Robust Rainfall Estimation with Multimodal Sensing for Precision Residential Irrigation

Abstract

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 precision of rainfall data is compromised by the limited spatial resolution of the rain gauges and the significant variability of hyperlocal rainfall, resulting in 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 robustly 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 to five locations (collecting more than 750 hours of video) with diverse environmental backgrounds and varying camera placements. Our realistic evaluation validates that ERIC achieves state-of-the-art rainfall estimation performance (∼ 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.