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Artificial intelligence and the future of food security in Nigeria

Authors

Abideen alamu Nigerian institute of social and economic research

DOI:

https://doi.org/10.63207/ai.v8i16.177

Abstract

Food insecurity remains persistent in Nigeria despite abundant agricultural potential, driven by low productivity, weak infrastructure, and climate shocks. As global agricultural systems increasingly adopt Artificial Intelligence (AI) for forecasting, crop monitoring, and pest detection, this study examines its relevance for Nigeria’s smallholder-dominated sector. A qualitative systematic review of 34 studies, identified through Scopus, Web of Science, PubMed, and Google Scholar and screened using a PRISMA approach, synthesised evidence on AI applications, food security dimensions, and adoption barriers. Findings show that while AI has improved yields and reduced costs in countries such as India, China, and Kenya, adoption in Nigeria remains minimal due to poor connectivity, low digital literacy, high costs, and weak policy coordination. The review highlights the need for localized, low-bandwidth, farmer-friendly AI tools and stronger data governance. It concludes that with improved rural infrastructure, capacity building, and support for local innovation, AI can significantly enhance Nigeria’s food security.

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References

Adelekan, S. A., Omotayo, A. O. (2021). Digital agriculture in Africa: Implications for smallholder farmers. Journal of Agricultural Extension, 25(3), 45–57.

Akbar, A., Anwar, M., Shah, M. A., Alotaibi, F. (2023). Applications of artificial intelligence in smart agriculture: A comprehensive review. Computers and Electronics in Agriculture, 206, 107645. https://doi.org/10.1016/j.compag.2023.107645 (Consulted on 25/09/2025)

Akinola, O., Adegbite, A., Okunlola, A. (2022). Artificial intelligence applications in Nigerian agriculture: Prospects and challenges. Journal of Agricultural Extension, 26(2), 45–56. https://doi.org/10.4314/jae.v26i2.5 (Consulted on 25/09/2025)

Bacco, M., Barsocchi, P., Ferro, E., Gotta, A., Ruggeri, M. (2019). The digitisation of agriculture: A survey of research activities on smart farming. Array, 3–4, 100009. https://doi.org/10.1016/j.array.2019.100009 (Consulted on 25/09/2025)

Barbedo, J. G. A. (2019). Plant disease identification from individual lesions and spots using deep learning. Biosystems Engineering, 180, 96–107. https://doi.org/10.1016/j.biosystemseng.2019.02.002 (Consulted on 25/09/2025)

Benos, L., Tagarakis, A. C., Dolias, G.; Berruto, R., Bochtis, D., Busato, P. (2021). Machine learning in agriculture: A comprehensive updated review. Sensors, 21(11), 3758. https://doi.org/10.3390/s21113758 (Consulted on 25/09/2025)

Booth, A., Sutton, A., Papaioannou, D. (2016). Systematic approaches to a successful literature review (2nd ed.). London: SAGE Publications.

Braun, V., Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa (Consulted on 25/09/2025)

Carter, H. (1989). Food and agriculture: Balancing tradition and change. Food Policy, 14(1), 3–14. https://doi.org/10.1016/0306-9192(89)90022-4 (Consulted on 25/09/2025)

Clapp, J., Moseley, W. G., Garrett, J. (2022). Food security and nutrition: Progress and challenges. Global Food Security, 32, 100605. https://doi.org/10.1016/j.gfs.2021.100605 (Consulted on 25/09/2025)

Cooper, H. (2016). Research synthesis and meta-analysis: A step-by-step approach (5th ed.). Thousand Oaks, CA: SAGE Publications.

Elijah, I. (2019). AGRICULTURAL PROGRAMMES AND FOOD SECURITY IN NIGERIA (1960-2016). A HISTORICAL OVERVIEW.

Eme, O. I., Onyishi, T., Uche, O. A. (2014). Challenges of Food Security in Nigeria: Options before Government. Oman Chapter of Arabian Journal of Business and Management Review, 4(1), 15–25. https://doi.org/10.12816/0016564 (Consulted on 25/09/2025)

FAO. (2006). Food security: Policy brief. Food and Agriculture Organization of the United Nations.

FAO. (2012). Nigeria Country programming framework 2013-2017. Food and Agriculture Organization of the United Nations.

FAO. (2021). The state of food and agriculture 2021: Making agrifood systems more resilient to shocks and stresses. Food and Agriculture Organization of the United Nations.

FAO. (2022). The state of food security and nutrition in the world 2022: Repurposing food and agricultural policies to make healthy diets more affordable. Food and Agriculture Organization of the United Nations.

Javaid, M., Haleem, A., Singh, R. P., Suman, R., Rab, S. (2023). Applications of artificial intelligence in agriculture: A comprehensive review. Journal of Ambient Intelligence and Humanized Computing, 14 (5), 4315–4333. https://doi.org/10.1007/s12652-021-03491-8 (Consulted on 25/09/2025)

Kamilaris, A.; Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90. https://doi.org/10.1016/j.compag.2018.02.016 (Consulted on 25/09/2025)

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 (Consulted on 25/09/2025)

Li, D., Ma, J., Xu, X. (2022). Artificial intelligence for irrigation prediction and management: A review. Agricultural Water Management, 261, 107378. https://doi.org/10.1016/j.agwat.2021.107378 (Consulted on 25/09/2025)

Liakos, K. G., Busato, P., Moshou, D., Pearson, S., Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674. https://doi.org/10.3390/s18082674 (Consulted on 25/09/2025)

Liu, Y., Ma, X., Shu, L., Hancke, G. P., Abu-Mahfouz, A. M. (2021). From industry 4.0 to agriculture 4.0: Current status, enabling technologies, and research challenges. IEEE Transactions on Industrial Informatics, 17(6), 4322–4334. https://doi.org/10.1109/TII.2020.3003910 (Consulted on 25/09/2025)

Marvin, H. J. P., Kleter, G. A., Bouwmeester, H. (2022). Artificial intelligence in food supply chains: Enhancing resilience and efficiency. Trends in Food Science & Technology, 120, 215–227. https://doi.org/10.1016/j.tifs.2021.12.006 (Consulted on 25/09/2025)

Matemilola, S., Elegbede, I. (2017). The challenges of food security in Nigeria. Open Access Library Journal, 4(12), 1–22. https://doi.org/10.4236/oalib.1104185 (Consulted on 25/09/2025)

Nwajuibe, C. (2012). Nigeria’s food security challenges. In FAO country reports on food security. Rome: Food and Agricultural Organization of the United Nations.

OECD. (2020). Digital opportunities for better agricultural policies. Organisation for Economic Co-operation and Development. https://doi.org/10.1787/18156797 (Consulted on 25/09/2025)

Ogundeji, A. A., Donkor, E., Onakuse, S. (2020). The impact of mechanization on smallholder rice farmers in Nigeria. Agriculture, 10(12), 624. https://doi.org/10.3390/agriculture10120624 (Consulted on 25/09/2025)

Oyedele, O. A., Akinbile, L. A., Oladosu, R. O. (2021). Digital technologies and sustainable soil management in Africa: Opportunities and challenges. Journal of Sustainable Agriculture, 45(7), 1123–1136.

Rahman, M. A., Irfan, M., Alvi, S. H. (2023). Artificial intelligence in livestock farming: Applications, opportunities, and challenges. Artificial Intelligence in Agriculture, 7, 23–36. https://doi.org/10.1016/j.aiia.2023.04.003 (Consulted on 25/09/2025)

Rangrej, P., Sharma, A., Patel, R. (2023). Artificial intelligence applications in agriculture: A case study of India. International Journal of Agricultural Sustainability, 21(2), 158–171. https://doi.org/10.1080/14735903.2022.2109158 (Consulted on 25/09/2025)

Ryan, M., Peschl, M. F., McNamara, J. (2023). Artificial intelligence in agriculture: Impacts on innovation, sustainability, and food security. Agricultural Systems, 206, 103597. https://doi.org/10.1016/j.agsy.2023.103597 (Consulted on 25/09/2025)

Shepherd, K., Turner, J., Maas, B. (2020). Digital agriculture and food systems in Africa. Outlook on Agriculture, 49(1), 10–17. https://doi.org/10.1177/0030727020904941 (Consulted on 25/09/2025)

Smith, D., Jones, R., Brown, L. (2021). Artificial intelligence and animal health management: A review. Frontiers in Veterinary Science, 8, 678456. https://doi.org/10.3389/fvets.2021.678456 (Consulted on 25/09/2025)

Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039 (Consulted on 25/09/2025)

Upton, J. B., Cissé, J. D., Barrett, C. B. (2016). Food security as resilience: Reconciling definition and measurement. Agricultural Economics, 47(S1), 135–147. https://doi.org/10.1111/agec.12305 (Consulted on 25/09/2025)

Webb, P., Coates, J., Frongillo, E. A., Rogers, B. L., Swindale, A., Bilinsky, P. (2006). Measuring household food insecurity: Why it’s so important and yet so difficult to do. Journal of Nutrition, 136(5), 1404S–1408S. https://doi.org/10.1093/jn/136.5.1404S (Consulted on 25/09/2025)

World Bank. (2022). Transforming agriculture in Africa through digital technologies. World Bank Group.

WMO. (2023). State of the global climate 2022. World Meteorological Organization.

Xu, X., Li, Y., Zhang, W. (2020). Intelligent greenhouse systems using artificial intelligence and IoT. Computers and Electronics in Agriculture, 173, 105383. https://doi.org/10.1016/j.compag.2020.105383 (Consulted on 25/09/2025)

Lajoie-O’Malley, A., Bronson, K., Van Der Burg, S.; Klerkx, L. (2020). The future(s) of digital agriculture and sustainable food systems: An analysis of high-level policy documents. Ecosystem Services, 45, 101183. https://doi.org/10.1016/j.ecoser.2020.101183 (Consulted on 25/09/2025)