ARCHIVES
Original Article
A Hybrid Machine Learning Model for Predicting Diseases in Coffee Production: A case study from Kenya
Joseph Kioko Munyao1
Cynthia Ikamari2
Shadrack Madila3
1 2 3 Department of computer science and i.t, Cooperative University of Kenya, Kenya.
Published Online: September-December 2025
Pages: 121-126
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250403022
References
1. 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
2. Kenya Agricultural Research Institute (KARI). (2023). Impact and potential improvements of AI in coffee farming in Kenya. Nairobi: KARI
Publications.
3. van den Berg, M., Hengsdijk, H., Wolf, J., van Ittersum, M. K., & Rötter, R. P. (2020). The future of cropping systems in Sub-Saharan Africa:
Challenges and opportunities for climate-smart agriculture. Agricultural Systems, 184, 102913. https://doi.org/10.1016/j.agsy.2020.102913
4. The Farming Insider. (2020). Challenges facing coffee farmers in Kenya: Climate change and disease management. Retrieved from
https://www.farminginsider.org/kenya-coffee-climate-disease
5. 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
6. Zhao, M., Kong, W., Lei, X., Song, C., & Liu, F. (2020). Smart agriculture: An overview from the perspective of the Internet of Things and
Artificial Intelligence. Computers and Electronics in Agriculture, 177, 105709. https://doi.org/10.1016/j.compag.2020.105709
7. Feng, Y., Wu, D., Zhu, Y., He, Y., & Gong, D. (2019). 1_combining hyperspectral, mid-infrared, and laser-induced breakdown spectroscopy
data for rice disease detection." Computers and Electronics in Agriculture, 158, 1–9. https://doi.org/10.1016/j.compag.2019.01.019
8. Zhang, L., Pang, J., Chen, X., & Lu, Z. (2024). 3.Drone-Based Enhanced Pest and Disease Detection in Agriculture Using Deep Learning.
ATAI Machine Learning, 3(1), 1–10.
9. García, L., Parra, L., Jiménez, J.M., Lloret, J., & Lorenz, P. (2020). "IoT-Based Smart Irrigation Systems: An Overview on the Recent Trends
on Sensors and IoT Systems fonr Irrigation in Precision Agriculture." Sensors, 20(4), 1042. https://doi.org/10.3390/s20041042
90. https://doi.org/10.1016/j.compag.2018.02.016
2. Kenya Agricultural Research Institute (KARI). (2023). Impact and potential improvements of AI in coffee farming in Kenya. Nairobi: KARI
Publications.
3. van den Berg, M., Hengsdijk, H., Wolf, J., van Ittersum, M. K., & Rötter, R. P. (2020). The future of cropping systems in Sub-Saharan Africa:
Challenges and opportunities for climate-smart agriculture. Agricultural Systems, 184, 102913. https://doi.org/10.1016/j.agsy.2020.102913
4. The Farming Insider. (2020). Challenges facing coffee farmers in Kenya: Climate change and disease management. Retrieved from
https://www.farminginsider.org/kenya-coffee-climate-disease
5. 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
6. Zhao, M., Kong, W., Lei, X., Song, C., & Liu, F. (2020). Smart agriculture: An overview from the perspective of the Internet of Things and
Artificial Intelligence. Computers and Electronics in Agriculture, 177, 105709. https://doi.org/10.1016/j.compag.2020.105709
7. Feng, Y., Wu, D., Zhu, Y., He, Y., & Gong, D. (2019). 1_combining hyperspectral, mid-infrared, and laser-induced breakdown spectroscopy
data for rice disease detection." Computers and Electronics in Agriculture, 158, 1–9. https://doi.org/10.1016/j.compag.2019.01.019
8. Zhang, L., Pang, J., Chen, X., & Lu, Z. (2024). 3.Drone-Based Enhanced Pest and Disease Detection in Agriculture Using Deep Learning.
ATAI Machine Learning, 3(1), 1–10.
9. García, L., Parra, L., Jiménez, J.M., Lloret, J., & Lorenz, P. (2020). "IoT-Based Smart Irrigation Systems: An Overview on the Recent Trends
on Sensors and IoT Systems fonr Irrigation in Precision Agriculture." Sensors, 20(4), 1042. https://doi.org/10.3390/s20041042
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