In-flight sensing of pollen grains via laser scattering and deep learning

The identification and imaging of pollen grains in-flight was performed via illumination of the pollen grains with three collinear laser beams that had central wavelengths of 450 nm, 520 nm and 635 nm. Two neural networks are reported here; the first neural network was able to categorise pollen grain species from their scattering patterns with ~86% accuracy, while the second neural network generated images of the pollen grains from their scattering patterns. This work demonstrates the potential application of laser scattering and deep learning for real-world in-flight pollen identification.