A domain adaptive wheat scab detection method for UAV images

Abstract

Wheat scab is a fungal disease that threatens wheat yield and quality worldwide. Accurate detection is critical for disease monitoring. However, detection is complicated by the diversity of wheat varieties and growing conditions, as well as challenges such as inconsistent object scales and excessive small objects in unmanned aerial vehicle (UAV) images. Additionally, acquiring large-scale annotated image samples is time-consuming and laborious. To address these challenges, this study proposes a domain adaptive wheat scab detection (DASD) method using UAV. The method first improves the layout images of the diffusion model to generate more realistic labeled target domain images, which are then used to fine-tune the wheat scab detection model. By aligning the feature distributions of the source and target domains, it reduces distribution shift between domains, thereby enhancing the detection model’s adaptation performance without the need for manual annotation of the target domain. Moreover, a Dynamic Multi-feature Fusion Block (DMFB) is designed and embedded into the Real-Time Detection with Transformer (RT-DETR) network to enhance the ability to adapt to varying disease sizes, thereby reducing missed and false detections. Finally, the effectiveness of the proposed method is validated through extensive comparative experiments conducted on multiple constructed datasets. Experimental results show that, without requiring labeled target domain data, the proposed method can improve the Average Precise (AP) value of detection results by 15–23 points compared to the baseline network, providing a feasible solution for large-scale wheat scab detection using UAV. The code is released at https://github.com/yangjie1874/DASD.

Plant phenotyping relevance

UAV画像から小麦赤かび病を検出するドメイン適応手法を開発し、複数データセットで比較検証しており、植物の病害状態の取得・推定が研究の中心です。

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