用门铃摄像头估算降雨，智能节水灌溉。
ERIC: Estimating Rainfall with Commodity Doorbell Camera for Precision Residential Irrigation
- 用轻量模型从摄像头视频中提取视觉音频特征估雨。
- 月均节水9112加仑，每月省28.56美元水费。
- 低成本部署在树莓派上，适合家庭自动灌溉场景。
现有住宅灌溉系统如WaterMyYard依赖附近气象站的降雨数据，但雨量计空间分辨率低且局地降雨差异大，导致大量水资源浪费。为提升灌溉效率，我们开发了名为ERIC的低成本系统，利用机器学习模型从商品门铃摄像头视频中估算降雨，并自动优化灌溉计划。具体包括：(a) 设计新型视觉与音频特征，结合轻量神经网络在边缘端推断降雨，保障用户隐私；(b) 在树莓派4上构建完整端到端灌溉系统，成本仅75美元。我们在五个不同背景和光照条件的地点部署系统，累计采集超750小时视频。综合评估表明，ERIC实现当前最优的降雨估计性能（约5mm/天），月均节水9,112加仑，相当于每月节省28.56美元水费。数据与代码已开源。
原文摘要 · Abstract (English)
Current state-of-the-art residential irrigation systems, such as WaterMyYard, rely on rainfall data from nearby weather stations to adjust irrigation amounts. However, the accuracy of rainfall data is compromised by the limited spatial resolution of rain gauges and the significant variability of hyperlocal rainfall, leading to substantial water waste. To improve irrigation efficiency, we developed a cost-effective irrigation system, dubbed ERIC, which employs machine learning models to estimate rainfall from commodity doorbell camera footage and optimizes irrigation schedules without human intervention. Specifically, we: a) designed novel visual and audio features with lightweight neural network models to infer rainfall from the camera at the edge, preserving user privacy; b) built a complete end-to-end irrigation system on Raspberry Pi 4, costing only \$75. We deployed the system across five locations (collecting over 750 hours of video) with varying backgrounds and light conditions. Comprehensive evaluation validates that ERIC achieves state-of-the-art rainfall estimation performance ($\sim$ 5mm/day), saving 9,112 gallons/month of water, translating to \$28.56/month in utility savings. Data and code are available at https://github.com/LENSS/ERIC-BuildSys2024.git
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