# Using UAS Imagery and Computer Vision to Support Site-Specific Weed Control in Corn

arXiv: https://arxiv.org/abs/2206.01734 · HTML: https://mlsanity.com/paper/2206.01734/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 133 references that are in the map.
- First posted on arXiv 2 Jun 2022.

## Builds on (most cited first, 3 of 3)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Towards agricultural autonomy: crop row detection under varying field conditions using deep learning](https://mlsanity.com/paper/2109.08247/) (2021)
- [Farm land weed detection with region-based deep convolutional neural networks](https://mlsanity.com/paper/1906.01885/) (2019)

## Built on by (most cited first, 1 of 1)

- [UAVs Meet Agentic AI: A Multidomain Survey of Autonomous Aerial Intelligence and Agentic UAVs](https://mlsanity.com/paper/2506.08045/) (2025)

## Abstract (arXiv)

Currently, weed control in a corn field is performed by a blanket application of herbicides that do not consider spatial distribution information of weeds and also uses an extensive amount of chemical herbicides. To reduce the amount of chemicals, we used drone-based high-resolution imagery and computer-vision techniques to perform site-specific weed control in corn.