Closing the Loop on Precision Spraying

This project aims to incorporate improvements in Syngenta's existing platforms for autonomous targeting of crops, weeds, pests by accurate dispensing of herbicide and pesticides. 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 and other test equipment and relevant data (video and field maps) from Syngenta to build realistic and practical solutions in terms of precision spraying.

Project Objectives:
Use computer vision techniques to allow computer vision model to distinguish if weeds have been sprayed, using multiple state-of-the-art deep learning architectures such as Mask R-CNN, and Neural ODE networks, this will be close to water droplet/spraying recognition.
Use existing technologies to track spray and minimise spray drift to optimise trajectory.
Create on-device and explainable AI (XAI) methodologies for real time usage of object systems created, using quantisation, pruning and other compression methods.
Create a ROS/Gazebo simulation for the existing system and test capabilities, looking at navigation, spray drift, GIS data and SLAM.

Project Deliverables:
Spraying recognition/monitoring system.
Trajectory optimisation system.
Optimised methodology for systems.
Evaluation of various components in simulation showing optimisations and the overall system.

Training Environment:
UEA, Syngenta, University of Lincoln and collaboration between other relevant national and international forums.
The student will be embedded in the Smart Emerging Technologies lab in UEA's School of Computing Sciences, and wil benefit from the Faculty of Science Graduate School's extensive programme of personal and professional development
Training needs will be assessed at the outset of the PhD and annually thereafter as part of formal progress review meetings

Risk Mitigation:
Alternative plans for simulations in case of failure to access data from real scenarios.
Regular meetings and communication with the supervisory team and Syngenta will be maintained to keep things on track.

The proposed CDT provides a unique vision of advanced RAS technologies embedded throughout the food supply chain, training the next generation of specialists and leaders in agri-food robotics and providing the underpinning research for the next generation of food production systems. These systems in turn will support the sustainable intensification of food production, the national agri-food industry, the environment, food quality and health.

RAS technologies are transforming global industries, creating new business opportunities and driving productivity across multiple sectors. The Agri-Food sector is the largest manufacturing sector of the UK and global economy. The UK food chain has a GVA of £108bn and employs 3.6m people. It is fundamentally challenged by global population growth, demographic changes, political pressures affecting migration and environmental impacts. In addition, agriculture has the lowest productivity of all industrial sectors (ONS, 2017). However, many RAS technologies are in their infancy - developing them within the agri-food sector will deliver impact but also provide a challenging environment that will significantly push the state of art in the underpinning RAS science. Although the opportunity for RAS is widely acknowledged, a shortage of trained engineers and specialists has limited the delivery of impact. This directly addresses this need and will produce the largest global cohort of RAS specialists in Agri-Food.

The impacts are multiple and include;

1) Impact on RAS technology. The Agri-Food sector provides an ideal test bed to develop multiple technologies that will have application in many industrial sectors and research domains. These include new approaches to autonomy and navigation in field environments; complex picking, grasping and manipulation; and novel applications of machine learning and AI in critical and essential sectors of the world economy.

2) Economic Impact. In the UK alone the Made Smarter Review (2017) estimates that automation and RAS will create £183bn of GVA over the next decade, £58bn of which from increased technology exports and reshoring of manufacturing. Expected impacts within Agri-Food are demonstrated by the £3.0M of industry support including the world largest agricultural engineering company (John Deere), the multinational Syngenta, one of the world's largest robotics manufacturers (ABB), the UK's largest farming company owned by James Dyson (one of the largest private investors in robotics), the UK's largest salads and fruit producer plus multiple SME RAS companies. These partners recognise the potential and need for RAS (see NFU and IAgrE Letters of Support).

3) Societal impact. Following the EU referendum, there is significant uncertainty that seasonal labour employed in the sector will be available going forwards, while the demographics of an aging population further limits the supply of manual labour. We see robotic automation as a means of performing onerous and difficult jobs in adverse environments, while advancing the UK skills base, enabling human jobs to move up the value chain and attracting skilled workers and graduates to Agri-Food.

4) Diversity impact. Gender under-representation is also a concern across the computer science, engineering and technology sectors, with only 15% of undergraduates being female. Through engagement with the EPSRC ASPIRE (Advanced Strategic Platform for Inclusive Research Environments) programme, AgriFoRwArdS will become an exemplar CDT with an EDI impact framework that is transferable to other CDTs.

5) Environmental Impact. The Agri-food sector uses 13% of UK carbon emissions and 70% of fresh water, while diffuse pollution from fertilisers and pesticides creates environmental damage. RAS technology, such as robotic weeders and field robots with advanced sensors, will enable a paradigm shift in precision agriculture that will sustainably intensify production while minimising environmental impacts.