How Artificial Intelligence is Revolutionizing Agricultural Business in Nigeria?

Authors

  • Olufemi Aladejebi The University of Lagos Business School, Akoka, Lagos, Nigeria
  • Bukola Amao-Taiwo University of Lagos, Nigeria Entrepreneurship and Skills Development Centre (ESDC), Akoka, Lagos, Nigeria
  • Oshinowo Bamidele University of Lagos, Faculty of Management Sciences Akoka, Lagos, Nigeria

DOI:

https://doi.org/10.14738/abr.1402.20078

Keywords:

Artificial Intelligence, Agriculture, Nigeria, Food Security, Digital Transformation

Abstract

Artificial intelligence (AI) is also becoming an acknowledged game-changer in the world of agriculture, and it has the potential to increase productivity, efficiency, and food security. This paper discussed how AI can transform agriculture in Nigeria, its present use, opportunities, challenges and the possible effects. Qualitative research design was used, and semi-structured interviews with major stakeholders of active Nigerian agribusiness firms using AI were used. The  companies offered their experience with AI-driven tools such as chatbots, predictive analytics, recommender systems, and pest detection models. The results showed that the early adoption of AI is transforming the agricultural practices by enhancing crop monitoring, improving farmer-market connections, optimising the use of inputs, and minimizing losses after harvesting. The companies cited positive effects of AI on productivity, farmer earnings, and rural development, and it was considered essential in enhancing food security and financial inclusion. But still, there were recurring issues that were found such as inadequate digital infrastructure, disjointed datasets, lack of digital literacy, and high implementation expenses. To solve these, companies implemented strategies like incremental implementation, open source technologies, offline solutions and collaborations with research institutions. The report concludes that AI can revolutionize the agricultural industry in Nigeria and lead to a tremendous economic growth, as long as systemic obstacles are overcome. Strategies to improve rural digital infrastructure, building centralised agricultural data systems, improving digital literacy, establishing inclusive financing systems, and collaboration between government, academia and the business sector are all recommended. These results can play a role in the continued discussion of digital agriculture and can serve as a means to implement AI usage in Sub-Saharan Africa on a larger scale.

References

Adeniyi, A., Onyekwena, C., &Adeyemi, O. (2024). A meta-review on the use of artificial intelligence in irrigation practices in Nigeria. African Journal of Agricultural Research, 20(3), 45-56

Amoussohoui, R., Arouna, A., Bavorova, M., Verner, V., Yergo, W., &Banout, J. (2024). Analysis of the factors influencing the adoption of digital extension services: evidence from the RiceAdvice application in Nigeria. The Journal of Agricultural Education and Extension, 30(3), 387-416.

Araújo, S. O., Peres, R. S., Barata, J., Lidon, F., & Ramalho, J. C. (2021). Key challenges & barriers to agricultural technology adoption. Retrieved from www.agtech.folio3.com

Audu, J., & Adegbenjo, A. (2022). A review of the applications of artificial intelligence in agriculture: Prospects and challenges in Nigeria. Retrieved from www.academia.edu

Audu, J., &Adegbenjo, A. (2022). A review of the applications of artificial intelligence in agriculture: Prospects and challenges in Nigeria. Journal of Agricultural Technology, 18(4), 112–125.

Ayoola, F., et al. (2025). Barriers and opportunities of artificial intelligence (AI) adoption in agricultural extension in Abuja, Nigeria. www.researchgate.net

Chatterjee, S., Kumar, P., & Mishra, S. (2024). Artificial intelligence in soil management: The new frontier of smart agriculture. Soil Science Annual, 75(2), 88-97

Page | 156 Scholar Publishing Eli-Chukwu, N. C. (2019). Applications of artificial intelligence in agriculture: A review. Engineering, Technology & Applied Science Research, 9(4), 4377-4383. doi:10.48084/etasr.2756

FAO. (2023). The state of food security and nutrition in the world 2023. Retrieved from www www.fao.org

FAO. (2023). The state of food security and nutrition in the world 2023. Food and Agriculture Organization of the United Nations. Retrieved from www.fao.org

Farmonaut. (2025). AI-powered farming: Boosting Jos agriculture by 20%. Farmonaut Agricultural Reports. Retrieved from https://farmonaut.com

García, L., Parra, L., Jimenez, J. M., Lloret, J., & Lorenz, P. (2020). IoT-based smart irrigation systems: An overview on the recent trends on sensors and IoT systems for irrigation in precision agriculture. Sensors, 20(4), 1042. doi:10.3390/s20041042

GSMA. (2024). AI for Africa: Use cases delivering impact. GSMA Mobile for Development. Retrieved from www.gsma.com/

Huntingford, C., Jeffers, E. S., Bonsall, M. B., Christensen, H. M., Lees, T., & Yang, H. (2019). Machine learning and artificial intelligence to aid climate change research and preparedness. Environmental Research Letters, 14(12), 124007. doi:10.1088/1748-9326/ab4e55

ICTworks. (2024). 9 ways artificial intelligence improves Nigerian agriculture outputs. ICTworks Technology Reviews. Retrieved from www.ictworks.org

Idowu, O. (2025). AI-driven soil mapping for enhanced agricultural productivity in Nigeria. African Journal of Soil Science, 15(1), 12–20. Retrieved from www.afrjsoilsci.org

Liakos, K. G., Busato, P., Moshou, D., Pearson, S., &Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674.

MDPI - Agriculture. (2024). Towards artificial intelligence applications in precision and sustainable agriculture. Agriculture, 14(2), 239

Ogunleye, A. A., &Akintade, O. O. (2024). Optimizing water resource management in agriculture using AI-powered solar irrigation systems. African Scholar Journal of Information Technology and Research, 5(3), 47–57.

Ojo, A., Adeyemi, O., & Okonkwo, C. (2025). GeaGrow: A mobile tool for soil nutrient prediction and fertilizer recommendation in Nigeria. Frontiers in Sustainable Food Systems, 9, 1533423.

Okolo, C. T. (2024). Using artificial intelligence in support of climate change adaptation in Africa. Humanities and Social Sciences Communications, 11(1), 1-10. Retrieved from www.nature.com

Omdena. (2025). Smart farming using AI for sustainable agriculture in Kano State, Nigeria. Omdena Project Reports. Retrieved from https://www.omdena.com

ReliefWeb. (2024). 26.5 million Nigerians projected to be food insecure in 2024. UN Office for the Coordination of Humanitarian Affairs. Retrieved from www.reliefweb.int

Russell, S. J., &Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.

World Bank. (2023). Nigeria economic update: Agriculture as a catalyst for growth. Retrieved from www.worldbank.org

Downloads

Published

2026-03-03

How to Cite

Aladejebi, O., Amao-Taiwo, B., & Bamidele, O. (2026). How Artificial Intelligence is Revolutionizing Agricultural Business in Nigeria?. Archives of Business Research, 14(02), 139–157. https://doi.org/10.14738/abr.1402.20078

Most read articles by the same author(s)

1 2 > >>