Real-time vision-based spot spraying development for high efficiency and precision weed management

Weeds are typically controlled by spraying chemicals uniformly across the whole field. However, the overuse of chemicals in this approach has increased the cost of crop protection and posed negative impacts on the environment and food security, which is a hindrance to sustainable agriculture development. Spot spraying, as a spatially variable weed management strategy, targets only weed species in fields to minimize the use of chemicals. Commercially available technologies based on sensing of vegetation optical properties are typically constrained by detecting weeds on a soil background (i.e. greenness detection in a bare soil background) and are not suitable to detect weeds among a growing crop. A vision-based spot spraying system enables discrimination between vegetation species. One of the key components for the vision-based spot spraying development is to build a reliable and robust weed/crop discrimination model. Traditionally, the development of a vision-based weed/crop discrimination model is highly relying on image analysis with prior knowledge of the defined colour, texture and morphology features between weed and crop. But this might fail to generalise over different crop fields with multiple weed species. The recent technology advancements in machine learning and computer vision have provided new opportunities to develop a robust and reliable vision-based weed/crop discrimination model under unstructured field conditions.

Currently, training a deep machine learning model require large numbers of labelled images. In this project, in order to remedy the manual labelling task and speed up the development process, a novel pipeline is proposed to generate realistic synthetic images based on the combination of conventional image analysis and generative adversarial networks (GANs). Furthermore, the project will also focus on dealing with the current bottlenecks of vision-based spot spraying development with regards to discrimination model generalization ability and spraying efficiency. Specifically, the objectives of this project are to (1) build two image data libraries (weed and crop) that allow fast annotated synthetic images generation via the developed generation pipeline; to (2) develop a robust and lightweight deep learning-based detection model based on large numbers of synthetic images and to (3) integrate the developed weed detection system into a spray boom for spot spraying and demonstrate its feasibility for high efficiency spot spraying (driving speed > 8 km/h) under field conditions. The demonstration will be displayed at a sugar beet field. The evaluation will be made in terms of spraying accuracy and efficiency.

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.