Hydra: Accurate Multi-Modal Leaf Wetness Sensing with mm-Wave and Camera Fusion
Yimeng Liu, Maolin Gan, Huaili Zeng, Li Liu, Younsuk Dong, and Zhichao Cao

TL;DR
Hydra combines mm-Wave radar and camera data with advanced neural networks to accurately detect leaf wetness, improving robustness and precision in real-world agricultural environments.
Contribution
This paper introduces Hydra, a novel multi-modal sensing system that fuses mm-Wave radar and camera data using CNNs and transformers for accurate leaf wetness detection.
Findings
Achieves up to 96% accuracy in controlled tests.
Maintains around 90% accuracy in diverse farm conditions.
Demonstrates robustness across different environmental scenarios.
Abstract
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…
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