ADAM-DETR: an intelligent rice disease detection method based on adaptive multi-scale feature fusion.

Abstract

Rice diseases pose a severe threat to global food security, while traditional detection methods suffer from low efficiency and dependence on manual expertise. To address the challenges of insufficient feature extraction and poor multi-scale disease adaptability in existing deep learning approaches under complex field environments, this study proposes ADAM-DETR, a rice disease detection algorithm based on improved RT-DETR. We constructed the RiDDET-5 dataset containing 9,303 images covering five major disease categories. The algorithm innovatively designs three core modules: the AdaptiveVision Network (AVN) backbone for enhanced feature extraction, the Dual-Domain Enhanced Transformer (DDET) module for spatiotemporal-frequency domain collaboration, and the Adaptive Multi-scale Feature Model (AMFM) for improved feature fusion. Experimental results demonstrate that ADAM-DETR achieves 94.76% mAP@50 on the RiDDET-5 dataset, representing a 3.25% improvement over the baseline, and 83.32% mAP@50 on the public Kamatis dataset with a 2.19% enhancement, validating its cross-domain generalization capability. The algorithm requires only 42.8G FLOPs with 14.3M parameters, achieving an optimal balance between accuracy and efficiency, providing an effective technical solution for disease monitoring in smart agriculture.

Plant phenotyping relevance

イネ葉の病徴を画像から検出する深層学習手法を開発し、データセット上で性能評価しており、植物病害状態のフェノタイピング手法が中心である。

Code and data availability

The paper's primary RiDDET-5 rice disease dataset (9,303 annotated images) is available only by contacting the authors via email. Two public third-party Roboflow Universe datasets (Kamatis and a rice leaf disease dataset) were used for cross-domain evaluation and are publicly accessible. No author analysis code or pre-

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