Vollständiger Abstract
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In Nigeria, maize is the most widely cultivated grain, largely supporting food security for about half of the population. Research has indicated that there is annual production is declaiming to approximately 50% in maize, this is due to crop diseases and pest damage. This research presents a deep learning surveillance system for crop disease prediction and pesticides recommendations based on a DenseNet-121 architecture for continuous video streams. The research was evaluated on a curated field dataset collected from three states in Nigeria; Adamawa, Borno, and Taraba State. The system achieved a mean accuracy of 98.2% and a mean F1-score of 0.982. The results reflect a strong discriminative capacity across the diverse textural maize diseases.
Bibliografischer Nachweis
Publikationsdaten
- Autor:innen
- Aisha Muhammad Hussein, Alhassan AbdulMutallib, Hyellamada Simon, Solomon Makasda Dickson, Sani Umar, Suleiman Muhammad Aliyu, Ruth Samuel
- Quelle
- FUDMA Journal of Sciences
- Publikation
- 2026-08-18
- Band / Ausgabe
- 10 / 13
- Seiten
- 293-297
- ISSN / ISBN
- 2616-1370, 2645-2944
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Zitierfähiger Nachweis
Aisha Muhammad Hussein, Alhassan AbdulMutallib, Hyellamada Simon, Solomon Makasda Dickson, Sani Umar, Suleiman Muhammad Aliyu, Ruth Samuel (2026). Development of High-Fidelity DenseNet Framework for Multi-Disease Real-Time Crop Health Surveillance and Farming Recommendation in Maize (Zea Maize) Cultivation in Nigeria. FUDMA Journal of Sciences, 10 (13), 293-297. https://doi.org/10.33003/fjs-2026-1013-5595
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