Advancing Rice Disease Detection in Farmland with an Enhanced YOLOv11 Algorithm.

Abstract

Smart rice disease detection is a key part of intelligent agriculture. To address issues like low efficiency, poor accuracy, and high costs in traditional methods, this paper introduces an enhanced lightweight version of the YOLOv11-RD algorithm, enhancing multi-scale feature extraction through the integration of the enhanced LSKAC attention mechanism and the SPPF module. It also lowers computational complexity and enhances local feature capture through the C3k2-CFCGLU block. The C3k2-CSCBAM block in the neck region reduces the training overhead and boosts target learning in complex backgrounds. Additionally, a lightweight 320 × 320 LSDECD detection head improves small-object detection. Experiments on a rice disease dataset extracted from agricultural operation videos demonstrate that, compared to YOLOv11n, the algorithm improves mAP50 and mAP50-95 by 2.7% and 11.5%, respectively, while reducing the model parameters by 4.58 M and the computational load by 1.1 G. The algorithm offers significant advantages in lightweight design and real-time performance, outperforming other classical object detection algorithms and providing an optimal solution for real-time field diagnosis.

Plant phenotyping relevance

イネ病害を対象とした画像ベースの検出手法を開発し、精度・計算量・リアルタイム性能を評価しているため、植物状態のフェノタイピング手法が中心である。

Code and data availability

The supplied blocks describe a rice disease dataset (5000 raw images from farming videos, curated to 4000 after augmentation) and a YOLOv11-RD model, but contain no public dataset deposit, no author code repository, no trained-model release, and no data availability statement. The dataset appears proprietary (collected

No evidence-backed public reproduction asset is currently recorded.