融合毫米波雷达与相机，精准监测叶片湿润时长。
Hydra: Accurate Multi-Modal Leaf Wetness Sensing with mm-Wave and Camera Fusion
- 用卷积网络融合毫米波深度图与可见光图像特征。
- 在多种环境下实现最高96%准确率，雨天等场景仍达90%。
- 适合农业病害预警，尤其适用于复杂光照条件。
叶面湿润持续时间（LWD）是植物病害发展的重要指标。现有检测方法缺乏标准化，且受植物形态和环境变化影响大，难以在真实农田中稳定应用。本文提出Hydra，通过融合毫米波雷达（76–81 GHz FMCW）与可见光相机，直接检测叶片表面是否有水。首先设计卷积神经网络（CNN），将多帧毫米波深度图与RGB图像进行选择性特征融合，生成多通道特征图；再通过基于Transformer的编码器捕捉特征图间的内在关联，生成最终特征表示，并送入分类器完成湿叶判定。训练阶段引入数据增强以提升泛化能力。实验表明，Hydra在不同植物上可实现高达96%的分类准确率；在雨天、清晨或弱光夜间等实际农场景下，仍能保持约90%的准确率，展现出显著的鲁棒性与实用性。
原文摘要 · Abstract (English)
Leaf Wetness Duration (LWD), the time that water remains on leaf surfaces, is crucial in the development of plant diseases. Existing LWD detection lacks standardized measurement techniques, and variations across different plant characteristics limit its effectiveness. Prior research proposes diverse approaches, but they fail to measure real natural leaves directly and lack resilience in various environmental conditions. This reduces the precision and robustness, revealing a notable practical application and effectiveness gap in real-world agricultural settings. This paper presents Hydra, an innovative approach that integrates millimeter-wave (mm-Wave) radar with camera technology to detect leaf wetness by determining if there is water on the leaf. We can measure the time to determine the LWD based on this detection. Firstly, we design a Convolutional Neural Network (CNN) to selectively fuse multiple mm-Wave depth images with an RGB image to generate multiple feature images. Then, we develop a transformer-based encoder to capture the inherent connection among the multiple feature images to generate a feature map, which is further fed to a classifier for detection. Moreover, we augment the dataset during training to generalize our model. Implemented using a frequency-modulated continuous-wave (FMCW) radar within the 76 to 81 GHz band, Hydra's performance is meticulously evaluated on plants, demonstrating the potential to classify leaf wetness with up to 96% accuracy across varying scenarios. Deploying Hydra in the farm, including rainy, dawn, or poorly light nights, it still achieves an accuracy rate of around 90%.
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