Localising Weeds Using a Prototype Weed Sprayer

Darbyshire, Madeleine, Salazar-Gomez, Adrian, Lennox, Callum , Gao, Junfeng, Sklar, Elizabeth and Parsons, Simon (2022) Localising Weeds Using a Prototype Weed Sprayer. In: UKRAS22 Conference “Robotics for Unconstrained Environments”, 26 Aug 2022, Aberystwyth, Wales.

Full content URL: https://doi.org/10.31256/Ua7Pr2W

Localising Weeds Using a Prototype Weed Sprayer
Published manuscript
UK-RAS2022_proceedings_Paper-4.pdf - Whole Document

Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


The application of convolutional neural networks (CNNs) to challenging visual recognition tasks has been shown to be highly effective and robust compared to traditional machine vision techniques. The recent development of small, powerful GPUs has enabled embedded systems to incorporate real-time, CNN-based, visual inference. Agriculture is a domain where this technology could be hugely advantageous. One such application within agriculture is precision spraying where only weeds are targeted with herbicide. This approach promises weed control with significant economic and environmental benefits from re- duced herbicide usage. While existing research has validated that CNN-based vision methods can accurately discern between weeds and crops, this paper explores how such detections can be used to actuate a prototype precision sprayer that incorporates a CNN- based weed detection system and validates spraying performance in a simplified scenario.

Keywords:Robotics, Computer Vision, Agriculture
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:53105
Deposited On:06 Apr 2023 13:41

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