Autonomous monitoring and control of crop growth as a feedback system

The proposed project is to model and control the growth of crops in an agricultural setting as a feedback system. The project goal is to enable growers to maximise their harvest, by taking advantage of distributed sensing to optimise the use of fertilisers and of automation for crop management. Specifically, the project will be developed through the following work packages.

WP1: Dynamic modelling of lettuce growth as a feedback system.
WP2: Feedback control algorithms for crop optimization.
WP3: Distributed sensing technologies for crop estimation.
WP4: Robotics and general automation for growth control.

All work packages will be developed in collaboration with G's growers. Prototypes and testing will be also developed in the controlled setting of the Agripods available in the Observatory for Human-Machine Collaboration at the University of Cambridge.

WP1 will be achieved within the first year. Choosing a suitable model for control will require an understanding of the requirements of the crops involved. Specifically, WP1 will develop open dynamic models whose inputs will be later used for control purposes. WP1 requires collecting and processing data from the grower's crops to build and test the model.

WP2 goal is to develop feedback control algorithms to optimize crop growth. These algorithms must take into account availability of resources and must be robust to uncertainties. A complete study will be developed by the end of the second year.

WP3 will be developed concurrently to WP1 and WP2. A first prototype for non-destructive estimation of lettuce weight is already operative. The prototype is currently undergoing calibration and general design revision. WP3 will take advantage of data and sensing technologies currently used by G's growers (Year 1). WP3 will also investigate the use of mobile robots for data gathering, with particular attention to available low-cost technologies (Year 2). Finally, WP3 and WP1 will be integrated. The goal is to take advantage of the dynamic model to improve sensing (fault detection, noise reduction). Likewise, distributed sensing will be used to refine and correct model predictions (Year 3)

WP4 will investigate actuation technologies and general robotics to develop a platform for growth control (fertilizer distribution, etc.). WP4 will start from the customization of available technologies at G's growers but will also consider new technologies based on mobile robotics. WP4 depends on the completion of the other work packages. WP4 will be completed in Year 3.

Major challenges for the project include: complex system dynamics in WP1; large uncertainties in WP2; limitations to the sampling rate and of the overall quality of sensor data in WP3; fault detection and difficulties in achieving high level of autonomy in WP4. A major risk for the project is the dependence on growing the crop, which is limited by seasonality and by the intrinsic slow nature of the process.

Risk mitigation include creating feasibility studies in the controlled environment of the Agripods, within the Observer for Human-Machine Collaboration. This indoor controlled setting is not limited by seasonality. It allows to validate growth models and control algorithms, by simulating realistic environmental conditions. It also allows testing of prototypes for sensing and actuation.

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.