BeeNet: Reconstructing Flower Shapes from Electric Fields using Deep Learning
This provides insights into arthropod environmental perception through electroreception, though it appears incremental as it applies existing deep learning methods to a novel but specific biological data type.
The researchers tackled the problem of reconstructing flower shapes from electric field data by developing a deep learning UNet model trained on simulated bee-flower interaction data, achieving accurate reconstruction of diverse flower shapes including complex ones not seen during training with performance peaking at an optimal distance.
Arthropods, including pollinators, respond to environmental electrical fields. Here, we show that electric field information can be decoded to reconstruct environmental features. We develop an algorithm capable of inferring the shapes of polarisable flowers from the electric field generated by a nearby charged bee. We simulated electric fields arising from bee flower interactions for flowers with varying petal geometries. These simulated data were used to train a deep learning UNet model to recreate petal shapes. The model accurately reconstructed diverse flower shapes including more complex flower shapes not included in training. Reconstruction performance peaked at an optimal bee flower distance, indicating distance-dependent encoding of shape information. These findings show that electroreception can impart rich spatial detail, offering insights into arthropod environmental perception.