Multi-modal fusion for remote biodiversity surveys of linear green infrastructure

Scientific background
The agricultural sector has a huge role to play in conserving biodiversity and mitigating its impact on the climate crisis. Biodiversity is an important indicator of the overall health of a habitat, and the environment in general. However, agricultural activities, typically focused on growing a single crop type over a large surface area and reliant on a number of substances (ranging from fertilisers to pesticides and herbicides) that are known to cause biodiversity losses, not just in arable fields, but also in the areas surrounding them. This project proposes to develop the means of automatically monitoring the biodiversity of habitats surrounding cultivated land, such as hedgerows, using computer vision techniques that incorporate information from several input sources including satellite, UAVs, and mobile robots.

Research methodology
The project will initially focus on the continuation of previous work applied to road verges, where a computer vision approach (based on deep convolutional neural networks) will be extended as follows:
(1) re-training of the model using data supplied by industry collaborator Gaist,
(2) transfer of knowledge learned on road verges to other domains, such as hedgerows and additional geographic locations,
(3) incorporation of hierarchical and ordinal relationships when classifying images.
The second phase of the project will focus on incorporating additional imaging modalities, such as satellite imagery, UAV photographs, and images collected by mobile robots. In the third and final phase, a hierarchical classification system that effectively utilises information from each of the different modalities.

Training
The PhD candidate will have the opportunity to study and develop computer vision techniques and contribute to the state-of-the-art in deep learning-based methods (i.e., convolutional neural networks, vision transformers). As such they will develop skills in image processing, machine learning, and data visualisation. The candidate will also develop skills in working with several hardware platforms, and operation of a UAV. They will develop their oral and written communications skills through production of journal manuscripts, conference and seminar talks, poster presentations, and engagement with collaborators from other disciplines.

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.