The future farm-interviews
Yiorgos GadanakisCan you tell us what you’re working on at the moment?
My research is currently spread across the agricultural sector with a focus on farmers’ ability to manage their production systems efficiently and effectively. My emphasis is on the opportunities that the Internet of Things provides to understand, interpret and practically apply data analysis in decision making at farm level, in relation to production and farm business management.
With respect to farm business management, we will see a new type of farm manager capable of using the Internet of Things effectively in order to allocate resources (natural and financial) and for decision making. The farm manager will also be able to use emerging technologies to improve crop productivity and to minimise the environmental pressures generated by the system. This new type of farm manager will combine the skills and knowledge of agricultural management with a set of entrepreneurial skills to support the development of the rural economy.
Future farms are going to need to harness the new technological solutions in order to tackle some of the major challenges we face including developing climate smart farming approaches, reducing environmental impact, and protecting biodiversity while maintaining a productive food supply to feed the increasing world population. Agri-tech can help by providing approaches to increase precision in the use of inputs, using innovative robotic solutions to perform menial on-farm tasks and generating useful digital information to improve productivity.
The research group that I lead, ‘Change in Agriculture: Innovation, Extension, Inclusion’ is working on a range of projects centred on farm innovation. Topics include adoption of technology, behaviour change, knowledge exchange and peer-to-peer learning, as well as the social and ethical consequences of the fourth agricultural revolution (eg animal welfare). I am working on a number of related projects, including ‘Robot Highways’ funded by InnovateUK where I’m exploring trust in autonomous robotics on-farm, as well as other projects related to behaviour change (AHDB-funded with Welsh Dairy farmers) and video knowledge exchange (Defra-funded, Agricology).
Hopefully it will look like arable farmers want it to look. Digitalisation appears to be developing quickly with technologies such as robots, Artificial Intelligence, drones, gene editing, vertical farming, and the Internet of Things attracting significant investment. I would guess that the future arable farm will incorporate more of these emergent technologies with machines undertaking manual tasks and AI interpreting complex big data, though only if farming communities want this to happen. Despite the fact that COVID has accelerated the drive towards automation, I don’t think adoption of these technologies will be quick, mirroring the slow adoption of many (not all) precision technologies over the last decades.
The eyeSpot project – robotic weeding. Weeds are a major challenge for growers of field vegetables. Our aim is leaf-specific weed control, applying small individual droplets of a herbicide to each weed leaf. Adopting this approach is predicted to offer an 80% probability of higher profits for leek growers and similar profitability for cabbage growers while reducing herbicide applications. (Co-funded by AHDB and Douglas Bomford Trust)
LINKDAPA: Linking data for adoption of precision agriculture. Farmers increasingly generate ‘big’ data about their fields. This EIT Food co-funded innovation project is using these data to co-create with farmers and their advisers, crop management zones for precision agriculture (PA) solutions in winter wheat crops in Italy, Germany and the UK. These will be incorporated into a new software platform to be commercialised and hosted by Agricolus SRL based in Italy.
Integrating Precision Farming in Computer Game. Working with the Swiss gaming computer software company, Giants, this EIT Food funded education project is developing a new precision farming module for Giants’ farming simulator game (with 2 million subscribers). It is a novel and innovative way to communicate the multiple benefits of precision farming to a wider audience of potentially interested farmers, agricultural students and other interested stakeholders.
A blended mix of technology and traditional methods. Great efforts to be carbon neutral, enhance carbon sequestration, minimise soil disturbance, target inputs, and harvest selectively. Automation will be part of it, but many farmers will not want to spend all day in their offices. So the idea of ‘hands-free’ is useful educationally but unlikely to be adopted. Automation is ideally thought of in terms of the four ‘Ds’. If a task is difficult, dull, dirty, or dangerous, then it’s likely to be worth automating.
I conduct research in automation, robotics and precision agriculture to support sustainable and efficient crop production systems. My research is focused on optimisation of three layers: (1) machines, (2) processes and (3) an overlaying systems layer (entire management). All these layers are influenced by ecologic, economic and societal factors. In my research, farmers have the central and primary role, and I strive to build the necessary infrastructure for them by utilising tools from new digital technologies, in order to support them in common agricultural tasks.
The farm of the future will be highly connected. Various sensors installed in the field and agricultural machinery performing operations will constantly record, process and transmit information to the cloud-based infrastructure for storage and in-depth analysis. Actuators and autonomous vehicles will be responsible for implementing the farmer’s strategy. The entire architecture will be based on a multi-level automation ecosystem, starting from simple closed-loop systems, ie irrigation, up to more complex systems with a higher level of cognition such as machine coordination.
A cutting-edge future technology that is expected to have a profound impact on agriculture in the next years is the concept of Digital Twins. They are already becoming available in other scientific disciplines, such as automotive and industrial informatics. The Digital Twins are virtual, digital equivalents to physical objects that provide a detailed representation of the object and the context that this object is working in. The aim is to combine IoT (Internet of Things) sensor data with historical data and human expertise, and, using Machine Learning techniques, to improve the outcome of prognostics. This technology could provide decision support to farmers and other stakeholders by enabling them to act immediately and efficiently in the event of a predicted deviation.
We have just started a new, collaborative research project called ENVISION which aims to co-develop a continuous and systematic monitoring service of sustainable agricultural practices. This will use Earth Observation (satellite) data and a range of methodologies such as machine learning to remotely monitor environmental indicators of sustainable farming such as crop type, vegetation status and soil erosion. By co-developing this service with regulators and farmers we aim to provide a robust and trusted system to support/replace on-farm inspections. This follows on from the development of the RECAP platform which provides remote monitoring of CAP obligations. I’m also working on a range of projects that look at the way sustainable farming practices can be developed and how this information can be communicated to the consumer.
I think there will be a gradual increase in the use of digital technologies for management and decision making on arable farms. Collaborative efforts between scientific researchers and farmers can help bring ecological knowledge into farming practice through, for example, Integrated Pest Management systems. In this way, decision support tools can be refined and improved in order to achieve more sustainable farming systems and provide a digital data trail from farm to fork.
Other agri-tech innovationsIn addition to the innovations mentioned by the experts above:
- Hands-free hectare: automated machines growing the first arable crop remotely, without operators in the driving seats or agronomists on the ground.
- Task based agricultural robots: there are a range of robots in development to assist with a variety of agricultural tasks such as crop scouting and control, harvesting, tilling, soil analysis and seeding and transplanting .
Do you know of any other interesting innovations that you think will transform arable farming in the future? Reference 1. Aravind, K.R., Raja, P. and Pérez Ruiz, M., 2017. Task-based agricultural mobile robots in arable farming: A review. Spanish Journal of Agricultural Research, 2017 (15 (1)), 1-16.
Innovation in Arable Farming: Technologies for Sustainable Farming Systems
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