Global Journal of Engineering and Technology Research (GJETR) · Research article · Open access
Crop Protection and Productivity through the Adoption of Machine Learning Algorithms in Sub-Saharan Africa: A Systematic Review
Benjamin Yennuna Konyannik, Funchious Paul Mensah, Amos Ntow Djarbeng, Evans Oko Bapulah, Abdul-Jalil Salifu, Mohammed Yakubu, Liberty Lugu, Daniel Agbetawopkor Futorwu, Charles Yaw Gyamfi, Rufai Yakubu, Saeed Babanah Rawuf
22 November 2025 · Vol. 1, Issue 3, pp. 109-127
DOI: 10.65150/EP-gjetr/V1E3/2025-04
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What is this article about?
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.
Article at a glance
- Published
- 22 November 2025
- Volume / issue / pages
- Vol. 1, Issue 3, pp. 109-127
- ISSN
- 3051-3782 (online) · 3051-3774 (print)
- Licence
- CC BY 4.0 — free to read and reuse with attribution
- Peer review
- Double-blind, at least two independent reviewers
- Publisher
- EP Journals Group
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.
Keywords: Agricultural productivity, crop protection, machine learning, precision agriculture, Sub-Saharan Africa
Cite this article
Benjamin Yennuna Konyannik, Funchious Paul Mensah, Amos Ntow Djarbeng, Evans Oko Bapulah, Abdul-Jalil Salifu, Mohammed Yakubu, Liberty Lugu, Daniel Agbetawopkor Futorwu, Charles Yaw Gyamfi, Rufai Yakubu, & Saeed Babanah Rawuf (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{Benjamin2025,
title = {Crop Protection and Productivity through the Adoption of Machine Learning Algorithms in Sub-Saharan Africa: A Systematic Review},
author = {Benjamin Yennuna Konyannik and Funchious Paul Mensah and Amos Ntow Djarbeng and Evans Oko Bapulah and Abdul-Jalil Salifu and Mohammed Yakubu and Liberty Lugu and Daniel Agbetawopkor Futorwu and Charles Yaw Gyamfi and Rufai Yakubu and Saeed Babanah Rawuf},
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}
}Questions about this article
Who wrote this article?
Benjamin Yennuna Konyannik, Funchious Paul Mensah, Amos Ntow Djarbeng, Evans Oko Bapulah, Abdul-Jalil Salifu, Mohammed Yakubu, Liberty Lugu, Daniel Agbetawopkor Futorwu, Charles Yaw Gyamfi, Rufai Yakubu, Saeed Babanah Rawuf.
Where and when was it published?
In Global Journal of Engineering and Technology Research, Volume 1, Issue 3, pages 109-127, published by EP Journals Group on 22 November 2025.
Is this article free to read?
Yes. It is open access under a Creative Commons Attribution 4.0 (CC BY 4.0) licence, so anyone can read, share and reuse it with attribution.
What is the DOI of this article?
10.65150/EP-gjetr/V1E3/2025-04 (https://doi.org/10.65150/EP-gjetr/V1E3/2025-04).
How do I cite this article?
APA: Benjamin Yennuna Konyannik, Funchious Paul Mensah, Amos Ntow Djarbeng, Evans Oko Bapulah, Abdul-Jalil Salifu, Mohammed Yakubu, Liberty Lugu, Daniel Agbetawopkor Futorwu, Charles Yaw Gyamfi, Rufai Yakubu, & Saeed Babanah Rawuf (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
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