Unsupervised domain adaptation semantic segmentation method for wheat disease detection based on UAV multispectral images

Abstract

Wheat diseases have severely threatened global food security, making prompt and accurate detection methods crucial for disease control. However, large-scale detection methods face challenges such as low accuracy, labor-intensive labeling processes, and limited applicability across different wheat diseases. This study proposes a novel method using an Unsupervised Domain Adaptation (UDA) for semantic segmentation, termed DATS-ASSFormer, to detect wheat rust, wheat scab, and wheat yellow dwarf from UAV-based multispectral images. The proposed method employs Domain Adaptation via Teacher-Student networks (DATS) to generate pseudo-labels for unlabeled data in the target domain, and Adaptive Semantic Segmentation Former (ASSFormer) to precisely segment diseased areas, thus minimizing the dependency on laborious manual labeling. To support the development and evaluation of the model, the Northwest A&F University-Wheat Disease Remote Sensing Dataset (NWAFU-WDRSD) was developed, encompassing 8,628 images of the three predominant wheat diseases. Extensive testing confirmed that the DATS-ASSFormer model outperformed existing models in six domain adaptation tasks, achieving average Oracle (supervised training within the target domain) and UDA mIoU scores of 85.80 % and 65.96 %, respectively. These results significantly enhance detection accuracy and robustness across various diseases in real-world agricultural settings. The efficacy of DATS-ASSFormer highlights its potential for practical applications in precision agriculture, offering a scalable and efficient solution for large-scale wheat disease detection and management. The project is accessible at https://github.com/YcZhangSing/DATS-ASSFormer.

Plant phenotyping relevance

UAVマルチスペクトル画像から小麦病害領域を抽出するセマンティックセグメンテーション手法を開発・評価し、専用データセットも構築しているため、植物病害表現型の取得が中心である。

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