Distributed Deep Learning in IoT Sensor Network for the Diagnosis of Plant Diseases
Athanasios Papanikolaou, Athanasios Tziouvaras, George Floros, Apostolos Xenakis, Fabio Bonsignorio, Miguel A Zamora Izquierdo · 2025
Read Distributed Deep Learning in IoT Sensor Network for the Diagnosis of Plant Diseases by Athanasios Papanikolaou, Athanasios Tziouvaras, George Floros, Apostolos Xenakis, Fabio Bonsignorio, Miguel A Zamora Izquierdo (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12736812.txt.
Description
Athanasios Papanikolaou and co-authors build a system that spots plant disease from leaf photos without sending farm data to the cloud. Standard deep learning needs heavy compute and constant uploads, which field gear cannot support. Their design spreads the work across small field nodes and one edge node.
The nodes train a shared image model together, each keeping its own data, and their results are averaged. Two training setups were compared: one shared model, and a layered one that first names the crop, then its disease. The layered setup read leaves more truly across crops and weather shifts. The plain setup answered faster and used less power.
Catalog record
| CREATOR | Athanasios Papanikolaou, Athanasios Tziouvaras, George Floros, Apostolos Xenakis, Fabio Bonsignorio, Miguel A Zamora Izquierdo |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 57 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |