Abstract

Abstract: Agricultural robotics present a possible solution to a number of inter-related social challenges including reducing fuel consumption and chemical application, reducing carbon emissions, and reducing dependency on manual labour. They may do this by combining automation with precision, ultimately at the level of per-plant monitoring, nurturing, and harvesting.

This talk will give a high-level overview of Agricultural Robotics and some examples from our previous and current projects such as the IBEX autonomous grassland weedsprayer (Video example: https://www.youtube.com/watch?v=eY3TVErkWlg) and our new robot, Theresa, under development for blackgrass control in fields of wheat.

Lincoln’s focus is on practical robotics, but this focus raises more theoretical questions which may be of interest in future collaborations. These include topics around 3D path planning, RF (radio) communications and localisation and machine vision. Agricultural robots may also generate large geospatial datasets which may be explored by machine learning; and have connections to Internet of Things devices, Transport Studies and Operations Research when considered as part of wider digital food supply chains.

The Universities of Lincoln, Cambridge, and East Anglia are beginning a 6.5MGBP Center for Doctoral Training in Agricultural Robotics – the largest training programme in this area in the world – and I will discuss possibilities for future collaboration in the area between our universities.

Bio: Charles Fox studied Computer Science at Cambridge (Clare, 97), Cognitive Science at Edinburgh, and obtained his DPhil in Robotics at Oxford. He was the leader of the InnovateUK IBEX2 Agricultural Robotics project. He worked as a high frequency trader for hedge fund Algometrics and as a AI robotics researcher at the Universities of Sheffield and Leeds before taking up a lectureship at the University of Lincoln. He is the author of the Springer textbook “Data Science for Transport”.

REFERENCES

Duckett et al, 2018. Agricultural Robotics: The future of Robotic Agriculture, UK-RAS white paper, https://arxiv.org/abs/1806.06762

Basu et al. 2018 Legal framework for small autonomous agricultural robots. AI & Society, https://link.springer.com/article/10.1007/s00146-018-0846-4

Binch&Fox, 2017. Controlled comparison of machine vision algorithms for Rumex and Urtica detection in grassland. Computers and Electronics in Agriculture, http://eprints.lincoln.ac.uk/32031/1/FoxBinch2017WeedsIbex.pdf.