# See as a Bee: UV Sensor for Aerial Strawberry Crop Monitoring

arXiv: https://arxiv.org/abs/2210.16923 · HTML: https://mlsanity.com/paper/2210.16923/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 89 references that are in the map.
- First posted on arXiv 30 Oct 2022.

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

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Benchmark Analysis of Representative Deep Neural Network Architectures](https://mlsanity.com/paper/1810.00736/) (2018)
- [Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges](https://mlsanity.com/paper/1805.00881/) (2018)
- [Detecting Invasive Insects with Unmanned Aerial Vehicles](https://mlsanity.com/paper/1903.00815/) (2019)

## Abstract (arXiv)

Precision agriculture aims to use technological tools for the agro-food sector to increase productivity, cut labor costs, and reduce the use of resources. This work takes inspiration from bees vision to design a remote sensing system tailored to incorporate UV-reflectance into a flower detector. We demonstrate how this approach can provide feature-rich images for deep learning strawberry flower detection and we apply it to a scalable, yet cost effective aerial monitoring robotic system in the field. We also compare the performance of our UV-G-B image detector with a similar work that utilizes RGB images.