Deep Image Analysis for Microalgae Identification
- Publisher:
- Springer Nature
- Publication Type:
- Chapter
- Citation:
- Information Integration and Web Intelligence, 2023, 14416 LNCS, pp. 280-292
- Issue Date:
- 2023-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 978-3-031-48316-5.pdf | Published version | 29.99 MB |
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The current management of microalgae cultivation requires manual microscopic examination in order to identify desired and competing species, as well as predators. In this study, we trained and tested a transfer learning model modified from EfficientNetV2 B3 model on 434 and 161 prospectively acquired images of the preferred Nanno-chloropsis sp microalgae and competitor Spirulina, respectively, and achieved >98% classification for both species on tenfold cross-validation. The model was further enhanced with gradient-weighted class activation mapping, which allowed visualisation of regions of the input images that were relevant to the classification, thereby improving its explainability. In this paper, we demonstrate that a simple deep transfer learning model can help microalgae farmers to identify and manage microalgae species. The application could enable the widespread adoption of microalgae by more farmers as an enviroment-friendly, drought-proof, and high-productive crop that can be grown on non-arable land and use waste water.
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