Multi-scale closed-loop tuning via spatial frequency collaborative sensitivity for rice leaf disease detection.

Abstract

Rice is a fundamental food source for more than half of the global population, making stable yields and quality improvements vital for food security and sustainable agricultural development. Early infections of rice leaf diseases often exhibit subtle symptoms, while conventional control methods based on empirical judgment and routine pesticide application result in both yield losses and environmental pollution. A Multi-scale closed-loop tuning via spatial frequency collaborative sensitivity (MCCA-YOLO) model has been proposed in this paper with a multiscale closed-loop tuning and spatial frequency collaborative attention mechanism for the early detection and classification of rice crop diseases. MCCA-YOLO incorporates a closed-loop tuning compound network architecture that combines a dual-backbone feature extractor with a spatial frequency enhancement module to achieve system self-verification feedback, reducing transmission errors and enhancing the texture features of leaves. The framework implements a cross-scale weighted fusion and a deformable spatial hybrid attention enhanced bidirectional feature pyramid fusion network for dynamic feature adaptation, effectively accommodating the complex morphology of rice leaf lesions. By conducting comprehensive ablation studies and comparative experiments with existing techniques on the rice plant diseases v8 dataset, the proposed approach achieves a mean average precision (mAP) of 92.2%, outperforming well-established methods, while delivering superior precision (0.915) and recall (0.900). Extensive empirical validation of additional v9 and Rice Leaf Spot Disease (RLSD) datasets for rice plant diseases further demonstrates the model's outstanding performance.

Plant phenotyping relevance

イネ葉の病徴を画像から検出・分類するYOLOベース手法を開発し、アブレーション、比較実験、複数データセットで性能検証しており、植物病害表現型の取得手法が中心である。

Code and data availability

The paper's rice leaf disease image datasets (Roboflow v8/v9, Kaggle RLSD) are explicitly declared publicly available, and the authors' MCCA-YOLO analysis code is stated to be open source on GitHub with a public URL.