Robot vision, deep learning, and robot navigation.
Papers and Presentations
- Rogers, H. and Fox, C., (2020). An Open Source Seeding Agri-Robot. UKRAS20 Conference: “Robots into the real world” Proceedings, 48-50. doi: 10.31256/Or6Mf2T
- Rogers, H. (2021). Robotic Manipulators in Agriculture: A Brief Review, ICRA Task-Informed Grasping Workshop – III, online. Watch here.
- De Silva, O., Heiwolt, K., Rogers, H., Wang, H., and Wang, N. (2021). RAS Counter: Non-invasive yield prediction for vineyards, AgriFoRwArdS CDT Annual Conference 2021, online.
- Member of the AgirFoRwArdS CDT Drink Outside the Box Organisation Committee.
My name is Harry, I joined the CDT in September 2020. Before this I was at the University of Lincoln completing a BSc in Computer Science looking to work in robotic software development. I am interested in robot vision, deep learning and robot navigation. During my undergraduate I published a paper about my dissertation project in which I built and programmed an agricultural robot, which dispensed seeds and was tracked via GPS.
During the MSc Robotics and Autonomous systems, I have worked part time on the BACCHUS project. This has been fun to be work on an actual project that will be deployed to have a system in a vineyard. I have also worked on a paper for ICRA Task-Informed Grasping Workshop with members from the CDT as well as the MSc. Finally, I have also helped setup the Drink Outside the Box as a member of the committee
What optimisation methods are required for DNNs on Embedded Systems?
Pest detection is becoming progressively significant throughout agriculture; pest resilience is developing into an increasingly complex issue. A Faster R-CNN will be deployed on multiple embedded systems to combat this with varying optimisations to conclude what optimisations are required for deployment on embedded systems. The Deep Neural Network (DNN) will be optimised with hyperparameter tuning, quantisation, and a novel custom pruning method. There will be conclusions drawn to show what optimisation methods are required for each embedded system with benefits and drawbacks.
Closing The Loop on Precision Spraying.
Despite the very long history of pesticides and herbicides it was not really until the world war II end that there was widespread use of synthesised chemicals in agriculture. Since then, there have been considerable developments often due to unintended or modelled consequences which usually amount to toxicity to animals or organisms essential to the ecosystem. The modern farmer is expected to be on top of considerable advice and guidance. Despite these advances the modern approach is known as “spray and pray” to imply that however carefully the diluted the chemicals are, there are always unintended consequences. A much-touted alternative is precision spraying in which exactly the right toxin is delivered to exactly the right place at exactly the right time. The technology already exists for the delivery of precise quantities of fluids through jets or aerosols. The question is, how feasible is it to monitor and control such systems in the field? That is what this project is about.
This project aims to incorporate improvements in Syngenta’s existing platforms for autonomous targeting of crops, weeds, pests, accurate dispensing of fluid. We will formulate quantifiable methodologies for post spraying effect monitoring to help Syngenta fulfil the regulatory guidelines for EU green deal. We will get access to the in-field/test equipment and relevant data (video/field maps) to build realistic and practical solutions in terms of precision spraying.