构建多模态叶片湿度数据集，提升农业病害监测精度
Hydra-Bench: A Benchmark for Multi-Modal Leaf Wetness Sensing
- 融合毫米波、合成孔径雷达与可见光图像，同步采集六月数据
- 在多种植物和环境条件下，验证多模态融合优于单一模态
- 适合农业物联网、智能植保与遥感算法研究者参考
叶片湿度检测是农业监测中的关键任务，直接影响作物病害预测与防护。现有传感器在真实动态环境下存在鲁棒性差、精度低和环境适应性不足的问题。为此，我们构建了一个面向叶片湿度检测的新型多模态基准数据集，包含六个月内从五种不同植物在受控与户外田间环境中采集的同步毫米波原始数据、合成孔径雷达（SAR）图像和RGB图像。我们基于Hydra模型提供详细基准测试，包括单模态基线对比、多种融合策略评估以及不同扫描距离下的性能表现。该数据集还可用于未来SAR成像算法优化，实现多样环境条件下检测精度的系统性评估。
原文摘要 · Abstract (English)
Leaf wetness detection is a crucial task in agricultural monitoring, as it directly impacts the prediction and protection of plant diseases. However, existing sensing systems suffer from limitations in robustness, accuracy, and environmental resilience when applied to natural leaves under dynamic real-world conditions. To address these challenges, we introduce a new multi-modal dataset specifically designed for evaluating and advancing machine learning algorithms in leaf wetness detection. Our dataset comprises synchronized mmWave raw data, Synthetic Aperture Radar (SAR) images, and RGB images collected over six months from five diverse plant species in both controlled and outdoor field environments. We provide detailed benchmarks using the Hydra model, including comparisons against single modality baselines and multiple fusion strategies, as well as performance under varying scan distances. Additionally, our dataset can serve as a benchmark for future SAR imaging algorithm optimization, enabling a systematic evaluation of detection accuracy under diverse conditions.
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