# BeeNet: Reconstructing Flower Shapes from Electric Fields using Deep Learning

arXiv: https://arxiv.org/abs/2508.11724 · HTML: https://mlsanity.com/paper/2508.11724/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 58 references that are in the map.
- First posted on arXiv 15 Aug 2025.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [3D Deep Learning on Medical Images: A Review](https://mlsanity.com/paper/2004.00218/) (2020)
- [A Deep Learning Scheme of Electromagnetic Scattering From Scatterers With Incomplete Profiles](https://mlsanity.com/paper/2505.02086/) (2025)
- [EMulator: Rapid Estimation of Complex-valued Electric Fields using a U-Net Architecture](https://mlsanity.com/paper/2505.02095/) (2025)

## Abstract (arXiv)

Pollinating insects can obtain information from electric fields arising from flowers. The density and usefulness of electric information remain unknown. Here, we show that electric information can be used to reconstruct geometrical features of the field source. We develop an algorithm that infers the shapes of polarisable flowers from the electric field generated in response to a nearby charged arthropod. We computed the electric fields arising from arthropod flower interactions for varying petal geometries, and used these data to train a deep learning U Net model to recreate the floral shapes. The model accurately reconstructed diverse shapes, including more complex flower morphologies not included in training. Reconstruction performance peaked at an optimal arthropod flower distance, indicating distance dependent encoding of shape information. These findings indicate that electroreception can impart rich spatial detail, offering insights into the electric ecology of arthropods. Together, this work introduces a deep learning framework for solving the inverse electrostatic imaging problem, enabling object shape reconstruction directly from measured electric fields.