# Hydra-Bench: A Benchmark for Multi-Modal Leaf Wetness Sensing

arXiv: https://arxiv.org/abs/2507.22685 · HTML: https://mlsanity.com/paper/2507.22685/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 32 references that are in the map.
- First posted on arXiv 30 Jul 2025.

## Builds on (most cited first, 2 of 2)

- [Artificial Intelligence of Things: A Survey](https://mlsanity.com/paper/2410.19998/) (2024)
- [Leafeon: Towards Accurate, Robust and Low-cost Leaf Water Content Sensing Using mmWave Radar](https://mlsanity.com/paper/2410.03680/) (2024)

## Built on by (most cited first, 2 of 2)

- [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 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.