Towards scalable deep learning for automated microscopy in harmful algal bloom monitoring: Data-centric workflow and multi-region generalisation.
Saved in:
| Title: | Towards scalable deep learning for automated microscopy in harmful algal bloom monitoring: Data-centric workflow and multi-region generalisation. |
|---|---|
| Authors: | Taheriashtiani N; Department of Civil and Environmental Engineering, Monash University, Clayton, Victoria, Australia. Electronic address: negar.taheriashtiani@monash.edu., McGregor GB; Department of the Environment, Tourism, Science and Innovation, Brisbane, Queensland, Australia., Crosbie ND; Melbourne Water, Melbourne, Victoria, Australia., Hobson P; SA Water, Adelaide, South Australia, Australia., Trotta E; SA Water, Adelaide, South Australia, Australia., Lintern A; Department of Civil and Environmental Engineering, Monash University, Clayton, Victoria, Australia., Zamyadi A; Department of Civil and Environmental Engineering, Monash University, Clayton, Victoria, Australia. |
| Source: | Water research [Water Res] 2026 Aug 15; Vol. 301, pp. 126038. Date of Electronic Publication: 2026 Apr 29. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Pergamon Press Country of Publication: England NLM ID: 0105072 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2448 (Electronic) Linking ISSN: 00431354 NLM ISO Abbreviation: Water Res Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
| ISSN: | 1879-2448 |
|---|---|
| DOI: | 10.1016/j.watres.2026.126038 |
