LSL-YOLO11n: a YOLO11n-based model for maize leaf disease detection in complex field environments

Abstract

Maize leaf diseases in field environments often exhibit large variations in lesion scale, irregular morphology, blurred boundaries, and complex backgrounds. These factors pose challenges for existing detection models, particularly in detecting small lesions and achieving precise bounding-box localization. To address these issues, this study proposes LSL-YOLO11n, a maize leaf disease detection model based on the YOLO11n framework. The proposed model improves feature representation, localization quality modeling, and bounding-box regression to enhance disease detection performance under complex field conditions. Experiments were conducted on a dataset containing 15,119 images and 29,366 annotated instances across eight categories, including seven maize disease categories and healthy leaves. To evaluate the effectiveness of the proposed model, ablation experiments, comparative experiments with mainstream object detection models, and visual detection analyses were carried out. The ablation results show that the improved components contribute positively to the overall detection performance. LSL-YOLO11n achieves a Precision of 84.4%, Recall of 73.9%, and mean Average Precision (mAP) of 83.3%, which is 3.1 percentage points higher than that of the baseline YOLO11n model. Compared with YOLOv8n, YOLOv9t, YOLOv10n, and YOLOv12n, the proposed model improves mAP by 4.7, 3.3, 5.3, and 10.9 percentage points, respectively. The visual detection results further indicate that LSL-YOLO11n performs more stably in complex backgrounds and small-lesion scenarios. These findings provide technical support for rapid maize disease recognition and intelligent field monitoring.

Plant phenotyping relevance

トウモロコシ葉の病斑・病害状態を画像から検出するYOLOベース手法を開発し、アブレーション比較や既存モデルとの性能評価を行っており、植物病害表現型の取得法が中心である。

Code and data availability

The supplied blocks describe a maize leaf disease detection dataset (15,119 images compiled from PlantVillage, PlantDoc, and Kaggle) and the LSL-YOLO11n model, but contain no data availability statement, code deposit, or authors' public URL for the dataset, annotations, trained model, or analysis code. The underlying图像

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