# WeedScout: Real-Time Autonomous blackgrass Classification and Mapping using dedicated hardware

arXiv: https://arxiv.org/abs/2405.07349 · HTML: https://mlsanity.com/paper/2405.07349/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 12 May 2024.

## Builds on (most cited first, 5 of 5)

- [A Survey of Modern Deep Learning based Object Detection Models](https://mlsanity.com/paper/2104.11892/) (2021)
- [A Survey of Deep Learning Techniques for Weed Detection from Images](https://mlsanity.com/paper/2103.01415/) (2021)
- [BonnBot-I: A Precise Weed Management and Crop Monitoring Platform](https://mlsanity.com/paper/2307.12588/) (2023)
- [Strawberry Detection Using a Heterogeneous Multi-Processor Platform](https://mlsanity.com/paper/2011.03651/) (2020)
- [Deep Learning approach for Classifying Trusses and Runners of Strawberries](https://mlsanity.com/paper/2207.02721/) (2022)

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

- [Improving Lightweight Weed Detection via Knowledge Distillation](https://mlsanity.com/paper/2507.12344/) (2025)

## Abstract (arXiv)

Blackgrass (Alopecurus myosuroides) is a competitive weed that has wide-ranging impacts on food security by reducing crop yields and increasing cultivation costs. In addition to the financial burden on agriculture, the application of herbicides as a preventive to blackgrass can negatively affect access to clean water and sanitation. The WeedScout project introduces a Real-Rime Autonomous Black-Grass Classification and Mapping (RT-ABGCM), a cutting-edge solution tailored for real-time detection of blackgrass, for precision weed management practices. Leveraging Artificial Intelligence (AI) algorithms, the system processes live image feeds, infers blackgrass density, and covers two stages of maturation. The research investigates the deployment of You Only Look Once (YOLO) models, specifically the streamlined YOLOv8 and YOLO-NAS, accelerated at the edge with the NVIDIA Jetson Nano (NJN). By optimising inference speed and model performance, the project advances the integration of AI into agricultural practices, offering potential solutions to challenges such as herbicide resistance and environmental impact. Additionally, two datasets and model weights are made available to the research community, facilitating further advancements in weed detection and precision farming technologies.