Global Journal of Engineering and Technology Research (GJETR)

Crop Protection and Productivity through the Adoption of Machine Learning Algorithms in Sub-Saharan Africa: A Systematic Review

Konyannik, Benjamin Yennuna, Mensah, Funchious Paul, Djarbeng, Amos Ntow, Bapulah, Evans Oko, Salifu, Abdul-Jalil, Yakubu, Mohammed, Lugu, Liberty, Futorwu, Daniel Agbetawopkor, Gyamfi, Charles Yaw, Yakubu, Rufai, Rawuf, Saeed Babanah

22 November 2025 · Vol. 1, Issue 3, pp. 109-127

DOI: 10.65150/EP-gjetr/V1E3/2025-04

Abstract

The economy of Sub-Saharan Africa depends heavily on agriculture, facing challenges like inefficient resource use, food insecurity, and climate shocks. This paper evaluates how machine learning (ML) could transform agricultural practices. We reviewed 88 studies (2010-2025) on ML applications like yield prediction, precision agriculture, and disease detection in countries like Rwanda, Nigeria, and Kenya. Systems like SMART-Crop Yield Prediction System predict maize yields with ≤0.177% inaccuracy using soil and meteorological data. CropGuard diagnoses crop diseases with 97% reli-ability using image recognition. ML can improve harvests by optimizing irrigation, cutting fertilizer waste by 30%, and enabling early pest action. Challenges include low technical literacy, erratic electricity, limited internet, and cultural resistance to adopting new tech. Scaling ML could strengthen food security, cut post-harvest losses, and em-power smallholder farmers. Success requires collaboration between governments, tech developers, and local communities. Priorities include developing farmer-friendly ML tools, funding rural infrastructure, and training initiatives. Sub-Saharan Africa may use ML for climate-resilient farms, stabilizing food supplies, and improving livelihoods by aligning tech with local needs, paving the way for sustainable agricultural growth.

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Konyannik, Benjamin Yennuna, Mensah, Funchious Paul, Djarbeng, Amos Ntow, Bapulah, Evans Oko, Salifu, Abdul-Jalil, Yakubu, Mohammed, Lugu, Liberty, Futorwu, Daniel Agbetawopkor, Gyamfi, Charles Yaw, Yakubu, Rufai, Rawuf, & Saeed Babanah (2025). Crop Protection and Productivity through the Adoption of Machine Learning Algorithms in Sub-Saharan Africa: A Systematic Review. Global Journal of Engineering and Technology Research, 1(3), 109-127. https://doi.org/10.65150/EP-gjetr/V1E3/2025-04

@article{Konyannik2025,
  title   = {Crop Protection and Productivity through the Adoption of Machine Learning Algorithms in Sub-Saharan Africa: A Systematic Review},
  author  = {Konyannik and Benjamin Yennuna and Mensah and Funchious Paul and Djarbeng and Amos Ntow and Bapulah and Evans Oko and Salifu and Abdul-Jalil and Yakubu and Mohammed and Lugu and Liberty and Futorwu and Daniel Agbetawopkor and Gyamfi and Charles Yaw and Yakubu and Rufai and Rawuf and Saeed Babanah},
  journal = {Global Journal of Engineering and Technology Research},
  year    = {2025},
  volume  = {1},
  number  = {3},
  pages   = {109-127},
  doi     = {10.65150/EP-gjetr/V1E3/2025-04},
  url     = {https://doi.org/10.65150/EP-gjetr/V1E3/2025-04}
}

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