Water plastic pollution is a serious
problem affecting sealife,
marine habitats, and the food chain. Artificial intelligence-enabled
coherent imaging has recently shown exciting advances in the field
of environmental monitoring, and portable holographic microscopes
are good candidates to map the microparticles content of marine waters.
The “holographic fingerprint” due to coherent light
diffraction is rich in information, fully encoded into the complex
wavefront scattered by the sample. Hence, proper analysis of the wavefronts
reconstructed from digital holograms can unlock new possibilities
in the fields of diagnostics and environmental monitoring. Fractal
geometry well describes natural objects and allows inferring added-value
information on the way these fill 2D spaces and 3D volumes. The most
abundant micron-scale class of objects that populate marine waters
consists of microalgae named diatoms, which are of interest as bioindicators
of water quality. Here we investigate the fractal properties of holographic
patterns of diatoms and microplastics, considering a heterogeneous
mixture of five types of plastic materials and 55 different species
of microalgae. We show that, different from the case of weak scattering
objects, a small set of fractal parameters is able to characterize
these two large ensembles. As an applicative example, we carry out
classification tests to show the possibility to identify the two classes
with high accuracy. This new holographic fractal description of scattering
micro-objects could be used in the near future for in situ automatic
mapping of microplastic pollutants and for taxonomy of diatoms as
water quality bioindicators, screened onboard holographic systems.