Assessing the stability of indoor farming systems using data outlier detection
Jean Pompeo, Ziwen Yu, Chi Zhang, Songzi Wu, Ying Zhang, Celina Gomez, Melanie Correll · 2025
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Description
Pompeo and six co-authors assess stability in indoor farming through outlier detection in a paper published in 2024. The test system grew lettuce at several temperatures with low-cost internet sensors. Bad air temperature data can hide system faults and hurt efficiency.
The team linked the count of farm operations to odd readings in temperature data. As operations built up, outlier scores rose, which points to falling stability. Odd readings may stem from sensor noise, drift, or other data faults. Some trials grew more resistant over time. A log-normal curve fit the leftover data best. The authors suggest better sensors, backup data paths, and sharper models.
Catalog record
| CREATOR | Jean Pompeo, Ziwen Yu, Chi Zhang, Songzi Wu, Ying Zhang, Celina Gomez, Melanie Correll |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 59 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |