# Hydra: Accurate Multi-Modal Leaf Wetness Sensing with mm-Wave and Camera Fusion

arXiv: https://arxiv.org/abs/2508.02409 · HTML: https://mlsanity.com/paper/2508.02409/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 56 references that are in the map.
- First posted on arXiv 4 Aug 2025.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Convolutional Sequence to Sequence Learning](https://mlsanity.com/paper/1705.03122/) (2017)
- [Understanding Multi-Turn Toxic Behaviors in Open-Domain Chatbots](https://mlsanity.com/paper/2307.09579/) (2023)
- [RIDERS: Radar-Infrared Depth Estimation for Robust Sensing](https://mlsanity.com/paper/2402.02067/) (2024)
- [DynamicFL: Balancing Communication Dynamics and Client Manipulation for Federated Learning](https://mlsanity.com/paper/2308.06267/) (2023)

## Built on by (most cited first, 3 of 3)

- [InfoCom: Kilobyte-Scale Communication-Efficient Collaborative Perception with Information Bottleneck](https://mlsanity.com/paper/2512.10305/) (2025)
- [AIoT-based Continuous, Contextualized, and Explainable Driving Assessment for Older Adults](https://mlsanity.com/paper/2603.00691/) (2026)
- [Mimir: Physics-Grounded LLM Agents for Long-Horizon Irrigation Control](https://mlsanity.com/paper/2610.02038/) (2026)

## Abstract (arXiv)

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%.