Leveraging on Artificial Intelligence Technology in Boosting Cassava Production in Nigeria: The Gains and Constraints
DOI:
https://doi.org/10.64348/zije.2026560Keywords:
Artificial Intelligence (AI) technology, Cassava production, Boosting production, Gains, ConstraintsAbstract
Artificial Intelligence (AI) technology is reshaping agricultural landscape globally by employing data-driven, efficient, and scalable production methods. Nigeria, as the world’s largest producer of cassava, faces persistent challenges like low yield per hectare, pest infestations, climate variability, and inefficient farming techniques. This study focuses on the potential of AI technology to exploit and enhance the nation’s agricultural potentials and productivity. The study decisively examines the application of AI in cassava production in Nigeria, focusing on its gains and constraints. It adopted ways to addressing the critical challenges like climate variability, scarce access to digital farming techniques, and the need for sustainable practices. Using a descriptive and analytical approach based on secondary data from FAO, NBS, IITA, and recent scholarly publications, the study explores AI-driven technologies such as precision agriculture, machine learning, remote sensing, and automation. Findings from the study reveal that AI technology application in cassava production can significantly improve productivity, optimize resource utilization, and reduce post-harvest losses. However, adoption can be constrained by high costs, poor infrastructure, low digital literacy, and weak policy frameworks. This paper investigates how AI technology can be employed to achieve the mass production of cassava in Nigeria, while critically examining the associated gains or benefits and constraints.
References
Adah, Christopher Obe., Raji, Samuel Adeiza., Ufedo, Monday Shaibu., and Oluyomi, Moses Sunday (2024). Analyzing the adoption of improved production technologies among cassava contract and non-contract farmers in Kogi State, Nigeria. Tiknit Journal for Agricultural Sciences. https://www.researchgate.net DOI: https://doi.org/10.25130/tjas.24.3.14
Ajibefun, I.A and Adejumo, A.O (2018). Challenges and Prospective of small-scale farmers in accessing agricultural credit in Ondo State, Nigeria. International Journal of Economics, Commerce and Management, 6(6), 1-15.
Akis, Smart (2016). https://www.smartakis.com index.php/network/what-is-smart farming?
Babawurien, T., Ewin, D.R., Scott, T.O and Neye-Akogo, C. (2023). A Comprehensive Review of Wind Turbine Modeling for Addressing Energy Challenges in Nigeria and South Africa in the 41R Content. The Journal of Engineering and Exact Sciences. DOI: https://doi.org/10.18540/jcecvl9iss2pp15479-01e
Benos, L., Tagarakis, A., Dolias, G., Berruto, R., Kateris, D and Bochtis, D (2021). Machine learning in agriculture: A comprehensive updated review sensors. 21(11), 3758. https://doi.org/10.3390/s21113758. DOI: https://doi.org/10.3390/s21113758
Cassner, A., Harris, D., Mausch, K., Terheggen, A., Lopes, C., Finlayson, R.F., and Dobie, P. (2019). Poverty eradication and food security through agriculture in Africa: Rethinking Objectives and entry points. Outlook on Agriculture, 48(4), 309-315. DOI: https://doi.org/10.1177/0030727019888513
Chikoti, Patrick Chiza., & Temba, Mthias. (2022). Expansion and Impact of Cassava brown streak and cassava mosaic diseases in Africa: A review. Sec. Crop Biology and sustainability. Vol. 6. 2022/https://doi.org/10.3389/fsufs.2022.1076364 DOI: https://doi.org/10.3389/fsufs.2022.1076364
Food and Agriculture Organization (FAO). (2020). Climate change and Food security: Risks and Responses. Retrieved from https://.www.fao.org/3/ca3941en/ca3941enpdf
Food and Agriculture Organization (FAO). (2023). Digital agriculture and innovation.
International Institute of Tropical Agriculture (IITA). (2022). Cassava development report.
Iorliam, Aamo., Ilorliam, Iveren Blessing, and Bum, Sylvester. (2021). IoTs for Smart Agriculture in Nigeria and Africa. https://www.researchgate.net
Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture. Computers and Electronics in Agriculture. DOI: https://doi.org/10.1016/j.compag.2018.02.016
Kayode, Gideon (Sept, 2025). Using AI to Boost Cassava Farming in Nigeria.https://www.linkedln.com
Lasisi, Aminu Jacob, and Olufemi, Omitoyin (31st Dec., 2024). AI-Driven Farm-to-table supply chain optimization: A Case study of Reducing Food waste. Journal of Entomology and Agronomy studies. Berkeley Research and Publications International. Vol. 6, 2024. Berkely publications.com
Legg, J., Somado, E.A., Barker, I., Beach, L., Ceballos, H., and Cuellar, W. (2024). A global alliance declaring war on cassava viruses in Africa. Food Security. 6, 232-248.doi:10.1007/s12571-004-0340-x DOI: https://doi.org/10.1007/s12571-014-0340-x
National Bureau of Statistics (NBS), (2024). Agricultural Statistics Report.
Ogboru, Justina O (2025). Digital Technologies and Enhancing Agricultural Production among Farmers in South-West Nigeria. RSIS: International. https://rsisinternational.org DOI: https://doi.org/10.51584/IJRIAS.2025.10060051
Omoseni, Adepoju., Oluwadamilola, Esan., and Oluwarotimi, Akinyomi. (2022). Food Security in Nigeria; Enhancing Workers’ Productivity in Precision Agriculture. Journal of Digital Food, Energy and Water Systems. DOI: https://doi.org/10.36615/digital_food_energy_water_systems.v3i2.2269
Shuaibu, N.S., Obunaike, G.N., and Jamilu, B.A. (2024). Crop yield prediction using selected machine learning Algorithm. Fudma Journal of Sciences. DOI: https://doi.org/10.33003/fjs-2024-0801-2220
United Nations Development Programme (UNDP). (2023). Technology and development in Africa.
World Bank. (2023). Agriculture and economic growth in Nigeria.
Yakubu, M., Yakubu, U., Yakubu, H., and Mayun, F.A (2024). The effective use of AI in improving agricultural productivity in Nigeria. Journal of Basics and Applied Sciences Research. Vol. 2
No. 4 (2024)/Articles. DOI: https://doi.org/10.33003/jobasr-2024v24-58
