Summary of Eric: Estimating Rainfall with Commodity Doorbell Camera For Precision Residential Irrigation, by Tian Liu et al.
ERIC: Estimating Rainfall with Commodity Doorbell Camera for Precision Residential Irrigation
by Tian Liu, Liuyi Jin, Radu Stoleru, Amran Haroon, Charles Swanson, Kexin Feng
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Systems and Control (eess.SY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a cost-effective irrigation system, dubbed ERIC, which leverages machine learning models to estimate rainfall from doorbell camera footage and optimizes irrigation schedules without human intervention. The system employs novel visual and audio features with lightweight neural network models to infer rainfall at the edge, preserving user privacy. The ERIC system is deployed across five locations with varying backgrounds and light conditions, collecting over 750 hours of video data. The comprehensive 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a world where your lawn gets the right amount of water without wasting any! This paper talks about a new way to do that using special cameras and computers. Instead of relying on weather stations far away, this system uses cameras you already have at home to figure out when it’s raining. It’s like having a personal rain reporter! The team tested this system in five different locations and found that it saves a lot of water (over 9,000 gallons a month!) which means people can save money on their water bills too. |
Keywords
* Artificial intelligence * Machine learning * Neural network
