Generating images of hydrated pollen grains using deep learning

Abstract

Pollen grains dehydrate during their development and following their departure from the host stigma. Since the size and shape of a pollen grain can be dependent on environmental conditions, being able to predict both of these factors for hydrated pollen grains from their dehydrated state could be beneficial in the fields of climate science, agriculture, and palynology. Here, we use deep learning to transform images of dehydrated Ranunculus pollen grains into images of hydrated Ranunculus pollen grains. We also then use a deep learning neural network that was trained on experimental images of different genera of pollen grains to identify the hydrated pollen grains from the generated transformed images, to test the accuracy of the image generation neural network. This pilot work demonstrates the first steps needed towards creating a general deep learning-based rehydration model that could be useful in understanding and predicting pollen morphology.