024). Digital progress and agricultural innovation in developing countries. World Bank Publications. https://www.worldbank.org/en/topic/agriculture/publication/digital-progress
Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597. https://doi.org/10.1080/2159676X.2019.1628806
Hong, Q. N., Fàbregues, S., Bartlett, G., Boardman, F., Cargo, M., Dagenais, P., Gagnon, M. P., Griffiths, F., Nicolau, B., O’Cathain, A., Rousseau, M. C., Vedel, I., & Pluye, P. (2018). The Mixed Methods Appraisal Tool (MMAT) version 2018 for information professionals and researchers. Education for Information, 34(4), 285–291. https://doi.org/10.3233/EFI-180221
Kitchenham, B., Brereton, O. P., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009). Systematic literature reviews in software engineering–A systematic literature review. Information and Software Technology, 51(1), 7–15. https://doi.org/10.1016/j.infsof.2008.09.009
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Downloads
Published
How to Cite
Issue
Section
Citation Check
License
Copyright (c) 2026 Jodi Setia, Ugik Romadi, Oftafiana Eka Rahmadhani, Ahmad Bahtiar Lubis, Army Destia Milatina Suci, Fittriyani, Stefanie Anita Asuat

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.



