Deep Image Analysis for Microalgae Identification

Deep Image Analysis for Microalgae Identification

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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