How machine learning is helping farmers manage the unpredictable
Could machine learning reduce the impact of a disastrous harvest? Speakers at a forthcoming Agri-Tech East meeting provide their perspectives.
A deluge of rain during the 2017 harvest slashed profits overnight – wheat for milling and barley for malting were downgraded and producers incurred additional costs for drying. Few industries have so much at stake than agriculture, and so much to gain from accurate, timely information.
Advances in machine learning to simplify complexity and improve decision making is to be discussed at Agri-Tech East’s Pollinator event on 20th February at the Sainsbury Laboratory in Cambridge. The event will include industry speakers from After the flood, Fujitsu, Iteris, Kings College London, Microsoft and PA Consulting describing the progress in applied artificial intelligence and looking to where the future is taking us.
Speaker John Lord from Iteris explains that combining crop and environmental data with artificial intelligence (AI) can help farmers make key decisions.
He says: “The UK harvest in 2017 was very disrupted by weather and farmers had a difficult decision over when to make the cut. Is it better to harvest wet and take the expense of drying or to wait for better weather and risk the chance of the grain sprouting in the ear?
“Mechanical drying is a major investment and grain moisture and environmental conditions can change rapidly so timing is everything.
“By bringing together crop health modelling with field level atmospheric data, our ClearAg app provides harvesting insights that allow users to make more informed decisions on when and where to harvest and dry crops.”
The company is also using AI for smart water control. Lord explains that specific land surface models are used to forecast the soil moisture at crop rooting zones. After submitting further information and user feedback, AI is used to validate and augment the complex model process, thereby building confidence in the models and keeping them current.
Humans have evolved to quickly extract information from patterns. After the flood is using AI to take data visualisation to a new dimension. It takes insights from multiple interactions (people-machine and machine-machine) and displays the findings as deceptively simple dashboards.
After the flood’s Chairman Nick Cross, who also manages his family farming business, explains: “Traditional data analytics are based on collecting data and then providing retrospective insights. We are moving into a new era of active data that uses real-time data to provide intelligent services.
After the flood creates a dynamic interpretation of live data. This allows fast reactions and the ability to create systems that learn from experience to respond to changes in their environment.
Within agri-food this could be using customer buying behaviours to predict demand for perishable goods, or monitoring fungal spores and weather conditions to allow preventative, precision spraying.
Cross continues: “I think there will be exciting opportunities to create intelligent data flows between customers, stores and the producers themselves, allowing farmers to be more responsive to specific consumers’ tastes and dietary needs. Perhaps there will come a time when food production will be personalised!”
Matthew Smith, Director of Business Development at Microsoft Research, agrees: “I've always been excited by creating information services for the food supply chain – finding ways to get the right information, to the right people, at the right time, in the right way. That information supply chain still doesn't exist as it should; it is fragmented and inefficient.
“Wonderfully, technology is approaching a maturity to create the information supply chains the world needs, harnessing things like cloud computing, IoT, AI and block chain.”
Dr Belinda Clarke, Director of Agri-Tech East, comments: “The opportunity within agri-food for learning systems that can track multiple sources of input from the environment and elsewhere and present this in a way that is easy for humans – or machines - to understand and take action is immense. AI is being applied in many areas and this event will explore what is possible now and what is on the horizon.”
The ‘AI’m of Machine Learning in Agriculture’ is being held on 20 February 2018 at Sainsbury Laboratory, 47 Bateman Street, Cambridge CB2 1LR. For full details of the speakers and registration visit:
Agri-Tech East is an independent business-focused cluster organisation for the East of England. It is creating a global innovation hub, to improve the international competitiveness of plant and crop-based agriculture and catalyse economic growth.