AD-DETR: A Real-Time Transformer with Multi-Scale Alignment and Spatial-Spectral Fusion for Crop Disease Detection.

Abstract

Agriculture faces significant challenges from crop diseases, which threaten global food security and cause substantial economic losses annually. While deep learning has advanced plant disease detection, existing models often struggle with generalization across heterogeneous environments and real-time deployment constraints, hindering their practical application in diverse agricultural settings. This paper proposes AD-DETR, an enhanced real-time detection transformer framework specifically designed for agricultural scenarios. The model incorporates three key innovations to address these issues. First, the Multi-Scale Align Network (MSANet) achieves adaptive feature alignment through an Adapt Fusion Align (AFA) block, effectively preserving disease detail information across varying scales. Second, the Spatial-Spectral Attentive Feature Fusion (SSAFF) module integrates frequency-domain processing with attention mechanisms, enhancing feature representation quality by combining spatial and spectral information. Third, the IPIoUv2 loss function improves bounding-box regression accuracy through an internal perception mechanism and scale-adaptive weighting. Comprehensive experiments demonstrate that AD-DETR achieves strong performance, with 90.2% mean average precision at IoU=0.5 on the Crop Disease dataset and 97.4% on the PlantDoc dataset. It maintains high efficiency with 16.4 million parameters, 47.2 GFLOPs computational complexity, and inference speeds of 230-242 frames per second. These results indicate that AD-DETR is robust to domain shift and suitable for resource-constrained applications, such as real-time monitoring on mobile and edge platforms.

Plant phenotyping relevance

植物病害の状態を画像から検出する深層学習手法を開発し、複数データセットで性能検証しており、フェノタイピング手法が研究の中心である。

Code and data availability

The paper uses a self-constructed Crop Disease dataset (37,714 images) and the public PlantDoc benchmark, but no block provides a public deposit, availability statement, or authors' URL for the dataset, images, annotations, or AD-DETR code/models. PlantDoc is cited prior work, not a paper-specific asset. No qualifying,

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