A System for the Recognition of Some Selected Grain Plant Leaves Using Deep Learning Algorithms

Abstract

Manual inspection of grain plant leaves for defects is subjective and labor-intensive. Few studies have compared deep learning methods on a combined multi-crop dataset. The study collected locally 5,640 leaf images from rice, maize, and guinea corn farms in Nigeria and grouped them into six classes representing defective and healthy leaves for each crop. Three models were trained: YOLOv8 for end-to-end detection and classification, EfficientNetB0 for standalone image classification, and a hybrid that used YOLOv8 for leaf detection followed by EfficientNetB0 for patch classification. The hybrid achieved 99.85% accuracy on the test set, slightly above EfficientNetB0 (99.82%) and YOLOv8 (mAP 0.995). The hybrid also supplies bounding box locations, helping farmers identify exactly where damage appears. This system offers a reliable, field-deployable tool for monitoring grain crop health.

Plant phenotyping relevance

穀物葉の健全・欠損状態を画像から検出・分類する深層学習システムの開発とモデル比較が中心であり、植物の病害・損傷状態を直接推定するため、植物フェノタイピング手法に該当する。

Code and data availability

The paper describes a locally collected 5,640-image multi-crop leaf dataset and YOLOv8/EfficientNetB0/hybrid models, but provides no public deposit, repository, or URL for the dataset, annotations, code, or trained models. The GrainLeaf6 dataset is only mentioned as a future-work release plan, so no paper-specific,公开,

No evidence-backed public reproduction asset is currently recorded.