Development of High-Fidelity DenseNet Framework for Multi-Disease Real-Time Crop Health Surveillance and Farming Recommendation in Maize (Zea Maize) Cultivation in Nigeria

Abstract

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.

Plant phenotyping relevance

トウモロコシの病害状態を映像から推定する深層学習手法の開発・評価が中心であり、植物病害表現型の画像ベース計測に該当する。農薬推薦も含むが、病害分類性能が明示的に評価されている。

Code and data availability

The paper describes a curated field image dataset (4,500 maize images from Adamawa, Borno, and Taraba States) and a DenseNet-121 pipeline, but contains no data availability statement, no public dataset deposit, no code/model release, and no supplement with assets. All URLs in the text are references or the license, not

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