Machine learning deployment is underway in the global plant breeding industry
This story is a part of the video of FCAI success stories series for explaining why fundamental research in AI is needed and how research results create solutions to the needs of people, society and companies.
Yield Systems aims to have a significant sustainable impact on the global food system by creating cost efficient AI capabilities for plant breeding and speeding up the development of crop yields and adaptation to climate change.
As the human population grows, it’s estimated that by 2050, the demand for food will exceed supply by as much as sixty per cent. The quantities of agronomic inputs; land, fresh water and fertilisers cannot be increased proportionally, so further improvements in efficiency are needed. Climate change will cause significant challenges, which need to be covered to maintain food security.
“As a first step we created a plant variety recommendation engine, that enables more precise plant variety selection in changing environmental conditions. As drought, heavy rains and other extreme weather conditions become more frequent, it is more and more important to be able to select right seed to the right place, to maximise resilience and yields” says Jussi Gillberg, Yield Systems CEO.
At the moment, machine learning deployment is underway in the global breeding industry.
“Early movers have adapted genomic prediction systems and image processing with drone-based systems, and also other parts of the process are being automated. This is no wonder considering the highly data-intensive nature of the breeding process” says Gillberg.
The company originates from a research group led by Samuel Kaski that had developed machine learning methods for personalised medicine at Aalto University. As they went on, they found that personalised medicine and breeding domains were similar in the related prediction problems, and some solutions could be transferable.
The group started to focus on environment-related crop performance in arable farming, and determining which varieties would work best in different types of conditions. The results would be used to develop more accurate selection methods for plant breeders.
From that research, Yield Systems developed an AI-powered observation instrument that, paired with machine learning, can get high accuracy estimates of canopy-level traits. This laboratory-like information from field conditions is then turned into rich data and understanding about characteristics, unobservable with competing technologies. This high-relevance data is the key for delivering for the improved varieties for the changing conditions and for reaching the extremely ambitious efficiency improvement goals.