ERIC: Estimating Rainfall with Commodity Doorbell Camera for Precision Residential Irrigation

TL;DR

ERIC is a low-cost, privacy-preserving irrigation system that uses machine learning on commodity doorbell camera footage to accurately estimate rainfall, improving water efficiency and reducing waste in residential settings.

Contribution

The paper introduces a novel approach to estimate rainfall using commodity doorbell cameras with lightweight neural networks, enabling cost-effective and private irrigation optimization.

Findings

Achieves state-of-the-art rainfall estimation (~5mm/day)

Saves approximately 9,112 gallons of water per month

Cost of system is only $75 on Raspberry Pi 4

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 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